Upload folder using huggingface_hub
Browse files- added_tokens.json +258 -0
- chat_template.jinja +137 -0
- config.json +97 -0
- configuration_hyperclovax.py +228 -0
- generation_config.json +8 -0
- merges.txt +0 -0
- model-00001-of-00013.safetensors +3 -0
- model-00002-of-00013.safetensors +3 -0
- model-00003-of-00013.safetensors +3 -0
- model-00004-of-00013.safetensors +3 -0
- model-00005-of-00013.safetensors +3 -0
- model-00006-of-00013.safetensors +3 -0
- model-00007-of-00013.safetensors +3 -0
- model-00008-of-00013.safetensors +3 -0
- model-00009-of-00013.safetensors +3 -0
- model-00010-of-00013.safetensors +3 -0
- model-00011-of-00013.safetensors +3 -0
- model-00012-of-00013.safetensors +3 -0
- model-00013-of-00013.safetensors +3 -0
- model.safetensors.index.json +658 -0
- modeling_hyperclovax.py +1866 -0
- special_tokens_map.json +39 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2079 -0
- vocab.json +0 -0
added_tokens.json
ADDED
|
@@ -0,0 +1,258 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"</arg_key>": 128045,
|
| 3 |
+
"</arg_value>": 128047,
|
| 4 |
+
"</think>": 128041,
|
| 5 |
+
"</tool_call>": 128043,
|
| 6 |
+
"</tool_response>": 128049,
|
| 7 |
+
"</tools>": 128051,
|
| 8 |
+
"<EMAIL>": 128037,
|
| 9 |
+
"<KEY>": 128038,
|
| 10 |
+
"<NAME>": 128036,
|
| 11 |
+
"<PASSWORD>": 128039,
|
| 12 |
+
"<arg_key>": 128044,
|
| 13 |
+
"<arg_value>": 128046,
|
| 14 |
+
"<code_to_intermediate>": 128018,
|
| 15 |
+
"<empty_output>": 128017,
|
| 16 |
+
"<file_sep>": 128008,
|
| 17 |
+
"<intermediate_to_code>": 128019,
|
| 18 |
+
"<issue_closed>": 128011,
|
| 19 |
+
"<issue_comment>": 128010,
|
| 20 |
+
"<issue_start>": 128009,
|
| 21 |
+
"<jupyter_code>": 128014,
|
| 22 |
+
"<jupyter_output>": 128015,
|
| 23 |
+
"<jupyter_script>": 128016,
|
| 24 |
+
"<jupyter_start>": 128012,
|
| 25 |
+
"<jupyter_text>": 128013,
|
| 26 |
+
"<pr>": 128020,
|
| 27 |
+
"<pr_base>": 128023,
|
| 28 |
+
"<pr_base_code>": 128025,
|
| 29 |
+
"<pr_comment>": 128028,
|
| 30 |
+
"<pr_diff>": 128026,
|
| 31 |
+
"<pr_diff_hunk>": 128027,
|
| 32 |
+
"<pr_diff_hunk_comment_line>": 128035,
|
| 33 |
+
"<pr_event_id>": 128029,
|
| 34 |
+
"<pr_file>": 128024,
|
| 35 |
+
"<pr_in_reply_to_comment_id>": 128034,
|
| 36 |
+
"<pr_in_reply_to_review_id>": 128033,
|
| 37 |
+
"<pr_is_merged>": 128022,
|
| 38 |
+
"<pr_review>": 128030,
|
| 39 |
+
"<pr_review_comment>": 128032,
|
| 40 |
+
"<pr_review_state>": 128031,
|
| 41 |
+
"<pr_status>": 128021,
|
| 42 |
+
"<repo_name>": 128007,
|
| 43 |
+
"<think>": 128040,
|
| 44 |
+
"<tool_call>": 128042,
|
| 45 |
+
"<tool_response>": 128048,
|
| 46 |
+
"<tools>": 128050,
|
| 47 |
+
"<|IMAGE_PAD|>": 128060,
|
| 48 |
+
"<|VIDEO_PAD|>": 128061,
|
| 49 |
+
"<|_placeholder_067|>": 128067,
|
| 50 |
+
"<|_placeholder_068|>": 128068,
|
| 51 |
+
"<|_placeholder_069|>": 128069,
|
| 52 |
+
"<|_placeholder_070|>": 128070,
|
| 53 |
+
"<|_placeholder_071|>": 128071,
|
| 54 |
+
"<|_placeholder_072|>": 128072,
|
| 55 |
+
"<|_placeholder_073|>": 128073,
|
| 56 |
+
"<|_placeholder_074|>": 128074,
|
| 57 |
+
"<|_placeholder_075|>": 128075,
|
| 58 |
+
"<|_placeholder_076|>": 128076,
|
| 59 |
+
"<|_placeholder_077|>": 128077,
|
| 60 |
+
"<|_placeholder_078|>": 128078,
|
| 61 |
+
"<|_placeholder_079|>": 128079,
|
| 62 |
+
"<|_placeholder_080|>": 128080,
|
| 63 |
+
"<|_placeholder_081|>": 128081,
|
| 64 |
+
"<|_placeholder_082|>": 128082,
|
| 65 |
+
"<|_placeholder_083|>": 128083,
|
| 66 |
+
"<|_placeholder_084|>": 128084,
|
| 67 |
+
"<|_placeholder_085|>": 128085,
|
| 68 |
+
"<|_placeholder_086|>": 128086,
|
| 69 |
+
"<|_placeholder_087|>": 128087,
|
| 70 |
+
"<|_placeholder_088|>": 128088,
|
| 71 |
+
"<|_placeholder_089|>": 128089,
|
| 72 |
+
"<|_placeholder_090|>": 128090,
|
| 73 |
+
"<|_placeholder_091|>": 128091,
|
| 74 |
+
"<|_placeholder_092|>": 128092,
|
| 75 |
+
"<|_placeholder_093|>": 128093,
|
| 76 |
+
"<|_placeholder_094|>": 128094,
|
| 77 |
+
"<|_placeholder_095|>": 128095,
|
| 78 |
+
"<|_placeholder_096|>": 128096,
|
| 79 |
+
"<|_placeholder_097|>": 128097,
|
| 80 |
+
"<|_placeholder_098|>": 128098,
|
| 81 |
+
"<|_placeholder_099|>": 128099,
|
| 82 |
+
"<|_placeholder_100|>": 128100,
|
| 83 |
+
"<|_placeholder_101|>": 128101,
|
| 84 |
+
"<|_placeholder_102|>": 128102,
|
| 85 |
+
"<|_placeholder_103|>": 128103,
|
| 86 |
+
"<|_placeholder_104|>": 128104,
|
| 87 |
+
"<|_placeholder_105|>": 128105,
|
| 88 |
+
"<|_placeholder_106|>": 128106,
|
| 89 |
+
"<|_placeholder_107|>": 128107,
|
| 90 |
+
"<|_placeholder_108|>": 128108,
|
| 91 |
+
"<|_placeholder_109|>": 128109,
|
| 92 |
+
"<|_placeholder_110|>": 128110,
|
| 93 |
+
"<|_placeholder_111|>": 128111,
|
| 94 |
+
"<|_placeholder_112|>": 128112,
|
| 95 |
+
"<|_placeholder_113|>": 128113,
|
| 96 |
+
"<|_placeholder_114|>": 128114,
|
| 97 |
+
"<|_placeholder_115|>": 128115,
|
| 98 |
+
"<|_placeholder_116|>": 128116,
|
| 99 |
+
"<|_placeholder_117|>": 128117,
|
| 100 |
+
"<|_placeholder_118|>": 128118,
|
| 101 |
+
"<|_placeholder_119|>": 128119,
|
| 102 |
+
"<|_placeholder_120|>": 128120,
|
| 103 |
+
"<|_placeholder_121|>": 128121,
|
| 104 |
+
"<|_placeholder_122|>": 128122,
|
| 105 |
+
"<|_placeholder_123|>": 128123,
|
| 106 |
+
"<|_placeholder_124|>": 128124,
|
| 107 |
+
"<|_placeholder_125|>": 128125,
|
| 108 |
+
"<|_placeholder_126|>": 128126,
|
| 109 |
+
"<|_placeholder_127|>": 128127,
|
| 110 |
+
"<|_placeholder_128|>": 128128,
|
| 111 |
+
"<|_placeholder_129|>": 128129,
|
| 112 |
+
"<|_placeholder_130|>": 128130,
|
| 113 |
+
"<|_placeholder_131|>": 128131,
|
| 114 |
+
"<|_placeholder_132|>": 128132,
|
| 115 |
+
"<|_placeholder_133|>": 128133,
|
| 116 |
+
"<|_placeholder_134|>": 128134,
|
| 117 |
+
"<|_placeholder_135|>": 128135,
|
| 118 |
+
"<|_placeholder_136|>": 128136,
|
| 119 |
+
"<|_placeholder_137|>": 128137,
|
| 120 |
+
"<|_placeholder_138|>": 128138,
|
| 121 |
+
"<|_placeholder_139|>": 128139,
|
| 122 |
+
"<|_placeholder_140|>": 128140,
|
| 123 |
+
"<|_placeholder_141|>": 128141,
|
| 124 |
+
"<|_placeholder_142|>": 128142,
|
| 125 |
+
"<|_placeholder_143|>": 128143,
|
| 126 |
+
"<|_placeholder_144|>": 128144,
|
| 127 |
+
"<|_placeholder_145|>": 128145,
|
| 128 |
+
"<|_placeholder_146|>": 128146,
|
| 129 |
+
"<|_placeholder_147|>": 128147,
|
| 130 |
+
"<|_placeholder_148|>": 128148,
|
| 131 |
+
"<|_placeholder_149|>": 128149,
|
| 132 |
+
"<|_placeholder_150|>": 128150,
|
| 133 |
+
"<|_placeholder_151|>": 128151,
|
| 134 |
+
"<|_placeholder_152|>": 128152,
|
| 135 |
+
"<|_placeholder_153|>": 128153,
|
| 136 |
+
"<|_placeholder_154|>": 128154,
|
| 137 |
+
"<|_placeholder_155|>": 128155,
|
| 138 |
+
"<|_placeholder_156|>": 128156,
|
| 139 |
+
"<|_placeholder_157|>": 128157,
|
| 140 |
+
"<|_placeholder_158|>": 128158,
|
| 141 |
+
"<|_placeholder_159|>": 128159,
|
| 142 |
+
"<|_placeholder_160|>": 128160,
|
| 143 |
+
"<|_placeholder_161|>": 128161,
|
| 144 |
+
"<|_placeholder_162|>": 128162,
|
| 145 |
+
"<|_placeholder_163|>": 128163,
|
| 146 |
+
"<|_placeholder_164|>": 128164,
|
| 147 |
+
"<|_placeholder_165|>": 128165,
|
| 148 |
+
"<|_placeholder_166|>": 128166,
|
| 149 |
+
"<|_placeholder_167|>": 128167,
|
| 150 |
+
"<|_placeholder_168|>": 128168,
|
| 151 |
+
"<|_placeholder_169|>": 128169,
|
| 152 |
+
"<|_placeholder_170|>": 128170,
|
| 153 |
+
"<|_placeholder_171|>": 128171,
|
| 154 |
+
"<|_placeholder_172|>": 128172,
|
| 155 |
+
"<|_placeholder_173|>": 128173,
|
| 156 |
+
"<|_placeholder_174|>": 128174,
|
| 157 |
+
"<|_placeholder_175|>": 128175,
|
| 158 |
+
"<|_placeholder_176|>": 128176,
|
| 159 |
+
"<|_placeholder_177|>": 128177,
|
| 160 |
+
"<|_placeholder_178|>": 128178,
|
| 161 |
+
"<|_placeholder_179|>": 128179,
|
| 162 |
+
"<|_placeholder_180|>": 128180,
|
| 163 |
+
"<|_placeholder_181|>": 128181,
|
| 164 |
+
"<|_placeholder_182|>": 128182,
|
| 165 |
+
"<|_placeholder_183|>": 128183,
|
| 166 |
+
"<|_placeholder_184|>": 128184,
|
| 167 |
+
"<|_placeholder_185|>": 128185,
|
| 168 |
+
"<|_placeholder_186|>": 128186,
|
| 169 |
+
"<|_placeholder_187|>": 128187,
|
| 170 |
+
"<|_placeholder_188|>": 128188,
|
| 171 |
+
"<|_placeholder_189|>": 128189,
|
| 172 |
+
"<|_placeholder_190|>": 128190,
|
| 173 |
+
"<|_placeholder_191|>": 128191,
|
| 174 |
+
"<|_placeholder_192|>": 128192,
|
| 175 |
+
"<|_placeholder_193|>": 128193,
|
| 176 |
+
"<|_placeholder_194|>": 128194,
|
| 177 |
+
"<|_placeholder_195|>": 128195,
|
| 178 |
+
"<|_placeholder_196|>": 128196,
|
| 179 |
+
"<|_placeholder_197|>": 128197,
|
| 180 |
+
"<|_placeholder_198|>": 128198,
|
| 181 |
+
"<|_placeholder_199|>": 128199,
|
| 182 |
+
"<|_placeholder_200|>": 128200,
|
| 183 |
+
"<|_placeholder_201|>": 128201,
|
| 184 |
+
"<|_placeholder_202|>": 128202,
|
| 185 |
+
"<|_placeholder_203|>": 128203,
|
| 186 |
+
"<|_placeholder_204|>": 128204,
|
| 187 |
+
"<|_placeholder_205|>": 128205,
|
| 188 |
+
"<|_placeholder_206|>": 128206,
|
| 189 |
+
"<|_placeholder_207|>": 128207,
|
| 190 |
+
"<|_placeholder_208|>": 128208,
|
| 191 |
+
"<|_placeholder_209|>": 128209,
|
| 192 |
+
"<|_placeholder_210|>": 128210,
|
| 193 |
+
"<|_placeholder_211|>": 128211,
|
| 194 |
+
"<|_placeholder_212|>": 128212,
|
| 195 |
+
"<|_placeholder_213|>": 128213,
|
| 196 |
+
"<|_placeholder_214|>": 128214,
|
| 197 |
+
"<|_placeholder_215|>": 128215,
|
| 198 |
+
"<|_placeholder_216|>": 128216,
|
| 199 |
+
"<|_placeholder_217|>": 128217,
|
| 200 |
+
"<|_placeholder_218|>": 128218,
|
| 201 |
+
"<|_placeholder_219|>": 128219,
|
| 202 |
+
"<|_placeholder_220|>": 128220,
|
| 203 |
+
"<|_placeholder_221|>": 128221,
|
| 204 |
+
"<|_placeholder_222|>": 128222,
|
| 205 |
+
"<|_placeholder_223|>": 128223,
|
| 206 |
+
"<|_placeholder_224|>": 128224,
|
| 207 |
+
"<|_placeholder_225|>": 128225,
|
| 208 |
+
"<|_placeholder_226|>": 128226,
|
| 209 |
+
"<|_placeholder_227|>": 128227,
|
| 210 |
+
"<|_placeholder_228|>": 128228,
|
| 211 |
+
"<|_placeholder_229|>": 128229,
|
| 212 |
+
"<|_placeholder_230|>": 128230,
|
| 213 |
+
"<|_placeholder_231|>": 128231,
|
| 214 |
+
"<|_placeholder_232|>": 128232,
|
| 215 |
+
"<|_placeholder_233|>": 128233,
|
| 216 |
+
"<|_placeholder_234|>": 128234,
|
| 217 |
+
"<|_placeholder_235|>": 128235,
|
| 218 |
+
"<|_placeholder_236|>": 128236,
|
| 219 |
+
"<|_placeholder_237|>": 128237,
|
| 220 |
+
"<|_placeholder_238|>": 128238,
|
| 221 |
+
"<|_placeholder_239|>": 128239,
|
| 222 |
+
"<|_placeholder_240|>": 128240,
|
| 223 |
+
"<|_placeholder_241|>": 128241,
|
| 224 |
+
"<|_placeholder_242|>": 128242,
|
| 225 |
+
"<|_placeholder_243|>": 128243,
|
| 226 |
+
"<|_placeholder_244|>": 128244,
|
| 227 |
+
"<|_placeholder_245|>": 128245,
|
| 228 |
+
"<|_placeholder_246|>": 128246,
|
| 229 |
+
"<|_placeholder_247|>": 128247,
|
| 230 |
+
"<|_placeholder_248|>": 128248,
|
| 231 |
+
"<|_placeholder_249|>": 128249,
|
| 232 |
+
"<|_placeholder_250|>": 128250,
|
| 233 |
+
"<|_placeholder_251|>": 128251,
|
| 234 |
+
"<|_placeholder_252|>": 128252,
|
| 235 |
+
"<|_placeholder_253|>": 128253,
|
| 236 |
+
"<|_placeholder_254|>": 128254,
|
| 237 |
+
"<|_placeholder_255|>": 128255,
|
| 238 |
+
"<|back_translation|>": 128065,
|
| 239 |
+
"<|code_switching|>": 128064,
|
| 240 |
+
"<|document_end|>": 128055,
|
| 241 |
+
"<|document_start|>": 128054,
|
| 242 |
+
"<|endofturn|>": 128003,
|
| 243 |
+
"<|fim_middle|>": 128005,
|
| 244 |
+
"<|fim_prefix|>": 128004,
|
| 245 |
+
"<|fim_suffix|>": 128006,
|
| 246 |
+
"<|im_end|>": 128001,
|
| 247 |
+
"<|im_start|>": 128000,
|
| 248 |
+
"<|image_end|>": 128057,
|
| 249 |
+
"<|image_start|>": 128056,
|
| 250 |
+
"<|instruction_pretraining|>": 128066,
|
| 251 |
+
"<|mime_end|>": 128053,
|
| 252 |
+
"<|mime_start|>": 128052,
|
| 253 |
+
"<|stop|>": 128002,
|
| 254 |
+
"<|video_end|>": 128059,
|
| 255 |
+
"<|video_start|>": 128058,
|
| 256 |
+
"<|vision_aux_end|>": 128063,
|
| 257 |
+
"<|vision_aux_start|>": 128062
|
| 258 |
+
}
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,137 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{%- set ns_img = namespace(count=0) %}
|
| 2 |
+
{%- set ns_vid = namespace(count=0) %}
|
| 3 |
+
{%- if tools %}
|
| 4 |
+
{{- '<|im_start|>system\n' }}
|
| 5 |
+
{%- if messages[0].role == 'system' and messages[0].content is string %}
|
| 6 |
+
{{- messages[0].content + '\n\n' }}
|
| 7 |
+
{%- endif %}
|
| 8 |
+
{{- '# Tools\n\n' }}
|
| 9 |
+
{{- 'You may call one or more functions to assist with the user query.\n\n' }}
|
| 10 |
+
{{- 'You are provided with function signatures within <tools></tools> XML tags:\n' }}
|
| 11 |
+
{{- '<tools>\n' }}
|
| 12 |
+
{%- for tool in tools %}
|
| 13 |
+
{{- tool | tojson(ensure_ascii=False) }}
|
| 14 |
+
{%- endfor %}
|
| 15 |
+
{{- '\n</tools>\n\n' }}
|
| 16 |
+
{{- 'For each function call, output the function name and arguments within the following XML format:\n' }}
|
| 17 |
+
{{- '<tool_call>{function-name}\n' }}
|
| 18 |
+
{{- '<arg_key>{arg-key-1}</arg_key>\n' }}
|
| 19 |
+
{{- '<arg_value>{arg-value-1}</arg_value>\n' }}
|
| 20 |
+
{{- '<arg_key>{arg-key-2}</arg_key>\n' }}
|
| 21 |
+
{{- '<arg_value>{arg-value-2}</arg_value>\n' }}
|
| 22 |
+
{{- '...\n' }}
|
| 23 |
+
{{- '</tool_call><|im_end|>\n' }}
|
| 24 |
+
{%- else %}
|
| 25 |
+
{%- if messages[0].role == 'system' %}
|
| 26 |
+
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
| 27 |
+
{%- endif %}
|
| 28 |
+
{%- endif %}
|
| 29 |
+
{%- set ns = namespace(last_user_index=-1) %}
|
| 30 |
+
{%- for m in messages %}
|
| 31 |
+
{%- if m.role == 'user' %}
|
| 32 |
+
{%- set ns.last_user_index = loop.index0 %}
|
| 33 |
+
{%- endif %}
|
| 34 |
+
{%- endfor %}
|
| 35 |
+
{%- for message in messages %}
|
| 36 |
+
{%- set content = message.get('content', '') or '' %}
|
| 37 |
+
{%- if (message.role == 'system' and not loop.first) %}
|
| 38 |
+
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>\n' }}
|
| 39 |
+
{%- elif message.role == 'user' %}
|
| 40 |
+
{{- '<|im_start|>user\n' }}
|
| 41 |
+
{%- if message['content'] is string %}
|
| 42 |
+
{{- message['content'] + '<|im_end|>\n' }}
|
| 43 |
+
{%- elif message['content'] is sequence %}
|
| 44 |
+
{%- for content in message['content'] %}
|
| 45 |
+
{%- if not loop.first %}
|
| 46 |
+
{{- '\n' }}
|
| 47 |
+
{%- endif %}
|
| 48 |
+
{%- if content['type'] == 'image_url' %}
|
| 49 |
+
{%- set media_url = content.get('image_url', {}).get('url', '') %}
|
| 50 |
+
{%- set url_lower = media_url.lower() %}
|
| 51 |
+
{%- set video_extensions = ['.mp4', '.avi', '.mov', '.mkv', '.webm', '.flv', '.wmv', '.m4v'] %}
|
| 52 |
+
{%- set ns_check = namespace(is_video=False) %}
|
| 53 |
+
{%- for ext in video_extensions %}
|
| 54 |
+
{%- if url_lower.endswith(ext) %}
|
| 55 |
+
{%- set ns_check.is_video = True %}
|
| 56 |
+
{%- endif %}
|
| 57 |
+
{%- endfor %}
|
| 58 |
+
{%- if ns_check.is_video %}
|
| 59 |
+
{%- set video_id = 'video_%02d' % ns_vid.count %}
|
| 60 |
+
{%- set ns_vid.count = ns_vid.count + 1 %}
|
| 61 |
+
{%- set filename = media_url.split('/')[-1] %}
|
| 62 |
+
{{- '<|mime_start|>{"id": "' + video_id + '", "type": "video/mp4", "filename": "' + filename + '"}<|mime_end|>\n' }}
|
| 63 |
+
{{- '<|video_aux_start|>다음 중 video_duration은 비디오 길이 정보입니다. 참고하여 답변하세요. {"video_duration": "<|video_meta_duration|>"}<|video_aux_end|>\n'}}
|
| 64 |
+
{{- '<|video_start|><|VIDEO_PAD|><|video_end|>\n'}}
|
| 65 |
+
{%- else %}
|
| 66 |
+
{%- set image_id = 'image_%02d' % ns_img.count %}
|
| 67 |
+
{%- set ns_img.count = ns_img.count + 1 %}
|
| 68 |
+
{%- set filename = media_url.split('/')[-1] %}
|
| 69 |
+
{{- '<|mime_start|>{"id": "' + image_id + '", "type": "image/jpeg", "filename": "' + filename + '"}<|mime_end|>\n' }}
|
| 70 |
+
{{- '<|image_start|><|IMAGE_PAD|><|image_end|>' }}
|
| 71 |
+
{%- endif %}
|
| 72 |
+
{%- elif content['type'] == 'text' %}
|
| 73 |
+
{{- content['text'] }}
|
| 74 |
+
{%- endif %}
|
| 75 |
+
{%- endfor %}
|
| 76 |
+
{{- '<|im_end|>\n'}}
|
| 77 |
+
{%- endif %}
|
| 78 |
+
{%- elif message.role == 'assistant' %}
|
| 79 |
+
{%- set reasoning_content = '' %}
|
| 80 |
+
{%- if message.get('reasoning_content') is string %}
|
| 81 |
+
{%- set reasoning_content = message.get('reasoning_content') %}
|
| 82 |
+
{%- else %}
|
| 83 |
+
{%- if '</think>' in content %}
|
| 84 |
+
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
| 85 |
+
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
| 86 |
+
{%- endif %}
|
| 87 |
+
{%- endif %}
|
| 88 |
+
{%- if loop.index0 > ns.last_user_index %}
|
| 89 |
+
{%- if loop.last or reasoning_content %}
|
| 90 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
| 91 |
+
{%- else %}
|
| 92 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 93 |
+
{%- endif %}
|
| 94 |
+
{%- else %}
|
| 95 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 96 |
+
{%- endif %}
|
| 97 |
+
{%- if message.get('tool_calls') %}
|
| 98 |
+
{%- for tool_call in message.get('tool_calls', []) %}
|
| 99 |
+
{%- if not loop.first or content %}
|
| 100 |
+
{{- '\n' }}
|
| 101 |
+
{%- endif %}
|
| 102 |
+
{%- if tool_call.get('function') %}
|
| 103 |
+
{%- set tool_call = tool_call.get('function') %}
|
| 104 |
+
{%- endif %}
|
| 105 |
+
{{- '<tool_call>' + tool_call.get('name', '') + '\n' }}
|
| 106 |
+
{%- set _args = tool_call.get('arguments', {}) %}
|
| 107 |
+
{%- if _args is string %}
|
| 108 |
+
{{- '<arguments>' + _args + '</arguments>\n' }}
|
| 109 |
+
{%- elif _args is mapping %}
|
| 110 |
+
{%- for k, v in _args.items() %}
|
| 111 |
+
{{- '<arg_key>' + k + '</arg_key>\n' }}
|
| 112 |
+
{{- '<arg_value>' + (v | tojson(ensure_ascii=False) if v is not string else v) + '</arg_value>\n' }}
|
| 113 |
+
{%- endfor %}
|
| 114 |
+
{%- endif %}
|
| 115 |
+
{{- '</tool_call>' }}
|
| 116 |
+
{%- endfor %}
|
| 117 |
+
{%- endif %}
|
| 118 |
+
{{- '<|im_end|>\n' }}
|
| 119 |
+
{%- elif message.role == 'tool' %}
|
| 120 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != 'tool') %}
|
| 121 |
+
{{- '<|im_start|>tool' }}
|
| 122 |
+
{%- endif %}
|
| 123 |
+
{{- '\n<tool_response>' + message.get('name', '') + '\n' }}
|
| 124 |
+
{{- content }}
|
| 125 |
+
{{- '\n</tool_response>' }}
|
| 126 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != 'tool') %}
|
| 127 |
+
{{- '<|im_end|>\n' }}
|
| 128 |
+
{%- endif %}
|
| 129 |
+
{%- endif %}
|
| 130 |
+
{%- endfor %}
|
| 131 |
+
{%- if add_generation_prompt %}
|
| 132 |
+
{%- if thinking is defined and thinking is true %}
|
| 133 |
+
{{- '<|im_start|>assistant\n<think>\n' }}
|
| 134 |
+
{%- else %}
|
| 135 |
+
{{- '<|im_start|>assistant\n<think>\n\n</think>\n\n' }}
|
| 136 |
+
{%- endif %}
|
| 137 |
+
{%- endif %}
|
config.json
ADDED
|
@@ -0,0 +1,97 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_cross_attention": false,
|
| 3 |
+
"architectures": [
|
| 4 |
+
"HyperCLOVAXForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
+
"attention_multiplier": 0.08838834764831845,
|
| 9 |
+
"auto_map": {
|
| 10 |
+
"AutoConfig": "configuration_hyperclovax.HyperCLOVAXConfig",
|
| 11 |
+
"AutoModel": "modeling_hyperclovax.HyperCLOVAXModel",
|
| 12 |
+
"AutoModelForCausalLM": "modeling_hyperclovax.HyperCLOVAXForCausalLM"
|
| 13 |
+
},
|
| 14 |
+
"bad_words_ids": null,
|
| 15 |
+
"begin_suppress_tokens": null,
|
| 16 |
+
"bos_token_id": 128000,
|
| 17 |
+
"chunk_size_feed_forward": 0,
|
| 18 |
+
"cross_attention_hidden_size": null,
|
| 19 |
+
"decoder_start_token_id": null,
|
| 20 |
+
"diversity_penalty": 0.0,
|
| 21 |
+
"do_sample": false,
|
| 22 |
+
"dtype": "bfloat16",
|
| 23 |
+
"early_stopping": false,
|
| 24 |
+
"embedding_multiplier": 1.0,
|
| 25 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 26 |
+
"end_token_id": 128001,
|
| 27 |
+
"eos_token_id": 128001,
|
| 28 |
+
"exponential_decay_length_penalty": null,
|
| 29 |
+
"finetuning_task": null,
|
| 30 |
+
"forced_bos_token_id": null,
|
| 31 |
+
"forced_eos_token_id": null,
|
| 32 |
+
"head_dim": 128,
|
| 33 |
+
"hidden_act": "silu",
|
| 34 |
+
"hidden_size": 5120,
|
| 35 |
+
"id2label": {
|
| 36 |
+
"0": "LABEL_0",
|
| 37 |
+
"1": "LABEL_1"
|
| 38 |
+
},
|
| 39 |
+
"initializer_range": 0.006,
|
| 40 |
+
"intermediate_size": 24192,
|
| 41 |
+
"is_decoder": false,
|
| 42 |
+
"is_encoder_decoder": false,
|
| 43 |
+
"label2id": {
|
| 44 |
+
"LABEL_0": 0,
|
| 45 |
+
"LABEL_1": 1
|
| 46 |
+
},
|
| 47 |
+
"length_penalty": 1.0,
|
| 48 |
+
"logits_scaling": 1.0,
|
| 49 |
+
"max_length": 20,
|
| 50 |
+
"max_position_embeddings": 131072,
|
| 51 |
+
"min_length": 0,
|
| 52 |
+
"mlp_bias": false,
|
| 53 |
+
"model_type": "hyperclovax",
|
| 54 |
+
"no_repeat_ngram_size": 0,
|
| 55 |
+
"num_attention_heads": 40,
|
| 56 |
+
"num_beam_groups": 1,
|
| 57 |
+
"num_beams": 1,
|
| 58 |
+
"num_hidden_layers": 72,
|
| 59 |
+
"num_key_value_heads": 8,
|
| 60 |
+
"num_return_sequences": 1,
|
| 61 |
+
"output_attentions": false,
|
| 62 |
+
"output_hidden_states": false,
|
| 63 |
+
"output_scores": false,
|
| 64 |
+
"pad_token_id": 0,
|
| 65 |
+
"prefix": null,
|
| 66 |
+
"pretraining_tp": 1,
|
| 67 |
+
"problem_type": null,
|
| 68 |
+
"pruned_heads": {},
|
| 69 |
+
"remove_invalid_values": false,
|
| 70 |
+
"repetition_penalty": 1.0,
|
| 71 |
+
"resid_pdrop": 0.2,
|
| 72 |
+
"residual_multiplier": 1.0,
|
| 73 |
+
"return_dict": true,
|
| 74 |
+
"return_dict_in_generate": false,
|
| 75 |
+
"rms_norm_eps": 1e-05,
|
| 76 |
+
"rope_scaling": null,
|
| 77 |
+
"rope_theta": 50000000,
|
| 78 |
+
"sep_token_id": null,
|
| 79 |
+
"suppress_tokens": null,
|
| 80 |
+
"task_specific_params": null,
|
| 81 |
+
"temperature": 1.0,
|
| 82 |
+
"tf_legacy_loss": false,
|
| 83 |
+
"tie_encoder_decoder": false,
|
| 84 |
+
"tie_word_embeddings": false,
|
| 85 |
+
"tokenizer_class": null,
|
| 86 |
+
"top_k": 50,
|
| 87 |
+
"top_p": 1.0,
|
| 88 |
+
"torch_dtype": "bfloat16",
|
| 89 |
+
"torchscript": false,
|
| 90 |
+
"typical_p": 1.0,
|
| 91 |
+
"use_bfloat16": false,
|
| 92 |
+
"use_cache": false,
|
| 93 |
+
"use_post_norm": false,
|
| 94 |
+
"vocab_size": 128256,
|
| 95 |
+
"_name_or_path": "/home/brian/models/HyperCLOVAX-SEED-Text-32B",
|
| 96 |
+
"transformers_version": "4.52.4"
|
| 97 |
+
}
|
configuration_hyperclovax.py
ADDED
|
@@ -0,0 +1,228 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
| 5 |
+
# and OPT implementations in this library. It has been modified from its
|
| 6 |
+
# original forms to accommodate minor architectural differences compared
|
| 7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
| 8 |
+
#
|
| 9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 10 |
+
# you may not use this file except in compliance with the License.
|
| 11 |
+
# You may obtain a copy of the License at
|
| 12 |
+
#
|
| 13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 14 |
+
#
|
| 15 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 18 |
+
# See the License for the specific language governing permissions and
|
| 19 |
+
# limitations under the License.
|
| 20 |
+
"""LLaMA model configuration"""
|
| 21 |
+
|
| 22 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 23 |
+
|
| 24 |
+
# from transformers.modeling_rope_utils import rope_config_validation
|
| 25 |
+
# from transformers import PretrainedConfig, rope_config_validation
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
class HyperCLOVAXConfig(PretrainedConfig):
|
| 29 |
+
r"""
|
| 30 |
+
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
|
| 31 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
| 32 |
+
defaults will yield a similar configuration to that of the LLaMA-7B.
|
| 33 |
+
|
| 34 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 35 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
Args:
|
| 39 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
| 40 |
+
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
|
| 41 |
+
`inputs_ids` passed when calling [`LlamaModel`]
|
| 42 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
| 43 |
+
Dimension of the hidden representations.
|
| 44 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
| 45 |
+
Dimension of the MLP representations.
|
| 46 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
| 47 |
+
Number of hidden layers in the Transformer decoder.
|
| 48 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 49 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
| 50 |
+
num_key_value_heads (`int`, *optional*):
|
| 51 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 52 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 53 |
+
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 54 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 55 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
| 56 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
| 57 |
+
`num_attention_heads`.
|
| 58 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| 59 |
+
The non-linear activation function (function or string) in the decoder.
|
| 60 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
| 61 |
+
The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
|
| 62 |
+
Llama 2 up to 4096, CodeLlama up to 16384.
|
| 63 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 64 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 65 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
| 66 |
+
The epsilon used by the rms normalization layers.
|
| 67 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 68 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 69 |
+
relevant if `config.is_decoder=True`.
|
| 70 |
+
pad_token_id (`int`, *optional*):
|
| 71 |
+
Padding token id.
|
| 72 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
| 73 |
+
Beginning of stream token id.
|
| 74 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
| 75 |
+
End of stream token id.
|
| 76 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
| 77 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
| 78 |
+
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
|
| 79 |
+
understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
|
| 80 |
+
results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
|
| 81 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 82 |
+
Whether to tie weight embeddings
|
| 83 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
| 84 |
+
The base period of the RoPE embeddings.
|
| 85 |
+
rope_scaling (`Dict`, *optional*):
|
| 86 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
| 87 |
+
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
| 88 |
+
accordingly.
|
| 89 |
+
Expected contents:
|
| 90 |
+
`rope_type` (`str`):
|
| 91 |
+
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
| 92 |
+
'llama3'], with 'default' being the original RoPE implementation.
|
| 93 |
+
`factor` (`float`, *optional*):
|
| 94 |
+
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
| 95 |
+
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
| 96 |
+
original maximum pre-trained length.
|
| 97 |
+
`original_max_position_embeddings` (`int`, *optional*):
|
| 98 |
+
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
| 99 |
+
pretraining.
|
| 100 |
+
`attention_factor` (`float`, *optional*):
|
| 101 |
+
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
| 102 |
+
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
| 103 |
+
`factor` field to infer the suggested value.
|
| 104 |
+
`beta_fast` (`float`, *optional*):
|
| 105 |
+
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
| 106 |
+
ramp function. If unspecified, it defaults to 32.
|
| 107 |
+
`beta_slow` (`float`, *optional*):
|
| 108 |
+
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
| 109 |
+
ramp function. If unspecified, it defaults to 1.
|
| 110 |
+
`short_factor` (`List[float]`, *optional*):
|
| 111 |
+
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
| 112 |
+
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
| 113 |
+
size divided by the number of attention heads divided by 2
|
| 114 |
+
`long_factor` (`List[float]`, *optional*):
|
| 115 |
+
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
| 116 |
+
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
| 117 |
+
size divided by the number of attention heads divided by 2
|
| 118 |
+
`low_freq_factor` (`float`, *optional*):
|
| 119 |
+
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
| 120 |
+
`high_freq_factor` (`float`, *optional*):
|
| 121 |
+
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
| 122 |
+
attention_bias (`bool`, *optional*, defaults to `False`):
|
| 123 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
| 124 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
| 125 |
+
The dropout ratio for the attention probabilities.
|
| 126 |
+
mlp_bias (`bool`, *optional*, defaults to `False`):
|
| 127 |
+
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
|
| 128 |
+
head_dim (`int`, *optional*):
|
| 129 |
+
The attention head dimension. If None, it will default to hidden_size // num_heads
|
| 130 |
+
|
| 131 |
+
```python
|
| 132 |
+
>>> from transformers import LlamaModel, LlamaConfig
|
| 133 |
+
|
| 134 |
+
>>> # Initializing a LLaMA llama-7b style configuration
|
| 135 |
+
>>> configuration = LlamaConfig()
|
| 136 |
+
|
| 137 |
+
>>> # Initializing a model from the llama-7b style configuration
|
| 138 |
+
>>> model = LlamaModel(configuration)
|
| 139 |
+
|
| 140 |
+
>>> # Accessing the model configuration
|
| 141 |
+
>>> configuration = model.config
|
| 142 |
+
```"""
|
| 143 |
+
|
| 144 |
+
model_type = "hyperclovax"
|
| 145 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 146 |
+
|
| 147 |
+
def __init__(
|
| 148 |
+
self,
|
| 149 |
+
vocab_size=32000,
|
| 150 |
+
hidden_size=4096,
|
| 151 |
+
intermediate_size=11008,
|
| 152 |
+
num_hidden_layers=32,
|
| 153 |
+
num_attention_heads=32,
|
| 154 |
+
num_key_value_heads=None,
|
| 155 |
+
hidden_act="silu",
|
| 156 |
+
max_position_embeddings=2048,
|
| 157 |
+
initializer_range=0.02,
|
| 158 |
+
rms_norm_eps=1e-6,
|
| 159 |
+
use_cache=True,
|
| 160 |
+
pad_token_id=None,
|
| 161 |
+
bos_token_id=1,
|
| 162 |
+
eos_token_id=2,
|
| 163 |
+
pretraining_tp=1,
|
| 164 |
+
tie_word_embeddings=False,
|
| 165 |
+
rope_theta=10000.0,
|
| 166 |
+
rope_scaling=None,
|
| 167 |
+
attention_bias=False,
|
| 168 |
+
attention_dropout=0.0,
|
| 169 |
+
mlp_bias=False,
|
| 170 |
+
head_dim=None,
|
| 171 |
+
embedding_multiplier=1.0, # mup
|
| 172 |
+
logits_scaling=1.0, # mup
|
| 173 |
+
attention_multiplier=1.0, # mup
|
| 174 |
+
residual_multiplier=1.0, # mup
|
| 175 |
+
use_post_norm=False, # post-norm
|
| 176 |
+
auto_map={
|
| 177 |
+
"AutoConfig": "configuration_hyperclovax.HyperCLOVAXConfig",
|
| 178 |
+
"AutoModel": "modeling_hyperclovax.HyperCLOVAXModel",
|
| 179 |
+
"AutoModelForCausalLM": "modeling_hyperclovax.HyperCLOVAXForCausalLM",
|
| 180 |
+
},
|
| 181 |
+
**kwargs,
|
| 182 |
+
):
|
| 183 |
+
self.vocab_size = vocab_size
|
| 184 |
+
self.max_position_embeddings = max_position_embeddings
|
| 185 |
+
self.hidden_size = hidden_size
|
| 186 |
+
self.intermediate_size = intermediate_size
|
| 187 |
+
self.num_hidden_layers = num_hidden_layers
|
| 188 |
+
self.num_attention_heads = num_attention_heads
|
| 189 |
+
|
| 190 |
+
# for backward compatibility
|
| 191 |
+
if num_key_value_heads is None:
|
| 192 |
+
num_key_value_heads = num_attention_heads
|
| 193 |
+
|
| 194 |
+
self.num_key_value_heads = num_key_value_heads
|
| 195 |
+
self.hidden_act = hidden_act
|
| 196 |
+
self.initializer_range = initializer_range
|
| 197 |
+
self.rms_norm_eps = rms_norm_eps
|
| 198 |
+
self.pretraining_tp = pretraining_tp
|
| 199 |
+
self.use_cache = use_cache
|
| 200 |
+
self.rope_theta = rope_theta
|
| 201 |
+
self.rope_scaling = rope_scaling
|
| 202 |
+
self.attention_bias = attention_bias
|
| 203 |
+
self.attention_dropout = attention_dropout
|
| 204 |
+
self.mlp_bias = mlp_bias
|
| 205 |
+
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
|
| 206 |
+
# Validate the correctness of rotary position embeddings parameters
|
| 207 |
+
# BC: if there is a 'type' field, copy it it to 'rope_type'.
|
| 208 |
+
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
| 209 |
+
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
| 210 |
+
# rope_config_validation(self)
|
| 211 |
+
|
| 212 |
+
# mup
|
| 213 |
+
self.embedding_multiplier = embedding_multiplier
|
| 214 |
+
self.logits_scaling = logits_scaling
|
| 215 |
+
self.attention_multiplier = attention_multiplier
|
| 216 |
+
self.residual_multiplier = residual_multiplier
|
| 217 |
+
|
| 218 |
+
# post-norm (dual-norm)
|
| 219 |
+
self.use_post_norm = use_post_norm
|
| 220 |
+
|
| 221 |
+
super().__init__(
|
| 222 |
+
pad_token_id=pad_token_id,
|
| 223 |
+
bos_token_id=bos_token_id,
|
| 224 |
+
eos_token_id=eos_token_id,
|
| 225 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 226 |
+
auto_map=auto_map,
|
| 227 |
+
**kwargs,
|
| 228 |
+
)
|
generation_config.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 128000,
|
| 4 |
+
"eos_token_id": 128001,
|
| 5 |
+
"pad_token_id": 0,
|
| 6 |
+
"transformers_version": "4.52.4",
|
| 7 |
+
"use_cache": false
|
| 8 |
+
}
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
model-00001-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3dcf1b621edecf413c0139db65dfa5cad76257b001a384f29a374310122dcd36
|
| 3 |
+
size 5233780272
|
model-00002-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1cd62521eb383eab1b2813e17ef12d1071efe3d8cd7483abf4979d9bff807bac
|
| 3 |
+
size 5214173656
|
model-00003-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e47583c64edc9929cddf93d4a77b50962c66d56798864943203ba7053d1b1fb4
|
| 3 |
+
size 5214173648
|
model-00004-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:112efea40c23f556021bcc754e872f90f7152cfc7acc7fa959517652921d6870
|
| 3 |
+
size 5214173656
|
model-00005-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:12c00b1534c97731baee3123b84b84dbaada195c307123c03623e64bdf6270e8
|
| 3 |
+
size 5214173648
|
model-00006-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:256420e9850f07f56b04f84889b80c1c8eebccd27d88edfbef7c603b6980d011
|
| 3 |
+
size 5214173656
|
model-00007-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4a56e0c76182e7a728a543f86ada80fb4464c203940287a0f246720a891f16cb
|
| 3 |
+
size 5214173648
|
model-00008-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2b662137975294b89c7396194e9e4fcb2590f1979d39c4b73a080af8985233d9
|
| 3 |
+
size 5214173656
|
model-00009-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1e02b2da36096dc1ca7c1ec69e9697b81fcc756679760c816c1a4538811d0717
|
| 3 |
+
size 5214173648
|
model-00010-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:85f91c706a802aa5430b705679df89c48acc859ce611def72c33e49de104d449
|
| 3 |
+
size 5214173648
|
model-00011-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ca83c0d2409d124c35057b10fe05a56096cff81cb629f1153fd9227f88f952e8
|
| 3 |
+
size 5214173656
|
model-00012-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:52fffd435f4fe5c2d4e6d925f04826462fb72344d2732dfc67734a444901f0b6
|
| 3 |
+
size 5214173648
|
model-00013-of-00013.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:38c6d54bd1ee971ded9d74ea0662de63f05c36df20c6e84ad93d4ebe35babca8
|
| 3 |
+
size 2607086784
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,658 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"total_size": 65196697600
|
| 4 |
+
},
|
| 5 |
+
"weight_map": {
|
| 6 |
+
"model.lm_head.weight": "model-00001-of-00013.safetensors",
|
| 7 |
+
"model.model.embed_tokens.weight": "model-00001-of-00013.safetensors",
|
| 8 |
+
"model.model.layers.0.input_layernorm.weight": "model-00001-of-00013.safetensors",
|
| 9 |
+
"model.model.layers.0.mlp.down_proj.weight": "model-00001-of-00013.safetensors",
|
| 10 |
+
"model.model.layers.0.mlp.gate_proj.weight": "model-00001-of-00013.safetensors",
|
| 11 |
+
"model.model.layers.0.mlp.up_proj.weight": "model-00001-of-00013.safetensors",
|
| 12 |
+
"model.model.layers.0.post_attention_layernorm.weight": "model-00001-of-00013.safetensors",
|
| 13 |
+
"model.model.layers.0.self_attn.k_proj.weight": "model-00001-of-00013.safetensors",
|
| 14 |
+
"model.model.layers.0.self_attn.o_proj.weight": "model-00001-of-00013.safetensors",
|
| 15 |
+
"model.model.layers.0.self_attn.q_proj.weight": "model-00001-of-00013.safetensors",
|
| 16 |
+
"model.model.layers.0.self_attn.v_proj.weight": "model-00001-of-00013.safetensors",
|
| 17 |
+
"model.model.layers.1.input_layernorm.weight": "model-00001-of-00013.safetensors",
|
| 18 |
+
"model.model.layers.1.mlp.down_proj.weight": "model-00001-of-00013.safetensors",
|
| 19 |
+
"model.model.layers.1.mlp.gate_proj.weight": "model-00001-of-00013.safetensors",
|
| 20 |
+
"model.model.layers.1.mlp.up_proj.weight": "model-00001-of-00013.safetensors",
|
| 21 |
+
"model.model.layers.1.post_attention_layernorm.weight": "model-00001-of-00013.safetensors",
|
| 22 |
+
"model.model.layers.1.self_attn.k_proj.weight": "model-00001-of-00013.safetensors",
|
| 23 |
+
"model.model.layers.1.self_attn.o_proj.weight": "model-00001-of-00013.safetensors",
|
| 24 |
+
"model.model.layers.1.self_attn.q_proj.weight": "model-00001-of-00013.safetensors",
|
| 25 |
+
"model.model.layers.1.self_attn.v_proj.weight": "model-00001-of-00013.safetensors",
|
| 26 |
+
"model.model.layers.10.input_layernorm.weight": "model-00001-of-00013.safetensors",
|
| 27 |
+
"model.model.layers.10.mlp.down_proj.weight": "model-00001-of-00013.safetensors",
|
| 28 |
+
"model.model.layers.10.mlp.gate_proj.weight": "model-00001-of-00013.safetensors",
|
| 29 |
+
"model.model.layers.10.mlp.up_proj.weight": "model-00001-of-00013.safetensors",
|
| 30 |
+
"model.model.layers.10.post_attention_layernorm.weight": "model-00001-of-00013.safetensors",
|
| 31 |
+
"model.model.layers.10.self_attn.k_proj.weight": "model-00001-of-00013.safetensors",
|
| 32 |
+
"model.model.layers.10.self_attn.o_proj.weight": "model-00001-of-00013.safetensors",
|
| 33 |
+
"model.model.layers.10.self_attn.q_proj.weight": "model-00001-of-00013.safetensors",
|
| 34 |
+
"model.model.layers.10.self_attn.v_proj.weight": "model-00001-of-00013.safetensors",
|
| 35 |
+
"model.model.layers.11.input_layernorm.weight": "model-00001-of-00013.safetensors",
|
| 36 |
+
"model.model.layers.11.mlp.down_proj.weight": "model-00002-of-00013.safetensors",
|
| 37 |
+
"model.model.layers.11.mlp.gate_proj.weight": "model-00002-of-00013.safetensors",
|
| 38 |
+
"model.model.layers.11.mlp.up_proj.weight": "model-00002-of-00013.safetensors",
|
| 39 |
+
"model.model.layers.11.post_attention_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 40 |
+
"model.model.layers.11.self_attn.k_proj.weight": "model-00002-of-00013.safetensors",
|
| 41 |
+
"model.model.layers.11.self_attn.o_proj.weight": "model-00002-of-00013.safetensors",
|
| 42 |
+
"model.model.layers.11.self_attn.q_proj.weight": "model-00002-of-00013.safetensors",
|
| 43 |
+
"model.model.layers.11.self_attn.v_proj.weight": "model-00002-of-00013.safetensors",
|
| 44 |
+
"model.model.layers.12.input_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 45 |
+
"model.model.layers.12.mlp.down_proj.weight": "model-00002-of-00013.safetensors",
|
| 46 |
+
"model.model.layers.12.mlp.gate_proj.weight": "model-00002-of-00013.safetensors",
|
| 47 |
+
"model.model.layers.12.mlp.up_proj.weight": "model-00002-of-00013.safetensors",
|
| 48 |
+
"model.model.layers.12.post_attention_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 49 |
+
"model.model.layers.12.self_attn.k_proj.weight": "model-00002-of-00013.safetensors",
|
| 50 |
+
"model.model.layers.12.self_attn.o_proj.weight": "model-00002-of-00013.safetensors",
|
| 51 |
+
"model.model.layers.12.self_attn.q_proj.weight": "model-00002-of-00013.safetensors",
|
| 52 |
+
"model.model.layers.12.self_attn.v_proj.weight": "model-00002-of-00013.safetensors",
|
| 53 |
+
"model.model.layers.13.input_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 54 |
+
"model.model.layers.13.mlp.down_proj.weight": "model-00002-of-00013.safetensors",
|
| 55 |
+
"model.model.layers.13.mlp.gate_proj.weight": "model-00002-of-00013.safetensors",
|
| 56 |
+
"model.model.layers.13.mlp.up_proj.weight": "model-00002-of-00013.safetensors",
|
| 57 |
+
"model.model.layers.13.post_attention_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 58 |
+
"model.model.layers.13.self_attn.k_proj.weight": "model-00002-of-00013.safetensors",
|
| 59 |
+
"model.model.layers.13.self_attn.o_proj.weight": "model-00002-of-00013.safetensors",
|
| 60 |
+
"model.model.layers.13.self_attn.q_proj.weight": "model-00002-of-00013.safetensors",
|
| 61 |
+
"model.model.layers.13.self_attn.v_proj.weight": "model-00002-of-00013.safetensors",
|
| 62 |
+
"model.model.layers.14.input_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 63 |
+
"model.model.layers.14.mlp.down_proj.weight": "model-00002-of-00013.safetensors",
|
| 64 |
+
"model.model.layers.14.mlp.gate_proj.weight": "model-00002-of-00013.safetensors",
|
| 65 |
+
"model.model.layers.14.mlp.up_proj.weight": "model-00002-of-00013.safetensors",
|
| 66 |
+
"model.model.layers.14.post_attention_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 67 |
+
"model.model.layers.14.self_attn.k_proj.weight": "model-00002-of-00013.safetensors",
|
| 68 |
+
"model.model.layers.14.self_attn.o_proj.weight": "model-00002-of-00013.safetensors",
|
| 69 |
+
"model.model.layers.14.self_attn.q_proj.weight": "model-00002-of-00013.safetensors",
|
| 70 |
+
"model.model.layers.14.self_attn.v_proj.weight": "model-00002-of-00013.safetensors",
|
| 71 |
+
"model.model.layers.15.input_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 72 |
+
"model.model.layers.15.mlp.down_proj.weight": "model-00002-of-00013.safetensors",
|
| 73 |
+
"model.model.layers.15.mlp.gate_proj.weight": "model-00002-of-00013.safetensors",
|
| 74 |
+
"model.model.layers.15.mlp.up_proj.weight": "model-00002-of-00013.safetensors",
|
| 75 |
+
"model.model.layers.15.post_attention_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 76 |
+
"model.model.layers.15.self_attn.k_proj.weight": "model-00002-of-00013.safetensors",
|
| 77 |
+
"model.model.layers.15.self_attn.o_proj.weight": "model-00002-of-00013.safetensors",
|
| 78 |
+
"model.model.layers.15.self_attn.q_proj.weight": "model-00002-of-00013.safetensors",
|
| 79 |
+
"model.model.layers.15.self_attn.v_proj.weight": "model-00002-of-00013.safetensors",
|
| 80 |
+
"model.model.layers.16.input_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 81 |
+
"model.model.layers.16.mlp.down_proj.weight": "model-00002-of-00013.safetensors",
|
| 82 |
+
"model.model.layers.16.mlp.gate_proj.weight": "model-00002-of-00013.safetensors",
|
| 83 |
+
"model.model.layers.16.mlp.up_proj.weight": "model-00002-of-00013.safetensors",
|
| 84 |
+
"model.model.layers.16.post_attention_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 85 |
+
"model.model.layers.16.self_attn.k_proj.weight": "model-00002-of-00013.safetensors",
|
| 86 |
+
"model.model.layers.16.self_attn.o_proj.weight": "model-00002-of-00013.safetensors",
|
| 87 |
+
"model.model.layers.16.self_attn.q_proj.weight": "model-00002-of-00013.safetensors",
|
| 88 |
+
"model.model.layers.16.self_attn.v_proj.weight": "model-00002-of-00013.safetensors",
|
| 89 |
+
"model.model.layers.17.input_layernorm.weight": "model-00002-of-00013.safetensors",
|
| 90 |
+
"model.model.layers.17.mlp.down_proj.weight": "model-00003-of-00013.safetensors",
|
| 91 |
+
"model.model.layers.17.mlp.gate_proj.weight": "model-00003-of-00013.safetensors",
|
| 92 |
+
"model.model.layers.17.mlp.up_proj.weight": "model-00003-of-00013.safetensors",
|
| 93 |
+
"model.model.layers.17.post_attention_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 94 |
+
"model.model.layers.17.self_attn.k_proj.weight": "model-00003-of-00013.safetensors",
|
| 95 |
+
"model.model.layers.17.self_attn.o_proj.weight": "model-00003-of-00013.safetensors",
|
| 96 |
+
"model.model.layers.17.self_attn.q_proj.weight": "model-00003-of-00013.safetensors",
|
| 97 |
+
"model.model.layers.17.self_attn.v_proj.weight": "model-00003-of-00013.safetensors",
|
| 98 |
+
"model.model.layers.18.input_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 99 |
+
"model.model.layers.18.mlp.down_proj.weight": "model-00003-of-00013.safetensors",
|
| 100 |
+
"model.model.layers.18.mlp.gate_proj.weight": "model-00003-of-00013.safetensors",
|
| 101 |
+
"model.model.layers.18.mlp.up_proj.weight": "model-00003-of-00013.safetensors",
|
| 102 |
+
"model.model.layers.18.post_attention_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 103 |
+
"model.model.layers.18.self_attn.k_proj.weight": "model-00003-of-00013.safetensors",
|
| 104 |
+
"model.model.layers.18.self_attn.o_proj.weight": "model-00003-of-00013.safetensors",
|
| 105 |
+
"model.model.layers.18.self_attn.q_proj.weight": "model-00003-of-00013.safetensors",
|
| 106 |
+
"model.model.layers.18.self_attn.v_proj.weight": "model-00003-of-00013.safetensors",
|
| 107 |
+
"model.model.layers.19.input_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 108 |
+
"model.model.layers.19.mlp.down_proj.weight": "model-00003-of-00013.safetensors",
|
| 109 |
+
"model.model.layers.19.mlp.gate_proj.weight": "model-00003-of-00013.safetensors",
|
| 110 |
+
"model.model.layers.19.mlp.up_proj.weight": "model-00003-of-00013.safetensors",
|
| 111 |
+
"model.model.layers.19.post_attention_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 112 |
+
"model.model.layers.19.self_attn.k_proj.weight": "model-00003-of-00013.safetensors",
|
| 113 |
+
"model.model.layers.19.self_attn.o_proj.weight": "model-00003-of-00013.safetensors",
|
| 114 |
+
"model.model.layers.19.self_attn.q_proj.weight": "model-00003-of-00013.safetensors",
|
| 115 |
+
"model.model.layers.19.self_attn.v_proj.weight": "model-00003-of-00013.safetensors",
|
| 116 |
+
"model.model.layers.2.input_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 117 |
+
"model.model.layers.2.mlp.down_proj.weight": "model-00003-of-00013.safetensors",
|
| 118 |
+
"model.model.layers.2.mlp.gate_proj.weight": "model-00003-of-00013.safetensors",
|
| 119 |
+
"model.model.layers.2.mlp.up_proj.weight": "model-00003-of-00013.safetensors",
|
| 120 |
+
"model.model.layers.2.post_attention_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 121 |
+
"model.model.layers.2.self_attn.k_proj.weight": "model-00003-of-00013.safetensors",
|
| 122 |
+
"model.model.layers.2.self_attn.o_proj.weight": "model-00003-of-00013.safetensors",
|
| 123 |
+
"model.model.layers.2.self_attn.q_proj.weight": "model-00003-of-00013.safetensors",
|
| 124 |
+
"model.model.layers.2.self_attn.v_proj.weight": "model-00003-of-00013.safetensors",
|
| 125 |
+
"model.model.layers.20.input_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 126 |
+
"model.model.layers.20.mlp.down_proj.weight": "model-00003-of-00013.safetensors",
|
| 127 |
+
"model.model.layers.20.mlp.gate_proj.weight": "model-00003-of-00013.safetensors",
|
| 128 |
+
"model.model.layers.20.mlp.up_proj.weight": "model-00003-of-00013.safetensors",
|
| 129 |
+
"model.model.layers.20.post_attention_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 130 |
+
"model.model.layers.20.self_attn.k_proj.weight": "model-00003-of-00013.safetensors",
|
| 131 |
+
"model.model.layers.20.self_attn.o_proj.weight": "model-00003-of-00013.safetensors",
|
| 132 |
+
"model.model.layers.20.self_attn.q_proj.weight": "model-00003-of-00013.safetensors",
|
| 133 |
+
"model.model.layers.20.self_attn.v_proj.weight": "model-00003-of-00013.safetensors",
|
| 134 |
+
"model.model.layers.21.input_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 135 |
+
"model.model.layers.21.mlp.down_proj.weight": "model-00003-of-00013.safetensors",
|
| 136 |
+
"model.model.layers.21.mlp.gate_proj.weight": "model-00003-of-00013.safetensors",
|
| 137 |
+
"model.model.layers.21.mlp.up_proj.weight": "model-00003-of-00013.safetensors",
|
| 138 |
+
"model.model.layers.21.post_attention_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 139 |
+
"model.model.layers.21.self_attn.k_proj.weight": "model-00003-of-00013.safetensors",
|
| 140 |
+
"model.model.layers.21.self_attn.o_proj.weight": "model-00003-of-00013.safetensors",
|
| 141 |
+
"model.model.layers.21.self_attn.q_proj.weight": "model-00003-of-00013.safetensors",
|
| 142 |
+
"model.model.layers.21.self_attn.v_proj.weight": "model-00003-of-00013.safetensors",
|
| 143 |
+
"model.model.layers.22.input_layernorm.weight": "model-00003-of-00013.safetensors",
|
| 144 |
+
"model.model.layers.22.mlp.down_proj.weight": "model-00004-of-00013.safetensors",
|
| 145 |
+
"model.model.layers.22.mlp.gate_proj.weight": "model-00004-of-00013.safetensors",
|
| 146 |
+
"model.model.layers.22.mlp.up_proj.weight": "model-00004-of-00013.safetensors",
|
| 147 |
+
"model.model.layers.22.post_attention_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 148 |
+
"model.model.layers.22.self_attn.k_proj.weight": "model-00004-of-00013.safetensors",
|
| 149 |
+
"model.model.layers.22.self_attn.o_proj.weight": "model-00004-of-00013.safetensors",
|
| 150 |
+
"model.model.layers.22.self_attn.q_proj.weight": "model-00004-of-00013.safetensors",
|
| 151 |
+
"model.model.layers.22.self_attn.v_proj.weight": "model-00004-of-00013.safetensors",
|
| 152 |
+
"model.model.layers.23.input_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 153 |
+
"model.model.layers.23.mlp.down_proj.weight": "model-00004-of-00013.safetensors",
|
| 154 |
+
"model.model.layers.23.mlp.gate_proj.weight": "model-00004-of-00013.safetensors",
|
| 155 |
+
"model.model.layers.23.mlp.up_proj.weight": "model-00004-of-00013.safetensors",
|
| 156 |
+
"model.model.layers.23.post_attention_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 157 |
+
"model.model.layers.23.self_attn.k_proj.weight": "model-00004-of-00013.safetensors",
|
| 158 |
+
"model.model.layers.23.self_attn.o_proj.weight": "model-00004-of-00013.safetensors",
|
| 159 |
+
"model.model.layers.23.self_attn.q_proj.weight": "model-00004-of-00013.safetensors",
|
| 160 |
+
"model.model.layers.23.self_attn.v_proj.weight": "model-00004-of-00013.safetensors",
|
| 161 |
+
"model.model.layers.24.input_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 162 |
+
"model.model.layers.24.mlp.down_proj.weight": "model-00004-of-00013.safetensors",
|
| 163 |
+
"model.model.layers.24.mlp.gate_proj.weight": "model-00004-of-00013.safetensors",
|
| 164 |
+
"model.model.layers.24.mlp.up_proj.weight": "model-00004-of-00013.safetensors",
|
| 165 |
+
"model.model.layers.24.post_attention_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 166 |
+
"model.model.layers.24.self_attn.k_proj.weight": "model-00004-of-00013.safetensors",
|
| 167 |
+
"model.model.layers.24.self_attn.o_proj.weight": "model-00004-of-00013.safetensors",
|
| 168 |
+
"model.model.layers.24.self_attn.q_proj.weight": "model-00004-of-00013.safetensors",
|
| 169 |
+
"model.model.layers.24.self_attn.v_proj.weight": "model-00004-of-00013.safetensors",
|
| 170 |
+
"model.model.layers.25.input_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 171 |
+
"model.model.layers.25.mlp.down_proj.weight": "model-00004-of-00013.safetensors",
|
| 172 |
+
"model.model.layers.25.mlp.gate_proj.weight": "model-00004-of-00013.safetensors",
|
| 173 |
+
"model.model.layers.25.mlp.up_proj.weight": "model-00004-of-00013.safetensors",
|
| 174 |
+
"model.model.layers.25.post_attention_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 175 |
+
"model.model.layers.25.self_attn.k_proj.weight": "model-00004-of-00013.safetensors",
|
| 176 |
+
"model.model.layers.25.self_attn.o_proj.weight": "model-00004-of-00013.safetensors",
|
| 177 |
+
"model.model.layers.25.self_attn.q_proj.weight": "model-00004-of-00013.safetensors",
|
| 178 |
+
"model.model.layers.25.self_attn.v_proj.weight": "model-00004-of-00013.safetensors",
|
| 179 |
+
"model.model.layers.26.input_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 180 |
+
"model.model.layers.26.mlp.down_proj.weight": "model-00004-of-00013.safetensors",
|
| 181 |
+
"model.model.layers.26.mlp.gate_proj.weight": "model-00004-of-00013.safetensors",
|
| 182 |
+
"model.model.layers.26.mlp.up_proj.weight": "model-00004-of-00013.safetensors",
|
| 183 |
+
"model.model.layers.26.post_attention_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 184 |
+
"model.model.layers.26.self_attn.k_proj.weight": "model-00004-of-00013.safetensors",
|
| 185 |
+
"model.model.layers.26.self_attn.o_proj.weight": "model-00004-of-00013.safetensors",
|
| 186 |
+
"model.model.layers.26.self_attn.q_proj.weight": "model-00004-of-00013.safetensors",
|
| 187 |
+
"model.model.layers.26.self_attn.v_proj.weight": "model-00004-of-00013.safetensors",
|
| 188 |
+
"model.model.layers.27.input_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 189 |
+
"model.model.layers.27.mlp.down_proj.weight": "model-00004-of-00013.safetensors",
|
| 190 |
+
"model.model.layers.27.mlp.gate_proj.weight": "model-00004-of-00013.safetensors",
|
| 191 |
+
"model.model.layers.27.mlp.up_proj.weight": "model-00004-of-00013.safetensors",
|
| 192 |
+
"model.model.layers.27.post_attention_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 193 |
+
"model.model.layers.27.self_attn.k_proj.weight": "model-00004-of-00013.safetensors",
|
| 194 |
+
"model.model.layers.27.self_attn.o_proj.weight": "model-00004-of-00013.safetensors",
|
| 195 |
+
"model.model.layers.27.self_attn.q_proj.weight": "model-00004-of-00013.safetensors",
|
| 196 |
+
"model.model.layers.27.self_attn.v_proj.weight": "model-00004-of-00013.safetensors",
|
| 197 |
+
"model.model.layers.28.input_layernorm.weight": "model-00004-of-00013.safetensors",
|
| 198 |
+
"model.model.layers.28.mlp.down_proj.weight": "model-00005-of-00013.safetensors",
|
| 199 |
+
"model.model.layers.28.mlp.gate_proj.weight": "model-00005-of-00013.safetensors",
|
| 200 |
+
"model.model.layers.28.mlp.up_proj.weight": "model-00005-of-00013.safetensors",
|
| 201 |
+
"model.model.layers.28.post_attention_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 202 |
+
"model.model.layers.28.self_attn.k_proj.weight": "model-00005-of-00013.safetensors",
|
| 203 |
+
"model.model.layers.28.self_attn.o_proj.weight": "model-00005-of-00013.safetensors",
|
| 204 |
+
"model.model.layers.28.self_attn.q_proj.weight": "model-00005-of-00013.safetensors",
|
| 205 |
+
"model.model.layers.28.self_attn.v_proj.weight": "model-00005-of-00013.safetensors",
|
| 206 |
+
"model.model.layers.29.input_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 207 |
+
"model.model.layers.29.mlp.down_proj.weight": "model-00005-of-00013.safetensors",
|
| 208 |
+
"model.model.layers.29.mlp.gate_proj.weight": "model-00005-of-00013.safetensors",
|
| 209 |
+
"model.model.layers.29.mlp.up_proj.weight": "model-00005-of-00013.safetensors",
|
| 210 |
+
"model.model.layers.29.post_attention_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 211 |
+
"model.model.layers.29.self_attn.k_proj.weight": "model-00005-of-00013.safetensors",
|
| 212 |
+
"model.model.layers.29.self_attn.o_proj.weight": "model-00005-of-00013.safetensors",
|
| 213 |
+
"model.model.layers.29.self_attn.q_proj.weight": "model-00005-of-00013.safetensors",
|
| 214 |
+
"model.model.layers.29.self_attn.v_proj.weight": "model-00005-of-00013.safetensors",
|
| 215 |
+
"model.model.layers.3.input_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 216 |
+
"model.model.layers.3.mlp.down_proj.weight": "model-00005-of-00013.safetensors",
|
| 217 |
+
"model.model.layers.3.mlp.gate_proj.weight": "model-00005-of-00013.safetensors",
|
| 218 |
+
"model.model.layers.3.mlp.up_proj.weight": "model-00005-of-00013.safetensors",
|
| 219 |
+
"model.model.layers.3.post_attention_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 220 |
+
"model.model.layers.3.self_attn.k_proj.weight": "model-00005-of-00013.safetensors",
|
| 221 |
+
"model.model.layers.3.self_attn.o_proj.weight": "model-00005-of-00013.safetensors",
|
| 222 |
+
"model.model.layers.3.self_attn.q_proj.weight": "model-00005-of-00013.safetensors",
|
| 223 |
+
"model.model.layers.3.self_attn.v_proj.weight": "model-00005-of-00013.safetensors",
|
| 224 |
+
"model.model.layers.30.input_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 225 |
+
"model.model.layers.30.mlp.down_proj.weight": "model-00005-of-00013.safetensors",
|
| 226 |
+
"model.model.layers.30.mlp.gate_proj.weight": "model-00005-of-00013.safetensors",
|
| 227 |
+
"model.model.layers.30.mlp.up_proj.weight": "model-00005-of-00013.safetensors",
|
| 228 |
+
"model.model.layers.30.post_attention_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 229 |
+
"model.model.layers.30.self_attn.k_proj.weight": "model-00005-of-00013.safetensors",
|
| 230 |
+
"model.model.layers.30.self_attn.o_proj.weight": "model-00005-of-00013.safetensors",
|
| 231 |
+
"model.model.layers.30.self_attn.q_proj.weight": "model-00005-of-00013.safetensors",
|
| 232 |
+
"model.model.layers.30.self_attn.v_proj.weight": "model-00005-of-00013.safetensors",
|
| 233 |
+
"model.model.layers.31.input_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 234 |
+
"model.model.layers.31.mlp.down_proj.weight": "model-00005-of-00013.safetensors",
|
| 235 |
+
"model.model.layers.31.mlp.gate_proj.weight": "model-00005-of-00013.safetensors",
|
| 236 |
+
"model.model.layers.31.mlp.up_proj.weight": "model-00005-of-00013.safetensors",
|
| 237 |
+
"model.model.layers.31.post_attention_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 238 |
+
"model.model.layers.31.self_attn.k_proj.weight": "model-00005-of-00013.safetensors",
|
| 239 |
+
"model.model.layers.31.self_attn.o_proj.weight": "model-00005-of-00013.safetensors",
|
| 240 |
+
"model.model.layers.31.self_attn.q_proj.weight": "model-00005-of-00013.safetensors",
|
| 241 |
+
"model.model.layers.31.self_attn.v_proj.weight": "model-00005-of-00013.safetensors",
|
| 242 |
+
"model.model.layers.32.input_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 243 |
+
"model.model.layers.32.mlp.down_proj.weight": "model-00005-of-00013.safetensors",
|
| 244 |
+
"model.model.layers.32.mlp.gate_proj.weight": "model-00005-of-00013.safetensors",
|
| 245 |
+
"model.model.layers.32.mlp.up_proj.weight": "model-00005-of-00013.safetensors",
|
| 246 |
+
"model.model.layers.32.post_attention_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 247 |
+
"model.model.layers.32.self_attn.k_proj.weight": "model-00005-of-00013.safetensors",
|
| 248 |
+
"model.model.layers.32.self_attn.o_proj.weight": "model-00005-of-00013.safetensors",
|
| 249 |
+
"model.model.layers.32.self_attn.q_proj.weight": "model-00005-of-00013.safetensors",
|
| 250 |
+
"model.model.layers.32.self_attn.v_proj.weight": "model-00005-of-00013.safetensors",
|
| 251 |
+
"model.model.layers.33.input_layernorm.weight": "model-00005-of-00013.safetensors",
|
| 252 |
+
"model.model.layers.33.mlp.down_proj.weight": "model-00006-of-00013.safetensors",
|
| 253 |
+
"model.model.layers.33.mlp.gate_proj.weight": "model-00006-of-00013.safetensors",
|
| 254 |
+
"model.model.layers.33.mlp.up_proj.weight": "model-00006-of-00013.safetensors",
|
| 255 |
+
"model.model.layers.33.post_attention_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 256 |
+
"model.model.layers.33.self_attn.k_proj.weight": "model-00006-of-00013.safetensors",
|
| 257 |
+
"model.model.layers.33.self_attn.o_proj.weight": "model-00006-of-00013.safetensors",
|
| 258 |
+
"model.model.layers.33.self_attn.q_proj.weight": "model-00006-of-00013.safetensors",
|
| 259 |
+
"model.model.layers.33.self_attn.v_proj.weight": "model-00006-of-00013.safetensors",
|
| 260 |
+
"model.model.layers.34.input_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 261 |
+
"model.model.layers.34.mlp.down_proj.weight": "model-00006-of-00013.safetensors",
|
| 262 |
+
"model.model.layers.34.mlp.gate_proj.weight": "model-00006-of-00013.safetensors",
|
| 263 |
+
"model.model.layers.34.mlp.up_proj.weight": "model-00006-of-00013.safetensors",
|
| 264 |
+
"model.model.layers.34.post_attention_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 265 |
+
"model.model.layers.34.self_attn.k_proj.weight": "model-00006-of-00013.safetensors",
|
| 266 |
+
"model.model.layers.34.self_attn.o_proj.weight": "model-00006-of-00013.safetensors",
|
| 267 |
+
"model.model.layers.34.self_attn.q_proj.weight": "model-00006-of-00013.safetensors",
|
| 268 |
+
"model.model.layers.34.self_attn.v_proj.weight": "model-00006-of-00013.safetensors",
|
| 269 |
+
"model.model.layers.35.input_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 270 |
+
"model.model.layers.35.mlp.down_proj.weight": "model-00006-of-00013.safetensors",
|
| 271 |
+
"model.model.layers.35.mlp.gate_proj.weight": "model-00006-of-00013.safetensors",
|
| 272 |
+
"model.model.layers.35.mlp.up_proj.weight": "model-00006-of-00013.safetensors",
|
| 273 |
+
"model.model.layers.35.post_attention_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 274 |
+
"model.model.layers.35.self_attn.k_proj.weight": "model-00006-of-00013.safetensors",
|
| 275 |
+
"model.model.layers.35.self_attn.o_proj.weight": "model-00006-of-00013.safetensors",
|
| 276 |
+
"model.model.layers.35.self_attn.q_proj.weight": "model-00006-of-00013.safetensors",
|
| 277 |
+
"model.model.layers.35.self_attn.v_proj.weight": "model-00006-of-00013.safetensors",
|
| 278 |
+
"model.model.layers.36.input_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 279 |
+
"model.model.layers.36.mlp.down_proj.weight": "model-00006-of-00013.safetensors",
|
| 280 |
+
"model.model.layers.36.mlp.gate_proj.weight": "model-00006-of-00013.safetensors",
|
| 281 |
+
"model.model.layers.36.mlp.up_proj.weight": "model-00006-of-00013.safetensors",
|
| 282 |
+
"model.model.layers.36.post_attention_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 283 |
+
"model.model.layers.36.self_attn.k_proj.weight": "model-00006-of-00013.safetensors",
|
| 284 |
+
"model.model.layers.36.self_attn.o_proj.weight": "model-00006-of-00013.safetensors",
|
| 285 |
+
"model.model.layers.36.self_attn.q_proj.weight": "model-00006-of-00013.safetensors",
|
| 286 |
+
"model.model.layers.36.self_attn.v_proj.weight": "model-00006-of-00013.safetensors",
|
| 287 |
+
"model.model.layers.37.input_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 288 |
+
"model.model.layers.37.mlp.down_proj.weight": "model-00006-of-00013.safetensors",
|
| 289 |
+
"model.model.layers.37.mlp.gate_proj.weight": "model-00006-of-00013.safetensors",
|
| 290 |
+
"model.model.layers.37.mlp.up_proj.weight": "model-00006-of-00013.safetensors",
|
| 291 |
+
"model.model.layers.37.post_attention_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 292 |
+
"model.model.layers.37.self_attn.k_proj.weight": "model-00006-of-00013.safetensors",
|
| 293 |
+
"model.model.layers.37.self_attn.o_proj.weight": "model-00006-of-00013.safetensors",
|
| 294 |
+
"model.model.layers.37.self_attn.q_proj.weight": "model-00006-of-00013.safetensors",
|
| 295 |
+
"model.model.layers.37.self_attn.v_proj.weight": "model-00006-of-00013.safetensors",
|
| 296 |
+
"model.model.layers.38.input_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 297 |
+
"model.model.layers.38.mlp.down_proj.weight": "model-00006-of-00013.safetensors",
|
| 298 |
+
"model.model.layers.38.mlp.gate_proj.weight": "model-00006-of-00013.safetensors",
|
| 299 |
+
"model.model.layers.38.mlp.up_proj.weight": "model-00006-of-00013.safetensors",
|
| 300 |
+
"model.model.layers.38.post_attention_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 301 |
+
"model.model.layers.38.self_attn.k_proj.weight": "model-00006-of-00013.safetensors",
|
| 302 |
+
"model.model.layers.38.self_attn.o_proj.weight": "model-00006-of-00013.safetensors",
|
| 303 |
+
"model.model.layers.38.self_attn.q_proj.weight": "model-00006-of-00013.safetensors",
|
| 304 |
+
"model.model.layers.38.self_attn.v_proj.weight": "model-00006-of-00013.safetensors",
|
| 305 |
+
"model.model.layers.39.input_layernorm.weight": "model-00006-of-00013.safetensors",
|
| 306 |
+
"model.model.layers.39.mlp.down_proj.weight": "model-00007-of-00013.safetensors",
|
| 307 |
+
"model.model.layers.39.mlp.gate_proj.weight": "model-00007-of-00013.safetensors",
|
| 308 |
+
"model.model.layers.39.mlp.up_proj.weight": "model-00007-of-00013.safetensors",
|
| 309 |
+
"model.model.layers.39.post_attention_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 310 |
+
"model.model.layers.39.self_attn.k_proj.weight": "model-00007-of-00013.safetensors",
|
| 311 |
+
"model.model.layers.39.self_attn.o_proj.weight": "model-00007-of-00013.safetensors",
|
| 312 |
+
"model.model.layers.39.self_attn.q_proj.weight": "model-00007-of-00013.safetensors",
|
| 313 |
+
"model.model.layers.39.self_attn.v_proj.weight": "model-00007-of-00013.safetensors",
|
| 314 |
+
"model.model.layers.4.input_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 315 |
+
"model.model.layers.4.mlp.down_proj.weight": "model-00007-of-00013.safetensors",
|
| 316 |
+
"model.model.layers.4.mlp.gate_proj.weight": "model-00007-of-00013.safetensors",
|
| 317 |
+
"model.model.layers.4.mlp.up_proj.weight": "model-00007-of-00013.safetensors",
|
| 318 |
+
"model.model.layers.4.post_attention_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 319 |
+
"model.model.layers.4.self_attn.k_proj.weight": "model-00007-of-00013.safetensors",
|
| 320 |
+
"model.model.layers.4.self_attn.o_proj.weight": "model-00007-of-00013.safetensors",
|
| 321 |
+
"model.model.layers.4.self_attn.q_proj.weight": "model-00007-of-00013.safetensors",
|
| 322 |
+
"model.model.layers.4.self_attn.v_proj.weight": "model-00007-of-00013.safetensors",
|
| 323 |
+
"model.model.layers.40.input_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 324 |
+
"model.model.layers.40.mlp.down_proj.weight": "model-00007-of-00013.safetensors",
|
| 325 |
+
"model.model.layers.40.mlp.gate_proj.weight": "model-00007-of-00013.safetensors",
|
| 326 |
+
"model.model.layers.40.mlp.up_proj.weight": "model-00007-of-00013.safetensors",
|
| 327 |
+
"model.model.layers.40.post_attention_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 328 |
+
"model.model.layers.40.self_attn.k_proj.weight": "model-00007-of-00013.safetensors",
|
| 329 |
+
"model.model.layers.40.self_attn.o_proj.weight": "model-00007-of-00013.safetensors",
|
| 330 |
+
"model.model.layers.40.self_attn.q_proj.weight": "model-00007-of-00013.safetensors",
|
| 331 |
+
"model.model.layers.40.self_attn.v_proj.weight": "model-00007-of-00013.safetensors",
|
| 332 |
+
"model.model.layers.41.input_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 333 |
+
"model.model.layers.41.mlp.down_proj.weight": "model-00007-of-00013.safetensors",
|
| 334 |
+
"model.model.layers.41.mlp.gate_proj.weight": "model-00007-of-00013.safetensors",
|
| 335 |
+
"model.model.layers.41.mlp.up_proj.weight": "model-00007-of-00013.safetensors",
|
| 336 |
+
"model.model.layers.41.post_attention_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 337 |
+
"model.model.layers.41.self_attn.k_proj.weight": "model-00007-of-00013.safetensors",
|
| 338 |
+
"model.model.layers.41.self_attn.o_proj.weight": "model-00007-of-00013.safetensors",
|
| 339 |
+
"model.model.layers.41.self_attn.q_proj.weight": "model-00007-of-00013.safetensors",
|
| 340 |
+
"model.model.layers.41.self_attn.v_proj.weight": "model-00007-of-00013.safetensors",
|
| 341 |
+
"model.model.layers.42.input_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 342 |
+
"model.model.layers.42.mlp.down_proj.weight": "model-00007-of-00013.safetensors",
|
| 343 |
+
"model.model.layers.42.mlp.gate_proj.weight": "model-00007-of-00013.safetensors",
|
| 344 |
+
"model.model.layers.42.mlp.up_proj.weight": "model-00007-of-00013.safetensors",
|
| 345 |
+
"model.model.layers.42.post_attention_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 346 |
+
"model.model.layers.42.self_attn.k_proj.weight": "model-00007-of-00013.safetensors",
|
| 347 |
+
"model.model.layers.42.self_attn.o_proj.weight": "model-00007-of-00013.safetensors",
|
| 348 |
+
"model.model.layers.42.self_attn.q_proj.weight": "model-00007-of-00013.safetensors",
|
| 349 |
+
"model.model.layers.42.self_attn.v_proj.weight": "model-00007-of-00013.safetensors",
|
| 350 |
+
"model.model.layers.43.input_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 351 |
+
"model.model.layers.43.mlp.down_proj.weight": "model-00007-of-00013.safetensors",
|
| 352 |
+
"model.model.layers.43.mlp.gate_proj.weight": "model-00007-of-00013.safetensors",
|
| 353 |
+
"model.model.layers.43.mlp.up_proj.weight": "model-00007-of-00013.safetensors",
|
| 354 |
+
"model.model.layers.43.post_attention_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 355 |
+
"model.model.layers.43.self_attn.k_proj.weight": "model-00007-of-00013.safetensors",
|
| 356 |
+
"model.model.layers.43.self_attn.o_proj.weight": "model-00007-of-00013.safetensors",
|
| 357 |
+
"model.model.layers.43.self_attn.q_proj.weight": "model-00007-of-00013.safetensors",
|
| 358 |
+
"model.model.layers.43.self_attn.v_proj.weight": "model-00007-of-00013.safetensors",
|
| 359 |
+
"model.model.layers.44.input_layernorm.weight": "model-00007-of-00013.safetensors",
|
| 360 |
+
"model.model.layers.44.mlp.down_proj.weight": "model-00008-of-00013.safetensors",
|
| 361 |
+
"model.model.layers.44.mlp.gate_proj.weight": "model-00008-of-00013.safetensors",
|
| 362 |
+
"model.model.layers.44.mlp.up_proj.weight": "model-00008-of-00013.safetensors",
|
| 363 |
+
"model.model.layers.44.post_attention_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 364 |
+
"model.model.layers.44.self_attn.k_proj.weight": "model-00008-of-00013.safetensors",
|
| 365 |
+
"model.model.layers.44.self_attn.o_proj.weight": "model-00008-of-00013.safetensors",
|
| 366 |
+
"model.model.layers.44.self_attn.q_proj.weight": "model-00008-of-00013.safetensors",
|
| 367 |
+
"model.model.layers.44.self_attn.v_proj.weight": "model-00008-of-00013.safetensors",
|
| 368 |
+
"model.model.layers.45.input_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 369 |
+
"model.model.layers.45.mlp.down_proj.weight": "model-00008-of-00013.safetensors",
|
| 370 |
+
"model.model.layers.45.mlp.gate_proj.weight": "model-00008-of-00013.safetensors",
|
| 371 |
+
"model.model.layers.45.mlp.up_proj.weight": "model-00008-of-00013.safetensors",
|
| 372 |
+
"model.model.layers.45.post_attention_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 373 |
+
"model.model.layers.45.self_attn.k_proj.weight": "model-00008-of-00013.safetensors",
|
| 374 |
+
"model.model.layers.45.self_attn.o_proj.weight": "model-00008-of-00013.safetensors",
|
| 375 |
+
"model.model.layers.45.self_attn.q_proj.weight": "model-00008-of-00013.safetensors",
|
| 376 |
+
"model.model.layers.45.self_attn.v_proj.weight": "model-00008-of-00013.safetensors",
|
| 377 |
+
"model.model.layers.46.input_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 378 |
+
"model.model.layers.46.mlp.down_proj.weight": "model-00008-of-00013.safetensors",
|
| 379 |
+
"model.model.layers.46.mlp.gate_proj.weight": "model-00008-of-00013.safetensors",
|
| 380 |
+
"model.model.layers.46.mlp.up_proj.weight": "model-00008-of-00013.safetensors",
|
| 381 |
+
"model.model.layers.46.post_attention_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 382 |
+
"model.model.layers.46.self_attn.k_proj.weight": "model-00008-of-00013.safetensors",
|
| 383 |
+
"model.model.layers.46.self_attn.o_proj.weight": "model-00008-of-00013.safetensors",
|
| 384 |
+
"model.model.layers.46.self_attn.q_proj.weight": "model-00008-of-00013.safetensors",
|
| 385 |
+
"model.model.layers.46.self_attn.v_proj.weight": "model-00008-of-00013.safetensors",
|
| 386 |
+
"model.model.layers.47.input_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 387 |
+
"model.model.layers.47.mlp.down_proj.weight": "model-00008-of-00013.safetensors",
|
| 388 |
+
"model.model.layers.47.mlp.gate_proj.weight": "model-00008-of-00013.safetensors",
|
| 389 |
+
"model.model.layers.47.mlp.up_proj.weight": "model-00008-of-00013.safetensors",
|
| 390 |
+
"model.model.layers.47.post_attention_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 391 |
+
"model.model.layers.47.self_attn.k_proj.weight": "model-00008-of-00013.safetensors",
|
| 392 |
+
"model.model.layers.47.self_attn.o_proj.weight": "model-00008-of-00013.safetensors",
|
| 393 |
+
"model.model.layers.47.self_attn.q_proj.weight": "model-00008-of-00013.safetensors",
|
| 394 |
+
"model.model.layers.47.self_attn.v_proj.weight": "model-00008-of-00013.safetensors",
|
| 395 |
+
"model.model.layers.48.input_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 396 |
+
"model.model.layers.48.mlp.down_proj.weight": "model-00008-of-00013.safetensors",
|
| 397 |
+
"model.model.layers.48.mlp.gate_proj.weight": "model-00008-of-00013.safetensors",
|
| 398 |
+
"model.model.layers.48.mlp.up_proj.weight": "model-00008-of-00013.safetensors",
|
| 399 |
+
"model.model.layers.48.post_attention_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 400 |
+
"model.model.layers.48.self_attn.k_proj.weight": "model-00008-of-00013.safetensors",
|
| 401 |
+
"model.model.layers.48.self_attn.o_proj.weight": "model-00008-of-00013.safetensors",
|
| 402 |
+
"model.model.layers.48.self_attn.q_proj.weight": "model-00008-of-00013.safetensors",
|
| 403 |
+
"model.model.layers.48.self_attn.v_proj.weight": "model-00008-of-00013.safetensors",
|
| 404 |
+
"model.model.layers.49.input_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 405 |
+
"model.model.layers.49.mlp.down_proj.weight": "model-00008-of-00013.safetensors",
|
| 406 |
+
"model.model.layers.49.mlp.gate_proj.weight": "model-00008-of-00013.safetensors",
|
| 407 |
+
"model.model.layers.49.mlp.up_proj.weight": "model-00008-of-00013.safetensors",
|
| 408 |
+
"model.model.layers.49.post_attention_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 409 |
+
"model.model.layers.49.self_attn.k_proj.weight": "model-00008-of-00013.safetensors",
|
| 410 |
+
"model.model.layers.49.self_attn.o_proj.weight": "model-00008-of-00013.safetensors",
|
| 411 |
+
"model.model.layers.49.self_attn.q_proj.weight": "model-00008-of-00013.safetensors",
|
| 412 |
+
"model.model.layers.49.self_attn.v_proj.weight": "model-00008-of-00013.safetensors",
|
| 413 |
+
"model.model.layers.5.input_layernorm.weight": "model-00008-of-00013.safetensors",
|
| 414 |
+
"model.model.layers.5.mlp.down_proj.weight": "model-00009-of-00013.safetensors",
|
| 415 |
+
"model.model.layers.5.mlp.gate_proj.weight": "model-00009-of-00013.safetensors",
|
| 416 |
+
"model.model.layers.5.mlp.up_proj.weight": "model-00009-of-00013.safetensors",
|
| 417 |
+
"model.model.layers.5.post_attention_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 418 |
+
"model.model.layers.5.self_attn.k_proj.weight": "model-00009-of-00013.safetensors",
|
| 419 |
+
"model.model.layers.5.self_attn.o_proj.weight": "model-00009-of-00013.safetensors",
|
| 420 |
+
"model.model.layers.5.self_attn.q_proj.weight": "model-00009-of-00013.safetensors",
|
| 421 |
+
"model.model.layers.5.self_attn.v_proj.weight": "model-00009-of-00013.safetensors",
|
| 422 |
+
"model.model.layers.50.input_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 423 |
+
"model.model.layers.50.mlp.down_proj.weight": "model-00009-of-00013.safetensors",
|
| 424 |
+
"model.model.layers.50.mlp.gate_proj.weight": "model-00009-of-00013.safetensors",
|
| 425 |
+
"model.model.layers.50.mlp.up_proj.weight": "model-00009-of-00013.safetensors",
|
| 426 |
+
"model.model.layers.50.post_attention_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 427 |
+
"model.model.layers.50.self_attn.k_proj.weight": "model-00009-of-00013.safetensors",
|
| 428 |
+
"model.model.layers.50.self_attn.o_proj.weight": "model-00009-of-00013.safetensors",
|
| 429 |
+
"model.model.layers.50.self_attn.q_proj.weight": "model-00009-of-00013.safetensors",
|
| 430 |
+
"model.model.layers.50.self_attn.v_proj.weight": "model-00009-of-00013.safetensors",
|
| 431 |
+
"model.model.layers.51.input_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 432 |
+
"model.model.layers.51.mlp.down_proj.weight": "model-00009-of-00013.safetensors",
|
| 433 |
+
"model.model.layers.51.mlp.gate_proj.weight": "model-00009-of-00013.safetensors",
|
| 434 |
+
"model.model.layers.51.mlp.up_proj.weight": "model-00009-of-00013.safetensors",
|
| 435 |
+
"model.model.layers.51.post_attention_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 436 |
+
"model.model.layers.51.self_attn.k_proj.weight": "model-00009-of-00013.safetensors",
|
| 437 |
+
"model.model.layers.51.self_attn.o_proj.weight": "model-00009-of-00013.safetensors",
|
| 438 |
+
"model.model.layers.51.self_attn.q_proj.weight": "model-00009-of-00013.safetensors",
|
| 439 |
+
"model.model.layers.51.self_attn.v_proj.weight": "model-00009-of-00013.safetensors",
|
| 440 |
+
"model.model.layers.52.input_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 441 |
+
"model.model.layers.52.mlp.down_proj.weight": "model-00009-of-00013.safetensors",
|
| 442 |
+
"model.model.layers.52.mlp.gate_proj.weight": "model-00009-of-00013.safetensors",
|
| 443 |
+
"model.model.layers.52.mlp.up_proj.weight": "model-00009-of-00013.safetensors",
|
| 444 |
+
"model.model.layers.52.post_attention_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 445 |
+
"model.model.layers.52.self_attn.k_proj.weight": "model-00009-of-00013.safetensors",
|
| 446 |
+
"model.model.layers.52.self_attn.o_proj.weight": "model-00009-of-00013.safetensors",
|
| 447 |
+
"model.model.layers.52.self_attn.q_proj.weight": "model-00009-of-00013.safetensors",
|
| 448 |
+
"model.model.layers.52.self_attn.v_proj.weight": "model-00009-of-00013.safetensors",
|
| 449 |
+
"model.model.layers.53.input_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 450 |
+
"model.model.layers.53.mlp.down_proj.weight": "model-00009-of-00013.safetensors",
|
| 451 |
+
"model.model.layers.53.mlp.gate_proj.weight": "model-00009-of-00013.safetensors",
|
| 452 |
+
"model.model.layers.53.mlp.up_proj.weight": "model-00009-of-00013.safetensors",
|
| 453 |
+
"model.model.layers.53.post_attention_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 454 |
+
"model.model.layers.53.self_attn.k_proj.weight": "model-00009-of-00013.safetensors",
|
| 455 |
+
"model.model.layers.53.self_attn.o_proj.weight": "model-00009-of-00013.safetensors",
|
| 456 |
+
"model.model.layers.53.self_attn.q_proj.weight": "model-00009-of-00013.safetensors",
|
| 457 |
+
"model.model.layers.53.self_attn.v_proj.weight": "model-00009-of-00013.safetensors",
|
| 458 |
+
"model.model.layers.54.input_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 459 |
+
"model.model.layers.54.mlp.down_proj.weight": "model-00009-of-00013.safetensors",
|
| 460 |
+
"model.model.layers.54.mlp.gate_proj.weight": "model-00009-of-00013.safetensors",
|
| 461 |
+
"model.model.layers.54.mlp.up_proj.weight": "model-00009-of-00013.safetensors",
|
| 462 |
+
"model.model.layers.54.post_attention_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 463 |
+
"model.model.layers.54.self_attn.k_proj.weight": "model-00009-of-00013.safetensors",
|
| 464 |
+
"model.model.layers.54.self_attn.o_proj.weight": "model-00009-of-00013.safetensors",
|
| 465 |
+
"model.model.layers.54.self_attn.q_proj.weight": "model-00009-of-00013.safetensors",
|
| 466 |
+
"model.model.layers.54.self_attn.v_proj.weight": "model-00009-of-00013.safetensors",
|
| 467 |
+
"model.model.layers.55.input_layernorm.weight": "model-00009-of-00013.safetensors",
|
| 468 |
+
"model.model.layers.55.mlp.down_proj.weight": "model-00010-of-00013.safetensors",
|
| 469 |
+
"model.model.layers.55.mlp.gate_proj.weight": "model-00010-of-00013.safetensors",
|
| 470 |
+
"model.model.layers.55.mlp.up_proj.weight": "model-00010-of-00013.safetensors",
|
| 471 |
+
"model.model.layers.55.post_attention_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 472 |
+
"model.model.layers.55.self_attn.k_proj.weight": "model-00010-of-00013.safetensors",
|
| 473 |
+
"model.model.layers.55.self_attn.o_proj.weight": "model-00010-of-00013.safetensors",
|
| 474 |
+
"model.model.layers.55.self_attn.q_proj.weight": "model-00010-of-00013.safetensors",
|
| 475 |
+
"model.model.layers.55.self_attn.v_proj.weight": "model-00010-of-00013.safetensors",
|
| 476 |
+
"model.model.layers.56.input_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 477 |
+
"model.model.layers.56.mlp.down_proj.weight": "model-00010-of-00013.safetensors",
|
| 478 |
+
"model.model.layers.56.mlp.gate_proj.weight": "model-00010-of-00013.safetensors",
|
| 479 |
+
"model.model.layers.56.mlp.up_proj.weight": "model-00010-of-00013.safetensors",
|
| 480 |
+
"model.model.layers.56.post_attention_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 481 |
+
"model.model.layers.56.self_attn.k_proj.weight": "model-00010-of-00013.safetensors",
|
| 482 |
+
"model.model.layers.56.self_attn.o_proj.weight": "model-00010-of-00013.safetensors",
|
| 483 |
+
"model.model.layers.56.self_attn.q_proj.weight": "model-00010-of-00013.safetensors",
|
| 484 |
+
"model.model.layers.56.self_attn.v_proj.weight": "model-00010-of-00013.safetensors",
|
| 485 |
+
"model.model.layers.57.input_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 486 |
+
"model.model.layers.57.mlp.down_proj.weight": "model-00010-of-00013.safetensors",
|
| 487 |
+
"model.model.layers.57.mlp.gate_proj.weight": "model-00010-of-00013.safetensors",
|
| 488 |
+
"model.model.layers.57.mlp.up_proj.weight": "model-00010-of-00013.safetensors",
|
| 489 |
+
"model.model.layers.57.post_attention_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 490 |
+
"model.model.layers.57.self_attn.k_proj.weight": "model-00010-of-00013.safetensors",
|
| 491 |
+
"model.model.layers.57.self_attn.o_proj.weight": "model-00010-of-00013.safetensors",
|
| 492 |
+
"model.model.layers.57.self_attn.q_proj.weight": "model-00010-of-00013.safetensors",
|
| 493 |
+
"model.model.layers.57.self_attn.v_proj.weight": "model-00010-of-00013.safetensors",
|
| 494 |
+
"model.model.layers.58.input_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 495 |
+
"model.model.layers.58.mlp.down_proj.weight": "model-00010-of-00013.safetensors",
|
| 496 |
+
"model.model.layers.58.mlp.gate_proj.weight": "model-00010-of-00013.safetensors",
|
| 497 |
+
"model.model.layers.58.mlp.up_proj.weight": "model-00010-of-00013.safetensors",
|
| 498 |
+
"model.model.layers.58.post_attention_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 499 |
+
"model.model.layers.58.self_attn.k_proj.weight": "model-00010-of-00013.safetensors",
|
| 500 |
+
"model.model.layers.58.self_attn.o_proj.weight": "model-00010-of-00013.safetensors",
|
| 501 |
+
"model.model.layers.58.self_attn.q_proj.weight": "model-00010-of-00013.safetensors",
|
| 502 |
+
"model.model.layers.58.self_attn.v_proj.weight": "model-00010-of-00013.safetensors",
|
| 503 |
+
"model.model.layers.59.input_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 504 |
+
"model.model.layers.59.mlp.down_proj.weight": "model-00010-of-00013.safetensors",
|
| 505 |
+
"model.model.layers.59.mlp.gate_proj.weight": "model-00010-of-00013.safetensors",
|
| 506 |
+
"model.model.layers.59.mlp.up_proj.weight": "model-00010-of-00013.safetensors",
|
| 507 |
+
"model.model.layers.59.post_attention_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 508 |
+
"model.model.layers.59.self_attn.k_proj.weight": "model-00010-of-00013.safetensors",
|
| 509 |
+
"model.model.layers.59.self_attn.o_proj.weight": "model-00010-of-00013.safetensors",
|
| 510 |
+
"model.model.layers.59.self_attn.q_proj.weight": "model-00010-of-00013.safetensors",
|
| 511 |
+
"model.model.layers.59.self_attn.v_proj.weight": "model-00010-of-00013.safetensors",
|
| 512 |
+
"model.model.layers.6.input_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 513 |
+
"model.model.layers.6.mlp.down_proj.weight": "model-00010-of-00013.safetensors",
|
| 514 |
+
"model.model.layers.6.mlp.gate_proj.weight": "model-00010-of-00013.safetensors",
|
| 515 |
+
"model.model.layers.6.mlp.up_proj.weight": "model-00010-of-00013.safetensors",
|
| 516 |
+
"model.model.layers.6.post_attention_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 517 |
+
"model.model.layers.6.self_attn.k_proj.weight": "model-00010-of-00013.safetensors",
|
| 518 |
+
"model.model.layers.6.self_attn.o_proj.weight": "model-00010-of-00013.safetensors",
|
| 519 |
+
"model.model.layers.6.self_attn.q_proj.weight": "model-00010-of-00013.safetensors",
|
| 520 |
+
"model.model.layers.6.self_attn.v_proj.weight": "model-00010-of-00013.safetensors",
|
| 521 |
+
"model.model.layers.60.input_layernorm.weight": "model-00010-of-00013.safetensors",
|
| 522 |
+
"model.model.layers.60.mlp.down_proj.weight": "model-00011-of-00013.safetensors",
|
| 523 |
+
"model.model.layers.60.mlp.gate_proj.weight": "model-00011-of-00013.safetensors",
|
| 524 |
+
"model.model.layers.60.mlp.up_proj.weight": "model-00011-of-00013.safetensors",
|
| 525 |
+
"model.model.layers.60.post_attention_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 526 |
+
"model.model.layers.60.self_attn.k_proj.weight": "model-00011-of-00013.safetensors",
|
| 527 |
+
"model.model.layers.60.self_attn.o_proj.weight": "model-00011-of-00013.safetensors",
|
| 528 |
+
"model.model.layers.60.self_attn.q_proj.weight": "model-00011-of-00013.safetensors",
|
| 529 |
+
"model.model.layers.60.self_attn.v_proj.weight": "model-00011-of-00013.safetensors",
|
| 530 |
+
"model.model.layers.61.input_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 531 |
+
"model.model.layers.61.mlp.down_proj.weight": "model-00011-of-00013.safetensors",
|
| 532 |
+
"model.model.layers.61.mlp.gate_proj.weight": "model-00011-of-00013.safetensors",
|
| 533 |
+
"model.model.layers.61.mlp.up_proj.weight": "model-00011-of-00013.safetensors",
|
| 534 |
+
"model.model.layers.61.post_attention_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 535 |
+
"model.model.layers.61.self_attn.k_proj.weight": "model-00011-of-00013.safetensors",
|
| 536 |
+
"model.model.layers.61.self_attn.o_proj.weight": "model-00011-of-00013.safetensors",
|
| 537 |
+
"model.model.layers.61.self_attn.q_proj.weight": "model-00011-of-00013.safetensors",
|
| 538 |
+
"model.model.layers.61.self_attn.v_proj.weight": "model-00011-of-00013.safetensors",
|
| 539 |
+
"model.model.layers.62.input_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 540 |
+
"model.model.layers.62.mlp.down_proj.weight": "model-00011-of-00013.safetensors",
|
| 541 |
+
"model.model.layers.62.mlp.gate_proj.weight": "model-00011-of-00013.safetensors",
|
| 542 |
+
"model.model.layers.62.mlp.up_proj.weight": "model-00011-of-00013.safetensors",
|
| 543 |
+
"model.model.layers.62.post_attention_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 544 |
+
"model.model.layers.62.self_attn.k_proj.weight": "model-00011-of-00013.safetensors",
|
| 545 |
+
"model.model.layers.62.self_attn.o_proj.weight": "model-00011-of-00013.safetensors",
|
| 546 |
+
"model.model.layers.62.self_attn.q_proj.weight": "model-00011-of-00013.safetensors",
|
| 547 |
+
"model.model.layers.62.self_attn.v_proj.weight": "model-00011-of-00013.safetensors",
|
| 548 |
+
"model.model.layers.63.input_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 549 |
+
"model.model.layers.63.mlp.down_proj.weight": "model-00011-of-00013.safetensors",
|
| 550 |
+
"model.model.layers.63.mlp.gate_proj.weight": "model-00011-of-00013.safetensors",
|
| 551 |
+
"model.model.layers.63.mlp.up_proj.weight": "model-00011-of-00013.safetensors",
|
| 552 |
+
"model.model.layers.63.post_attention_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 553 |
+
"model.model.layers.63.self_attn.k_proj.weight": "model-00011-of-00013.safetensors",
|
| 554 |
+
"model.model.layers.63.self_attn.o_proj.weight": "model-00011-of-00013.safetensors",
|
| 555 |
+
"model.model.layers.63.self_attn.q_proj.weight": "model-00011-of-00013.safetensors",
|
| 556 |
+
"model.model.layers.63.self_attn.v_proj.weight": "model-00011-of-00013.safetensors",
|
| 557 |
+
"model.model.layers.64.input_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 558 |
+
"model.model.layers.64.mlp.down_proj.weight": "model-00011-of-00013.safetensors",
|
| 559 |
+
"model.model.layers.64.mlp.gate_proj.weight": "model-00011-of-00013.safetensors",
|
| 560 |
+
"model.model.layers.64.mlp.up_proj.weight": "model-00011-of-00013.safetensors",
|
| 561 |
+
"model.model.layers.64.post_attention_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 562 |
+
"model.model.layers.64.self_attn.k_proj.weight": "model-00011-of-00013.safetensors",
|
| 563 |
+
"model.model.layers.64.self_attn.o_proj.weight": "model-00011-of-00013.safetensors",
|
| 564 |
+
"model.model.layers.64.self_attn.q_proj.weight": "model-00011-of-00013.safetensors",
|
| 565 |
+
"model.model.layers.64.self_attn.v_proj.weight": "model-00011-of-00013.safetensors",
|
| 566 |
+
"model.model.layers.65.input_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 567 |
+
"model.model.layers.65.mlp.down_proj.weight": "model-00011-of-00013.safetensors",
|
| 568 |
+
"model.model.layers.65.mlp.gate_proj.weight": "model-00011-of-00013.safetensors",
|
| 569 |
+
"model.model.layers.65.mlp.up_proj.weight": "model-00011-of-00013.safetensors",
|
| 570 |
+
"model.model.layers.65.post_attention_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 571 |
+
"model.model.layers.65.self_attn.k_proj.weight": "model-00011-of-00013.safetensors",
|
| 572 |
+
"model.model.layers.65.self_attn.o_proj.weight": "model-00011-of-00013.safetensors",
|
| 573 |
+
"model.model.layers.65.self_attn.q_proj.weight": "model-00011-of-00013.safetensors",
|
| 574 |
+
"model.model.layers.65.self_attn.v_proj.weight": "model-00011-of-00013.safetensors",
|
| 575 |
+
"model.model.layers.66.input_layernorm.weight": "model-00011-of-00013.safetensors",
|
| 576 |
+
"model.model.layers.66.mlp.down_proj.weight": "model-00012-of-00013.safetensors",
|
| 577 |
+
"model.model.layers.66.mlp.gate_proj.weight": "model-00012-of-00013.safetensors",
|
| 578 |
+
"model.model.layers.66.mlp.up_proj.weight": "model-00012-of-00013.safetensors",
|
| 579 |
+
"model.model.layers.66.post_attention_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 580 |
+
"model.model.layers.66.self_attn.k_proj.weight": "model-00012-of-00013.safetensors",
|
| 581 |
+
"model.model.layers.66.self_attn.o_proj.weight": "model-00012-of-00013.safetensors",
|
| 582 |
+
"model.model.layers.66.self_attn.q_proj.weight": "model-00012-of-00013.safetensors",
|
| 583 |
+
"model.model.layers.66.self_attn.v_proj.weight": "model-00012-of-00013.safetensors",
|
| 584 |
+
"model.model.layers.67.input_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 585 |
+
"model.model.layers.67.mlp.down_proj.weight": "model-00012-of-00013.safetensors",
|
| 586 |
+
"model.model.layers.67.mlp.gate_proj.weight": "model-00012-of-00013.safetensors",
|
| 587 |
+
"model.model.layers.67.mlp.up_proj.weight": "model-00012-of-00013.safetensors",
|
| 588 |
+
"model.model.layers.67.post_attention_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 589 |
+
"model.model.layers.67.self_attn.k_proj.weight": "model-00012-of-00013.safetensors",
|
| 590 |
+
"model.model.layers.67.self_attn.o_proj.weight": "model-00012-of-00013.safetensors",
|
| 591 |
+
"model.model.layers.67.self_attn.q_proj.weight": "model-00012-of-00013.safetensors",
|
| 592 |
+
"model.model.layers.67.self_attn.v_proj.weight": "model-00012-of-00013.safetensors",
|
| 593 |
+
"model.model.layers.68.input_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 594 |
+
"model.model.layers.68.mlp.down_proj.weight": "model-00012-of-00013.safetensors",
|
| 595 |
+
"model.model.layers.68.mlp.gate_proj.weight": "model-00012-of-00013.safetensors",
|
| 596 |
+
"model.model.layers.68.mlp.up_proj.weight": "model-00012-of-00013.safetensors",
|
| 597 |
+
"model.model.layers.68.post_attention_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 598 |
+
"model.model.layers.68.self_attn.k_proj.weight": "model-00012-of-00013.safetensors",
|
| 599 |
+
"model.model.layers.68.self_attn.o_proj.weight": "model-00012-of-00013.safetensors",
|
| 600 |
+
"model.model.layers.68.self_attn.q_proj.weight": "model-00012-of-00013.safetensors",
|
| 601 |
+
"model.model.layers.68.self_attn.v_proj.weight": "model-00012-of-00013.safetensors",
|
| 602 |
+
"model.model.layers.69.input_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 603 |
+
"model.model.layers.69.mlp.down_proj.weight": "model-00012-of-00013.safetensors",
|
| 604 |
+
"model.model.layers.69.mlp.gate_proj.weight": "model-00012-of-00013.safetensors",
|
| 605 |
+
"model.model.layers.69.mlp.up_proj.weight": "model-00012-of-00013.safetensors",
|
| 606 |
+
"model.model.layers.69.post_attention_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 607 |
+
"model.model.layers.69.self_attn.k_proj.weight": "model-00012-of-00013.safetensors",
|
| 608 |
+
"model.model.layers.69.self_attn.o_proj.weight": "model-00012-of-00013.safetensors",
|
| 609 |
+
"model.model.layers.69.self_attn.q_proj.weight": "model-00012-of-00013.safetensors",
|
| 610 |
+
"model.model.layers.69.self_attn.v_proj.weight": "model-00012-of-00013.safetensors",
|
| 611 |
+
"model.model.layers.7.input_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 612 |
+
"model.model.layers.7.mlp.down_proj.weight": "model-00012-of-00013.safetensors",
|
| 613 |
+
"model.model.layers.7.mlp.gate_proj.weight": "model-00012-of-00013.safetensors",
|
| 614 |
+
"model.model.layers.7.mlp.up_proj.weight": "model-00012-of-00013.safetensors",
|
| 615 |
+
"model.model.layers.7.post_attention_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 616 |
+
"model.model.layers.7.self_attn.k_proj.weight": "model-00012-of-00013.safetensors",
|
| 617 |
+
"model.model.layers.7.self_attn.o_proj.weight": "model-00012-of-00013.safetensors",
|
| 618 |
+
"model.model.layers.7.self_attn.q_proj.weight": "model-00012-of-00013.safetensors",
|
| 619 |
+
"model.model.layers.7.self_attn.v_proj.weight": "model-00012-of-00013.safetensors",
|
| 620 |
+
"model.model.layers.70.input_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 621 |
+
"model.model.layers.70.mlp.down_proj.weight": "model-00012-of-00013.safetensors",
|
| 622 |
+
"model.model.layers.70.mlp.gate_proj.weight": "model-00012-of-00013.safetensors",
|
| 623 |
+
"model.model.layers.70.mlp.up_proj.weight": "model-00012-of-00013.safetensors",
|
| 624 |
+
"model.model.layers.70.post_attention_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 625 |
+
"model.model.layers.70.self_attn.k_proj.weight": "model-00012-of-00013.safetensors",
|
| 626 |
+
"model.model.layers.70.self_attn.o_proj.weight": "model-00012-of-00013.safetensors",
|
| 627 |
+
"model.model.layers.70.self_attn.q_proj.weight": "model-00012-of-00013.safetensors",
|
| 628 |
+
"model.model.layers.70.self_attn.v_proj.weight": "model-00012-of-00013.safetensors",
|
| 629 |
+
"model.model.layers.71.input_layernorm.weight": "model-00012-of-00013.safetensors",
|
| 630 |
+
"model.model.layers.71.mlp.down_proj.weight": "model-00013-of-00013.safetensors",
|
| 631 |
+
"model.model.layers.71.mlp.gate_proj.weight": "model-00013-of-00013.safetensors",
|
| 632 |
+
"model.model.layers.71.mlp.up_proj.weight": "model-00013-of-00013.safetensors",
|
| 633 |
+
"model.model.layers.71.post_attention_layernorm.weight": "model-00013-of-00013.safetensors",
|
| 634 |
+
"model.model.layers.71.self_attn.k_proj.weight": "model-00013-of-00013.safetensors",
|
| 635 |
+
"model.model.layers.71.self_attn.o_proj.weight": "model-00013-of-00013.safetensors",
|
| 636 |
+
"model.model.layers.71.self_attn.q_proj.weight": "model-00013-of-00013.safetensors",
|
| 637 |
+
"model.model.layers.71.self_attn.v_proj.weight": "model-00013-of-00013.safetensors",
|
| 638 |
+
"model.model.layers.8.input_layernorm.weight": "model-00013-of-00013.safetensors",
|
| 639 |
+
"model.model.layers.8.mlp.down_proj.weight": "model-00013-of-00013.safetensors",
|
| 640 |
+
"model.model.layers.8.mlp.gate_proj.weight": "model-00013-of-00013.safetensors",
|
| 641 |
+
"model.model.layers.8.mlp.up_proj.weight": "model-00013-of-00013.safetensors",
|
| 642 |
+
"model.model.layers.8.post_attention_layernorm.weight": "model-00013-of-00013.safetensors",
|
| 643 |
+
"model.model.layers.8.self_attn.k_proj.weight": "model-00013-of-00013.safetensors",
|
| 644 |
+
"model.model.layers.8.self_attn.o_proj.weight": "model-00013-of-00013.safetensors",
|
| 645 |
+
"model.model.layers.8.self_attn.q_proj.weight": "model-00013-of-00013.safetensors",
|
| 646 |
+
"model.model.layers.8.self_attn.v_proj.weight": "model-00013-of-00013.safetensors",
|
| 647 |
+
"model.model.layers.9.input_layernorm.weight": "model-00013-of-00013.safetensors",
|
| 648 |
+
"model.model.layers.9.mlp.down_proj.weight": "model-00013-of-00013.safetensors",
|
| 649 |
+
"model.model.layers.9.mlp.gate_proj.weight": "model-00013-of-00013.safetensors",
|
| 650 |
+
"model.model.layers.9.mlp.up_proj.weight": "model-00013-of-00013.safetensors",
|
| 651 |
+
"model.model.layers.9.post_attention_layernorm.weight": "model-00013-of-00013.safetensors",
|
| 652 |
+
"model.model.layers.9.self_attn.k_proj.weight": "model-00013-of-00013.safetensors",
|
| 653 |
+
"model.model.layers.9.self_attn.o_proj.weight": "model-00013-of-00013.safetensors",
|
| 654 |
+
"model.model.layers.9.self_attn.q_proj.weight": "model-00013-of-00013.safetensors",
|
| 655 |
+
"model.model.layers.9.self_attn.v_proj.weight": "model-00013-of-00013.safetensors",
|
| 656 |
+
"model.model.norm.weight": "model-00013-of-00013.safetensors"
|
| 657 |
+
}
|
| 658 |
+
}
|
modeling_hyperclovax.py
ADDED
|
@@ -0,0 +1,1866 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
| 5 |
+
# and OPT implementations in this library. It has been modified from its
|
| 6 |
+
# original forms to accommodate minor architectural differences compared
|
| 7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
| 8 |
+
#
|
| 9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 10 |
+
# you may not use this file except in compliance with the License.
|
| 11 |
+
# You may obtain a copy of the License at
|
| 12 |
+
#
|
| 13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 14 |
+
#
|
| 15 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 18 |
+
# See the License for the specific language governing permissions and
|
| 19 |
+
# limitations under the License.
|
| 20 |
+
import math
|
| 21 |
+
from typing import List, Optional, Tuple, Union
|
| 22 |
+
|
| 23 |
+
import torch
|
| 24 |
+
import torch.nn.functional as F
|
| 25 |
+
import torch.utils.checkpoint
|
| 26 |
+
from torch import nn
|
| 27 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
| 28 |
+
from transformers.activations import ACT2FN
|
| 29 |
+
from transformers.cache_utils import Cache, DynamicCache, StaticCache
|
| 30 |
+
from transformers.generation import GenerationMixin
|
| 31 |
+
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
|
| 32 |
+
from transformers.modeling_flash_attention_utils import _flash_attention_forward
|
| 33 |
+
from transformers.modeling_outputs import (
|
| 34 |
+
BaseModelOutputWithPast,
|
| 35 |
+
CausalLMOutputWithPast,
|
| 36 |
+
QuestionAnsweringModelOutput,
|
| 37 |
+
SequenceClassifierOutputWithPast,
|
| 38 |
+
TokenClassifierOutput,
|
| 39 |
+
)
|
| 40 |
+
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
|
| 41 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 42 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
|
| 43 |
+
from transformers.utils import (
|
| 44 |
+
add_start_docstrings,
|
| 45 |
+
add_start_docstrings_to_model_forward,
|
| 46 |
+
is_flash_attn_greater_or_equal_2_10,
|
| 47 |
+
is_torchdynamo_compiling,
|
| 48 |
+
logging,
|
| 49 |
+
replace_return_docstrings,
|
| 50 |
+
)
|
| 51 |
+
|
| 52 |
+
from .configuration_hyperclovax import HyperCLOVAXConfig
|
| 53 |
+
|
| 54 |
+
logger = logging.get_logger(__name__)
|
| 55 |
+
|
| 56 |
+
_CONFIG_FOR_DOC = "HyperCLOVAXConfig"
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
def _prepare_4d_causal_attention_mask_with_cache_position(
|
| 60 |
+
attention_mask: torch.Tensor,
|
| 61 |
+
sequence_length: int,
|
| 62 |
+
target_length: int,
|
| 63 |
+
dtype: torch.dtype,
|
| 64 |
+
device: torch.device,
|
| 65 |
+
min_dtype: float,
|
| 66 |
+
cache_position: torch.Tensor,
|
| 67 |
+
batch_size: int,
|
| 68 |
+
):
|
| 69 |
+
"""
|
| 70 |
+
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
| 71 |
+
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
| 72 |
+
|
| 73 |
+
Args:
|
| 74 |
+
attention_mask (`torch.Tensor`):
|
| 75 |
+
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
|
| 76 |
+
sequence_length (`int`):
|
| 77 |
+
The sequence length being processed.
|
| 78 |
+
target_length (`int`):
|
| 79 |
+
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
|
| 80 |
+
dtype (`torch.dtype`):
|
| 81 |
+
The dtype to use for the 4D attention mask.
|
| 82 |
+
device (`torch.device`):
|
| 83 |
+
The device to plcae the 4D attention mask on.
|
| 84 |
+
min_dtype (`float`):
|
| 85 |
+
The minimum value representable with the dtype `dtype`.
|
| 86 |
+
cache_position (`torch.Tensor`):
|
| 87 |
+
Indices depicting the position of the input sequence tokens in the sequence.
|
| 88 |
+
batch_size (`torch.Tensor`):
|
| 89 |
+
Batch size.
|
| 90 |
+
"""
|
| 91 |
+
if attention_mask is not None and attention_mask.dim() == 4:
|
| 92 |
+
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
| 93 |
+
causal_mask = attention_mask
|
| 94 |
+
else:
|
| 95 |
+
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
|
| 96 |
+
if sequence_length != 1:
|
| 97 |
+
causal_mask = torch.triu(causal_mask, diagonal=1)
|
| 98 |
+
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
|
| 99 |
+
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
| 100 |
+
if attention_mask is not None:
|
| 101 |
+
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
| 102 |
+
mask_length = attention_mask.shape[-1]
|
| 103 |
+
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
|
| 104 |
+
padding_mask = padding_mask == 0
|
| 105 |
+
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(padding_mask, min_dtype)
|
| 106 |
+
|
| 107 |
+
return causal_mask
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
class HyperCLOVAXRMSNorm(nn.Module):
|
| 111 |
+
def __init__(self, hidden_size, eps=1e-6):
|
| 112 |
+
"""
|
| 113 |
+
HyperCLOVAXRMSNorm is equivalent to T5LayerNorm
|
| 114 |
+
"""
|
| 115 |
+
super().__init__()
|
| 116 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
| 117 |
+
self.variance_epsilon = eps
|
| 118 |
+
|
| 119 |
+
def forward(self, hidden_states):
|
| 120 |
+
input_dtype = hidden_states.dtype
|
| 121 |
+
hidden_states = hidden_states.to(torch.float32)
|
| 122 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
| 123 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
| 124 |
+
return self.weight * hidden_states.to(input_dtype)
|
| 125 |
+
|
| 126 |
+
def extra_repr(self):
|
| 127 |
+
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
ALL_LAYERNORM_LAYERS.append(HyperCLOVAXRMSNorm)
|
| 131 |
+
|
| 132 |
+
|
| 133 |
+
class HyperCLOVAXRotaryEmbedding(nn.Module):
|
| 134 |
+
def __init__(
|
| 135 |
+
self,
|
| 136 |
+
dim=None,
|
| 137 |
+
max_position_embeddings=2048,
|
| 138 |
+
base=10000,
|
| 139 |
+
device=None,
|
| 140 |
+
scaling_factor=1.0,
|
| 141 |
+
rope_type="default",
|
| 142 |
+
config: Optional[HyperCLOVAXConfig] = None,
|
| 143 |
+
):
|
| 144 |
+
super().__init__()
|
| 145 |
+
# TODO (joao): remove the `if` below, only used for BC
|
| 146 |
+
self.rope_kwargs = {}
|
| 147 |
+
if config is None:
|
| 148 |
+
logger.warning_once(
|
| 149 |
+
"`HyperCLOVAXRotaryEmbedding` can now be fully parameterized by passing the model config through the "
|
| 150 |
+
"`config` argument. All other arguments will be removed in v4.46"
|
| 151 |
+
)
|
| 152 |
+
self.rope_kwargs = {
|
| 153 |
+
"rope_type": rope_type,
|
| 154 |
+
"factor": scaling_factor,
|
| 155 |
+
"dim": dim,
|
| 156 |
+
"base": base,
|
| 157 |
+
"max_position_embeddings": max_position_embeddings,
|
| 158 |
+
}
|
| 159 |
+
self.rope_type = rope_type
|
| 160 |
+
self.max_seq_len_cached = max_position_embeddings
|
| 161 |
+
self.original_max_seq_len = max_position_embeddings
|
| 162 |
+
else:
|
| 163 |
+
# BC: "rope_type" was originally "type"
|
| 164 |
+
if config.rope_scaling is not None:
|
| 165 |
+
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
| 166 |
+
else:
|
| 167 |
+
self.rope_type = "default"
|
| 168 |
+
self.max_seq_len_cached = config.max_position_embeddings
|
| 169 |
+
self.original_max_seq_len = config.max_position_embeddings
|
| 170 |
+
|
| 171 |
+
self.config = config
|
| 172 |
+
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
| 173 |
+
|
| 174 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
|
| 175 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 176 |
+
self.original_inv_freq = self.inv_freq
|
| 177 |
+
|
| 178 |
+
def _dynamic_frequency_update(self, position_ids, device):
|
| 179 |
+
"""
|
| 180 |
+
dynamic RoPE layers should recompute `inv_freq` in the following situations:
|
| 181 |
+
1 - growing beyond the cached sequence length (allow scaling)
|
| 182 |
+
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
|
| 183 |
+
"""
|
| 184 |
+
seq_len = torch.max(position_ids) + 1
|
| 185 |
+
if seq_len > self.max_seq_len_cached: # growth
|
| 186 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(
|
| 187 |
+
self.config, device, seq_len=seq_len, **self.rope_kwargs
|
| 188 |
+
)
|
| 189 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
|
| 190 |
+
self.max_seq_len_cached = seq_len
|
| 191 |
+
|
| 192 |
+
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
|
| 193 |
+
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
|
| 194 |
+
self.max_seq_len_cached = self.original_max_seq_len
|
| 195 |
+
|
| 196 |
+
@torch.no_grad()
|
| 197 |
+
def forward(self, x, position_ids):
|
| 198 |
+
if "dynamic" in self.rope_type:
|
| 199 |
+
self._dynamic_frequency_update(position_ids, device=x.device)
|
| 200 |
+
|
| 201 |
+
# Core RoPE block
|
| 202 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
| 203 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
| 204 |
+
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
|
| 205 |
+
device_type = x.device.type
|
| 206 |
+
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
| 207 |
+
with torch.autocast(device_type=device_type, enabled=False):
|
| 208 |
+
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
| 209 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 210 |
+
cos = emb.cos()
|
| 211 |
+
sin = emb.sin()
|
| 212 |
+
|
| 213 |
+
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
|
| 214 |
+
cos = cos * self.attention_scaling
|
| 215 |
+
sin = sin * self.attention_scaling
|
| 216 |
+
|
| 217 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
| 218 |
+
|
| 219 |
+
|
| 220 |
+
class HyperCLOVAXLinearScalingRotaryEmbedding(HyperCLOVAXRotaryEmbedding):
|
| 221 |
+
"""HyperCLOVAXRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
| 222 |
+
|
| 223 |
+
def __init__(self, *args, **kwargs):
|
| 224 |
+
logger.warning_once(
|
| 225 |
+
"`HyperCLOVAXLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use "
|
| 226 |
+
"`HyperCLOVAXRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)."
|
| 227 |
+
)
|
| 228 |
+
kwargs["rope_type"] = "linear"
|
| 229 |
+
super().__init__(*args, **kwargs)
|
| 230 |
+
|
| 231 |
+
|
| 232 |
+
class HyperCLOVAXDynamicNTKScalingRotaryEmbedding(HyperCLOVAXRotaryEmbedding):
|
| 233 |
+
"""HyperCLOVAXRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
| 234 |
+
|
| 235 |
+
def __init__(self, *args, **kwargs):
|
| 236 |
+
logger.warning_once(
|
| 237 |
+
"`HyperCLOVAXDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use "
|
| 238 |
+
"`HyperCLOVAXRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to "
|
| 239 |
+
"__init__)."
|
| 240 |
+
)
|
| 241 |
+
kwargs["rope_type"] = "dynamic"
|
| 242 |
+
super().__init__(*args, **kwargs)
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
def rotate_half(x):
|
| 246 |
+
"""Rotates half the hidden dims of the input."""
|
| 247 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 248 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 249 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 250 |
+
|
| 251 |
+
|
| 252 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
| 253 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
| 254 |
+
|
| 255 |
+
Args:
|
| 256 |
+
q (`torch.Tensor`): The query tensor.
|
| 257 |
+
k (`torch.Tensor`): The key tensor.
|
| 258 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
| 259 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
| 260 |
+
position_ids (`torch.Tensor`, *optional*):
|
| 261 |
+
Deprecated and unused.
|
| 262 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
| 263 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
| 264 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
| 265 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
| 266 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
| 267 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
| 268 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
| 269 |
+
Returns:
|
| 270 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
| 271 |
+
"""
|
| 272 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
| 273 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
| 274 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
| 275 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
| 276 |
+
return q_embed, k_embed
|
| 277 |
+
|
| 278 |
+
|
| 279 |
+
class HyperCLOVAXMLP(nn.Module):
|
| 280 |
+
def __init__(self, config):
|
| 281 |
+
super().__init__()
|
| 282 |
+
self.config = config
|
| 283 |
+
self.hidden_size = config.hidden_size
|
| 284 |
+
self.intermediate_size = config.intermediate_size
|
| 285 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
| 286 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
| 287 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
|
| 288 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
| 289 |
+
|
| 290 |
+
def forward(self, x):
|
| 291 |
+
if self.config.pretraining_tp > 1:
|
| 292 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
| 293 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
| 294 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
| 295 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
| 296 |
+
|
| 297 |
+
gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
| 298 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
| 299 |
+
|
| 300 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
| 301 |
+
down_proj = [
|
| 302 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
| 303 |
+
]
|
| 304 |
+
down_proj = sum(down_proj)
|
| 305 |
+
else:
|
| 306 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
| 307 |
+
|
| 308 |
+
return down_proj
|
| 309 |
+
|
| 310 |
+
|
| 311 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 312 |
+
"""
|
| 313 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 314 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 315 |
+
"""
|
| 316 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 317 |
+
if n_rep == 1:
|
| 318 |
+
return hidden_states
|
| 319 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
| 320 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 321 |
+
|
| 322 |
+
|
| 323 |
+
class HyperCLOVAXAttention(nn.Module):
|
| 324 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 325 |
+
|
| 326 |
+
def __init__(self, config: HyperCLOVAXConfig, layer_idx: Optional[int] = None):
|
| 327 |
+
super().__init__()
|
| 328 |
+
self.config = config
|
| 329 |
+
self.layer_idx = layer_idx
|
| 330 |
+
if layer_idx is None:
|
| 331 |
+
logger.warning_once(
|
| 332 |
+
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
| 333 |
+
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
| 334 |
+
"when creating this class."
|
| 335 |
+
)
|
| 336 |
+
|
| 337 |
+
self.attention_dropout = config.attention_dropout
|
| 338 |
+
self.hidden_size = config.hidden_size
|
| 339 |
+
self.num_heads = config.num_attention_heads
|
| 340 |
+
self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
|
| 341 |
+
self.num_key_value_heads = config.num_key_value_heads
|
| 342 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
| 343 |
+
self.max_position_embeddings = config.max_position_embeddings
|
| 344 |
+
self.rope_theta = config.rope_theta
|
| 345 |
+
self.is_causal = True
|
| 346 |
+
|
| 347 |
+
self.scaling = config.attention_multiplier
|
| 348 |
+
|
| 349 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
| 350 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
| 351 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
| 352 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
|
| 353 |
+
|
| 354 |
+
# TODO (joao): remove in v4.46 (RoPE is computed in the model, not in the decoder layers)
|
| 355 |
+
self.rotary_emb = HyperCLOVAXRotaryEmbedding(config=self.config)
|
| 356 |
+
|
| 357 |
+
def forward(
|
| 358 |
+
self,
|
| 359 |
+
hidden_states: torch.Tensor,
|
| 360 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 361 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 362 |
+
past_key_value: Optional[Cache] = None,
|
| 363 |
+
output_attentions: bool = False,
|
| 364 |
+
use_cache: bool = False,
|
| 365 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 366 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
|
| 367 |
+
**kwargs,
|
| 368 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 369 |
+
bsz, q_len, _ = hidden_states.size()
|
| 370 |
+
|
| 371 |
+
if self.config.pretraining_tp > 1:
|
| 372 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
| 373 |
+
query_slices = self.q_proj.weight.split(
|
| 374 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
| 375 |
+
)
|
| 376 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
| 377 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
| 378 |
+
|
| 379 |
+
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 380 |
+
query_states = torch.cat(query_states, dim=-1)
|
| 381 |
+
|
| 382 |
+
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 383 |
+
key_states = torch.cat(key_states, dim=-1)
|
| 384 |
+
|
| 385 |
+
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 386 |
+
value_states = torch.cat(value_states, dim=-1)
|
| 387 |
+
|
| 388 |
+
else:
|
| 389 |
+
query_states = self.q_proj(hidden_states)
|
| 390 |
+
key_states = self.k_proj(hidden_states)
|
| 391 |
+
value_states = self.v_proj(hidden_states)
|
| 392 |
+
|
| 393 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
| 394 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 395 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 396 |
+
|
| 397 |
+
if position_embeddings is None:
|
| 398 |
+
logger.warning_once(
|
| 399 |
+
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
|
| 400 |
+
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
|
| 401 |
+
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
|
| 402 |
+
"removed and `position_embeddings` will be mandatory."
|
| 403 |
+
)
|
| 404 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
| 405 |
+
else:
|
| 406 |
+
cos, sin = position_embeddings
|
| 407 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
| 408 |
+
|
| 409 |
+
if past_key_value is not None:
|
| 410 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
| 411 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
| 412 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 413 |
+
|
| 414 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
| 415 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
| 416 |
+
# attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling / math.sqrt(self.head_dim)
|
| 417 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
|
| 418 |
+
|
| 419 |
+
if attention_mask is not None: # no matter the length, we just slice it
|
| 420 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
| 421 |
+
attn_weights = attn_weights + causal_mask
|
| 422 |
+
|
| 423 |
+
# upcast attention to fp32
|
| 424 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
| 425 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
| 426 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 427 |
+
|
| 428 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
| 429 |
+
raise ValueError(
|
| 430 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
| 431 |
+
f" {attn_output.size()}"
|
| 432 |
+
)
|
| 433 |
+
|
| 434 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 435 |
+
|
| 436 |
+
attn_output = attn_output.reshape(bsz, q_len, -1)
|
| 437 |
+
|
| 438 |
+
if self.config.pretraining_tp > 1:
|
| 439 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
| 440 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
| 441 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
| 442 |
+
else:
|
| 443 |
+
attn_output = self.o_proj(attn_output)
|
| 444 |
+
|
| 445 |
+
if not output_attentions:
|
| 446 |
+
attn_weights = None
|
| 447 |
+
|
| 448 |
+
return attn_output, attn_weights, past_key_value
|
| 449 |
+
|
| 450 |
+
|
| 451 |
+
class HyperCLOVAXFlashAttention2(HyperCLOVAXAttention):
|
| 452 |
+
"""
|
| 453 |
+
HyperCLOVAX flash attention module. This module inherits from `HyperCLOVAXAttention` as the weights of the module stays
|
| 454 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
| 455 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
| 456 |
+
"""
|
| 457 |
+
|
| 458 |
+
def __init__(self, *args, **kwargs):
|
| 459 |
+
super().__init__(*args, **kwargs)
|
| 460 |
+
|
| 461 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
| 462 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
| 463 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
| 464 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
| 465 |
+
|
| 466 |
+
def forward(
|
| 467 |
+
self,
|
| 468 |
+
hidden_states: torch.Tensor,
|
| 469 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
| 470 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 471 |
+
past_key_value: Optional[Cache] = None,
|
| 472 |
+
output_attentions: bool = False,
|
| 473 |
+
use_cache: bool = False,
|
| 474 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 475 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
|
| 476 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 477 |
+
if isinstance(past_key_value, StaticCache):
|
| 478 |
+
raise ValueError(
|
| 479 |
+
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
|
| 480 |
+
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
|
| 481 |
+
)
|
| 482 |
+
|
| 483 |
+
output_attentions = False
|
| 484 |
+
|
| 485 |
+
bsz, q_len, _ = hidden_states.size()
|
| 486 |
+
|
| 487 |
+
query_states = self.q_proj(hidden_states)
|
| 488 |
+
key_states = self.k_proj(hidden_states)
|
| 489 |
+
value_states = self.v_proj(hidden_states)
|
| 490 |
+
|
| 491 |
+
# Flash attention requires the input to have the shape
|
| 492 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
| 493 |
+
# therefore we just need to keep the original shape
|
| 494 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
| 495 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 496 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 497 |
+
|
| 498 |
+
if position_embeddings is None:
|
| 499 |
+
logger.warning_once(
|
| 500 |
+
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
|
| 501 |
+
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
|
| 502 |
+
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
|
| 503 |
+
"removed and `position_embeddings` will be mandatory."
|
| 504 |
+
)
|
| 505 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
| 506 |
+
else:
|
| 507 |
+
cos, sin = position_embeddings
|
| 508 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
| 509 |
+
|
| 510 |
+
if past_key_value is not None:
|
| 511 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
| 512 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
| 513 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 514 |
+
|
| 515 |
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
| 516 |
+
# to be able to avoid many of these transpose/reshape/view.
|
| 517 |
+
query_states = query_states.transpose(1, 2)
|
| 518 |
+
key_states = key_states.transpose(1, 2)
|
| 519 |
+
value_states = value_states.transpose(1, 2)
|
| 520 |
+
|
| 521 |
+
dropout_rate = self.attention_dropout if self.training else 0.0
|
| 522 |
+
|
| 523 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
| 524 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
| 525 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
| 526 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
| 527 |
+
# in fp32. (HyperCLOVAXRMSNorm handles it correctly)
|
| 528 |
+
|
| 529 |
+
input_dtype = query_states.dtype
|
| 530 |
+
if input_dtype == torch.float32:
|
| 531 |
+
if torch.is_autocast_enabled():
|
| 532 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
| 533 |
+
# Handle the case where the model is quantized
|
| 534 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
| 535 |
+
target_dtype = self.config._pre_quantization_dtype
|
| 536 |
+
else:
|
| 537 |
+
target_dtype = self.q_proj.weight.dtype
|
| 538 |
+
|
| 539 |
+
logger.warning_once(
|
| 540 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
| 541 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
| 542 |
+
f" {target_dtype}."
|
| 543 |
+
)
|
| 544 |
+
|
| 545 |
+
query_states = query_states.to(target_dtype)
|
| 546 |
+
key_states = key_states.to(target_dtype)
|
| 547 |
+
value_states = value_states.to(target_dtype)
|
| 548 |
+
|
| 549 |
+
attn_output = _flash_attention_forward(
|
| 550 |
+
query_states,
|
| 551 |
+
key_states,
|
| 552 |
+
value_states,
|
| 553 |
+
attention_mask,
|
| 554 |
+
q_len,
|
| 555 |
+
position_ids=position_ids,
|
| 556 |
+
dropout=dropout_rate,
|
| 557 |
+
softmax_scale=self.scaling, # mup
|
| 558 |
+
sliding_window=getattr(self, "sliding_window", None),
|
| 559 |
+
use_top_left_mask=self._flash_attn_uses_top_left_mask,
|
| 560 |
+
is_causal=self.is_causal,
|
| 561 |
+
)
|
| 562 |
+
|
| 563 |
+
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
|
| 564 |
+
attn_output = self.o_proj(attn_output)
|
| 565 |
+
|
| 566 |
+
if not output_attentions:
|
| 567 |
+
attn_weights = None
|
| 568 |
+
|
| 569 |
+
return attn_output, attn_weights, past_key_value
|
| 570 |
+
|
| 571 |
+
|
| 572 |
+
class HyperCLOVAXSdpaAttention(HyperCLOVAXAttention):
|
| 573 |
+
"""
|
| 574 |
+
HyperCLOVAX attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
| 575 |
+
`HyperCLOVAXAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
| 576 |
+
SDPA API.
|
| 577 |
+
"""
|
| 578 |
+
|
| 579 |
+
# Adapted from HyperCLOVAXAttention.forward
|
| 580 |
+
def forward(
|
| 581 |
+
self,
|
| 582 |
+
hidden_states: torch.Tensor,
|
| 583 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 584 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 585 |
+
past_key_value: Optional[Cache] = None,
|
| 586 |
+
output_attentions: bool = False,
|
| 587 |
+
use_cache: bool = False,
|
| 588 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 589 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
|
| 590 |
+
**kwargs,
|
| 591 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 592 |
+
if output_attentions:
|
| 593 |
+
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
| 594 |
+
logger.warning_once(
|
| 595 |
+
"HyperCLOVAXModel is using HyperCLOVAXSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
| 596 |
+
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
| 597 |
+
)
|
| 598 |
+
return super().forward(
|
| 599 |
+
hidden_states=hidden_states,
|
| 600 |
+
attention_mask=attention_mask,
|
| 601 |
+
position_ids=position_ids,
|
| 602 |
+
past_key_value=past_key_value,
|
| 603 |
+
output_attentions=output_attentions,
|
| 604 |
+
use_cache=use_cache,
|
| 605 |
+
cache_position=cache_position,
|
| 606 |
+
position_embeddings=position_embeddings,
|
| 607 |
+
)
|
| 608 |
+
|
| 609 |
+
bsz, q_len, _ = hidden_states.size()
|
| 610 |
+
|
| 611 |
+
query_states = self.q_proj(hidden_states)
|
| 612 |
+
key_states = self.k_proj(hidden_states)
|
| 613 |
+
value_states = self.v_proj(hidden_states)
|
| 614 |
+
|
| 615 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
| 616 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 617 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 618 |
+
|
| 619 |
+
if position_embeddings is None:
|
| 620 |
+
logger.warning_once(
|
| 621 |
+
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
|
| 622 |
+
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
|
| 623 |
+
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
|
| 624 |
+
"removed and `position_embeddings` will be mandatory."
|
| 625 |
+
)
|
| 626 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
| 627 |
+
else:
|
| 628 |
+
cos, sin = position_embeddings
|
| 629 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
| 630 |
+
|
| 631 |
+
if past_key_value is not None:
|
| 632 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
| 633 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
| 634 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 635 |
+
|
| 636 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
| 637 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
| 638 |
+
|
| 639 |
+
causal_mask = attention_mask
|
| 640 |
+
if attention_mask is not None:
|
| 641 |
+
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
| 642 |
+
|
| 643 |
+
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
| 644 |
+
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
| 645 |
+
if query_states.device.type == "cuda" and causal_mask is not None:
|
| 646 |
+
query_states = query_states.contiguous()
|
| 647 |
+
key_states = key_states.contiguous()
|
| 648 |
+
value_states = value_states.contiguous()
|
| 649 |
+
|
| 650 |
+
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
| 651 |
+
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
| 652 |
+
is_causal = True if causal_mask is None and q_len > 1 else False
|
| 653 |
+
|
| 654 |
+
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
| 655 |
+
query_states,
|
| 656 |
+
key_states,
|
| 657 |
+
value_states,
|
| 658 |
+
attn_mask=causal_mask,
|
| 659 |
+
dropout_p=self.attention_dropout if self.training else 0.0,
|
| 660 |
+
is_causal=is_causal,
|
| 661 |
+
scale=self.scaling, # mup
|
| 662 |
+
)
|
| 663 |
+
|
| 664 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 665 |
+
attn_output = attn_output.view(bsz, q_len, -1)
|
| 666 |
+
|
| 667 |
+
attn_output = self.o_proj(attn_output)
|
| 668 |
+
|
| 669 |
+
return attn_output, None, past_key_value
|
| 670 |
+
|
| 671 |
+
|
| 672 |
+
HyperCLOVAX_ATTENTION_CLASSES = {
|
| 673 |
+
"eager": HyperCLOVAXAttention,
|
| 674 |
+
"flash_attention_2": HyperCLOVAXFlashAttention2,
|
| 675 |
+
"sdpa": HyperCLOVAXSdpaAttention,
|
| 676 |
+
}
|
| 677 |
+
|
| 678 |
+
|
| 679 |
+
class HyperCLOVAXDecoderLayer(nn.Module):
|
| 680 |
+
def __init__(self, config: HyperCLOVAXConfig, layer_idx: int):
|
| 681 |
+
super().__init__()
|
| 682 |
+
self.hidden_size = config.hidden_size
|
| 683 |
+
|
| 684 |
+
self.self_attn = HyperCLOVAX_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
| 685 |
+
|
| 686 |
+
self.mlp = HyperCLOVAXMLP(config)
|
| 687 |
+
self.input_layernorm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 688 |
+
self.post_attention_layernorm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 689 |
+
|
| 690 |
+
# post-norm (dual-norm)
|
| 691 |
+
self.use_post_norm = config.use_post_norm
|
| 692 |
+
if self.use_post_norm:
|
| 693 |
+
self.post_norm1 = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 694 |
+
self.post_norm2 = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 695 |
+
|
| 696 |
+
self.residual_multiplier = config.residual_multiplier # mup
|
| 697 |
+
|
| 698 |
+
def forward(
|
| 699 |
+
self,
|
| 700 |
+
hidden_states: torch.Tensor,
|
| 701 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 702 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 703 |
+
past_key_value: Optional[Cache] = None,
|
| 704 |
+
output_attentions: Optional[bool] = False,
|
| 705 |
+
use_cache: Optional[bool] = False,
|
| 706 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 707 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
|
| 708 |
+
**kwargs,
|
| 709 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
| 710 |
+
"""
|
| 711 |
+
Args:
|
| 712 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
| 713 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
| 714 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
| 715 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
| 716 |
+
output_attentions (`bool`, *optional*):
|
| 717 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
| 718 |
+
returned tensors for more detail.
|
| 719 |
+
use_cache (`bool`, *optional*):
|
| 720 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
| 721 |
+
(see `past_key_values`).
|
| 722 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
| 723 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
| 724 |
+
Indices depicting the position of the input sequence tokens in the sequence
|
| 725 |
+
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
| 726 |
+
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
| 727 |
+
with `head_dim` being the embedding dimension of each attention head.
|
| 728 |
+
kwargs (`dict`, *optional*):
|
| 729 |
+
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
| 730 |
+
into the model
|
| 731 |
+
"""
|
| 732 |
+
residual = hidden_states
|
| 733 |
+
|
| 734 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 735 |
+
|
| 736 |
+
# Self Attention
|
| 737 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
| 738 |
+
hidden_states=hidden_states,
|
| 739 |
+
attention_mask=attention_mask,
|
| 740 |
+
position_ids=position_ids,
|
| 741 |
+
past_key_value=past_key_value,
|
| 742 |
+
output_attentions=output_attentions,
|
| 743 |
+
use_cache=use_cache,
|
| 744 |
+
cache_position=cache_position,
|
| 745 |
+
position_embeddings=position_embeddings,
|
| 746 |
+
**kwargs,
|
| 747 |
+
)
|
| 748 |
+
|
| 749 |
+
if self.use_post_norm:
|
| 750 |
+
hidden_states = self.post_norm1(hidden_states)
|
| 751 |
+
|
| 752 |
+
hidden_states = residual + hidden_states * self.residual_multiplier # mup
|
| 753 |
+
|
| 754 |
+
# Fully Connected
|
| 755 |
+
residual = hidden_states
|
| 756 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
| 757 |
+
hidden_states = self.mlp(hidden_states)
|
| 758 |
+
|
| 759 |
+
if self.use_post_norm:
|
| 760 |
+
hidden_states = self.post_norm2(hidden_states)
|
| 761 |
+
|
| 762 |
+
hidden_states = residual + hidden_states * self.residual_multiplier # mup
|
| 763 |
+
|
| 764 |
+
outputs = (hidden_states,)
|
| 765 |
+
|
| 766 |
+
if output_attentions:
|
| 767 |
+
outputs += (self_attn_weights,)
|
| 768 |
+
|
| 769 |
+
if use_cache:
|
| 770 |
+
outputs += (present_key_value,)
|
| 771 |
+
|
| 772 |
+
return outputs
|
| 773 |
+
|
| 774 |
+
|
| 775 |
+
HyperCLOVAX_START_DOCSTRING = r"""
|
| 776 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
| 777 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
| 778 |
+
etc.)
|
| 779 |
+
|
| 780 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
| 781 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
| 782 |
+
and behavior.
|
| 783 |
+
|
| 784 |
+
Parameters:
|
| 785 |
+
config ([`HyperCLOVAXConfig`]):
|
| 786 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
| 787 |
+
load the weights associated with the model, only the configuration. Check out the
|
| 788 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
| 789 |
+
"""
|
| 790 |
+
|
| 791 |
+
|
| 792 |
+
@add_start_docstrings(
|
| 793 |
+
"The bare HyperCLOVAX Model outputting raw hidden-states without any specific head on top.",
|
| 794 |
+
HyperCLOVAX_START_DOCSTRING,
|
| 795 |
+
)
|
| 796 |
+
class HyperCLOVAXPreTrainedModel(PreTrainedModel):
|
| 797 |
+
config_class = HyperCLOVAXConfig
|
| 798 |
+
base_model_prefix = "model"
|
| 799 |
+
supports_gradient_checkpointing = True
|
| 800 |
+
_no_split_modules = ["HyperCLOVAXDecoderLayer"]
|
| 801 |
+
_skip_keys_device_placement = ["past_key_values"]
|
| 802 |
+
_supports_flash_attn_2 = True
|
| 803 |
+
_supports_sdpa = True
|
| 804 |
+
_supports_cache_class = True
|
| 805 |
+
_supports_quantized_cache = True
|
| 806 |
+
_supports_static_cache = True
|
| 807 |
+
|
| 808 |
+
def _init_weights(self, module):
|
| 809 |
+
std = self.config.initializer_range
|
| 810 |
+
if isinstance(module, nn.Linear):
|
| 811 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 812 |
+
if module.bias is not None:
|
| 813 |
+
module.bias.data.zero_()
|
| 814 |
+
elif isinstance(module, nn.Embedding):
|
| 815 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 816 |
+
if module.padding_idx is not None:
|
| 817 |
+
module.weight.data[module.padding_idx].zero_()
|
| 818 |
+
|
| 819 |
+
|
| 820 |
+
HyperCLOVAX_INPUTS_DOCSTRING = r"""
|
| 821 |
+
Args:
|
| 822 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
| 823 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
| 824 |
+
it.
|
| 825 |
+
|
| 826 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 827 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 828 |
+
|
| 829 |
+
[What are input IDs?](../glossary#input-ids)
|
| 830 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 831 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
| 832 |
+
|
| 833 |
+
- 1 for tokens that are **not masked**,
|
| 834 |
+
- 0 for tokens that are **masked**.
|
| 835 |
+
|
| 836 |
+
[What are attention masks?](../glossary#attention-mask)
|
| 837 |
+
|
| 838 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 839 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 840 |
+
|
| 841 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
| 842 |
+
`past_key_values`).
|
| 843 |
+
|
| 844 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
| 845 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
| 846 |
+
information on the default strategy.
|
| 847 |
+
|
| 848 |
+
- 1 indicates the head is **not masked**,
|
| 849 |
+
- 0 indicates the head is **masked**.
|
| 850 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 851 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
| 852 |
+
config.n_positions - 1]`.
|
| 853 |
+
|
| 854 |
+
[What are position IDs?](../glossary#position-ids)
|
| 855 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
| 856 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
| 857 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
| 858 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
| 859 |
+
|
| 860 |
+
Two formats are allowed:
|
| 861 |
+
- a [`~cache_utils.Cache`] instance, see our
|
| 862 |
+
[kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
|
| 863 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
| 864 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
| 865 |
+
cache format.
|
| 866 |
+
|
| 867 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
| 868 |
+
legacy cache format will be returned.
|
| 869 |
+
|
| 870 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
| 871 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
| 872 |
+
of shape `(batch_size, sequence_length)`.
|
| 873 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
| 874 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
| 875 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
| 876 |
+
model's internal embedding lookup matrix.
|
| 877 |
+
use_cache (`bool`, *optional*):
|
| 878 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
| 879 |
+
`past_key_values`).
|
| 880 |
+
output_attentions (`bool`, *optional*):
|
| 881 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
| 882 |
+
tensors for more detail.
|
| 883 |
+
output_hidden_states (`bool`, *optional*):
|
| 884 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
| 885 |
+
more detail.
|
| 886 |
+
return_dict (`bool`, *optional*):
|
| 887 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
| 888 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
| 889 |
+
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
| 890 |
+
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
| 891 |
+
the complete sequence length.
|
| 892 |
+
"""
|
| 893 |
+
|
| 894 |
+
|
| 895 |
+
@add_start_docstrings(
|
| 896 |
+
"The bare HyperCLOVAX Model outputting raw hidden-states without any specific head on top.",
|
| 897 |
+
HyperCLOVAX_START_DOCSTRING,
|
| 898 |
+
)
|
| 899 |
+
class HyperCLOVAXModel(HyperCLOVAXPreTrainedModel):
|
| 900 |
+
"""
|
| 901 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HyperCLOVAXDecoderLayer`]
|
| 902 |
+
|
| 903 |
+
Args:
|
| 904 |
+
config: HyperCLOVAXConfig
|
| 905 |
+
"""
|
| 906 |
+
|
| 907 |
+
def __init__(self, config: HyperCLOVAXConfig):
|
| 908 |
+
super().__init__(config)
|
| 909 |
+
self.padding_idx = config.pad_token_id
|
| 910 |
+
self.vocab_size = config.vocab_size
|
| 911 |
+
|
| 912 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
| 913 |
+
self.layers = nn.ModuleList(
|
| 914 |
+
[HyperCLOVAXDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
| 915 |
+
)
|
| 916 |
+
self.norm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 917 |
+
self.rotary_emb = HyperCLOVAXRotaryEmbedding(config=config)
|
| 918 |
+
self.gradient_checkpointing = False
|
| 919 |
+
|
| 920 |
+
# Initialize weights and apply final processing
|
| 921 |
+
self.post_init()
|
| 922 |
+
|
| 923 |
+
# mup
|
| 924 |
+
self.embedding_multiplier = config.embedding_multiplier
|
| 925 |
+
|
| 926 |
+
def get_input_embeddings(self):
|
| 927 |
+
return self.embed_tokens
|
| 928 |
+
|
| 929 |
+
def set_input_embeddings(self, value):
|
| 930 |
+
self.embed_tokens = value
|
| 931 |
+
|
| 932 |
+
@add_start_docstrings_to_model_forward(HyperCLOVAX_INPUTS_DOCSTRING)
|
| 933 |
+
def forward(
|
| 934 |
+
self,
|
| 935 |
+
input_ids: torch.LongTensor = None,
|
| 936 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 937 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 938 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
| 939 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 940 |
+
use_cache: Optional[bool] = None,
|
| 941 |
+
output_attentions: Optional[bool] = None,
|
| 942 |
+
output_hidden_states: Optional[bool] = None,
|
| 943 |
+
return_dict: Optional[bool] = None,
|
| 944 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 945 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
| 946 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 947 |
+
output_hidden_states = (
|
| 948 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 949 |
+
)
|
| 950 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 951 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 952 |
+
|
| 953 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
| 954 |
+
raise ValueError(
|
| 955 |
+
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
| 956 |
+
)
|
| 957 |
+
|
| 958 |
+
if self.gradient_checkpointing and self.training and use_cache:
|
| 959 |
+
logger.warning_once(
|
| 960 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
| 961 |
+
)
|
| 962 |
+
use_cache = False
|
| 963 |
+
|
| 964 |
+
if inputs_embeds is None:
|
| 965 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 966 |
+
|
| 967 |
+
inputs_embeds = inputs_embeds * self.embedding_multiplier # mup
|
| 968 |
+
|
| 969 |
+
# kept for BC (non `Cache` `past_key_values` inputs)
|
| 970 |
+
return_legacy_cache = False
|
| 971 |
+
if use_cache and not isinstance(past_key_values, Cache):
|
| 972 |
+
return_legacy_cache = True
|
| 973 |
+
if past_key_values is None:
|
| 974 |
+
past_key_values = DynamicCache()
|
| 975 |
+
else:
|
| 976 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
| 977 |
+
logger.warning_once(
|
| 978 |
+
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
|
| 979 |
+
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
|
| 980 |
+
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
|
| 981 |
+
)
|
| 982 |
+
|
| 983 |
+
if cache_position is None:
|
| 984 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
| 985 |
+
cache_position = torch.arange(
|
| 986 |
+
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
| 987 |
+
)
|
| 988 |
+
if position_ids is None:
|
| 989 |
+
position_ids = cache_position.unsqueeze(0)
|
| 990 |
+
|
| 991 |
+
causal_mask = self._update_causal_mask(
|
| 992 |
+
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
| 993 |
+
)
|
| 994 |
+
hidden_states = inputs_embeds
|
| 995 |
+
|
| 996 |
+
# create position embeddings to be shared across the decoder layers
|
| 997 |
+
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
| 998 |
+
|
| 999 |
+
# decoder layers
|
| 1000 |
+
all_hidden_states = () if output_hidden_states else None
|
| 1001 |
+
all_self_attns = () if output_attentions else None
|
| 1002 |
+
next_decoder_cache = None
|
| 1003 |
+
|
| 1004 |
+
for decoder_layer in self.layers:
|
| 1005 |
+
if output_hidden_states:
|
| 1006 |
+
all_hidden_states += (hidden_states,)
|
| 1007 |
+
|
| 1008 |
+
if self.gradient_checkpointing and self.training:
|
| 1009 |
+
layer_outputs = self._gradient_checkpointing_func(
|
| 1010 |
+
decoder_layer.__call__,
|
| 1011 |
+
hidden_states,
|
| 1012 |
+
causal_mask,
|
| 1013 |
+
position_ids,
|
| 1014 |
+
past_key_values,
|
| 1015 |
+
output_attentions,
|
| 1016 |
+
use_cache,
|
| 1017 |
+
cache_position,
|
| 1018 |
+
position_embeddings,
|
| 1019 |
+
)
|
| 1020 |
+
else:
|
| 1021 |
+
layer_outputs = decoder_layer(
|
| 1022 |
+
hidden_states,
|
| 1023 |
+
attention_mask=causal_mask,
|
| 1024 |
+
position_ids=position_ids,
|
| 1025 |
+
past_key_value=past_key_values,
|
| 1026 |
+
output_attentions=output_attentions,
|
| 1027 |
+
use_cache=use_cache,
|
| 1028 |
+
cache_position=cache_position,
|
| 1029 |
+
position_embeddings=position_embeddings,
|
| 1030 |
+
)
|
| 1031 |
+
|
| 1032 |
+
hidden_states = layer_outputs[0]
|
| 1033 |
+
|
| 1034 |
+
if use_cache:
|
| 1035 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
| 1036 |
+
|
| 1037 |
+
if output_attentions:
|
| 1038 |
+
all_self_attns += (layer_outputs[1],)
|
| 1039 |
+
|
| 1040 |
+
hidden_states = self.norm(hidden_states)
|
| 1041 |
+
|
| 1042 |
+
# add hidden states from the last decoder layer
|
| 1043 |
+
if output_hidden_states:
|
| 1044 |
+
all_hidden_states += (hidden_states,)
|
| 1045 |
+
|
| 1046 |
+
next_cache = next_decoder_cache if use_cache else None
|
| 1047 |
+
if return_legacy_cache:
|
| 1048 |
+
next_cache = next_cache.to_legacy_cache()
|
| 1049 |
+
|
| 1050 |
+
if not return_dict:
|
| 1051 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
| 1052 |
+
return BaseModelOutputWithPast(
|
| 1053 |
+
last_hidden_state=hidden_states,
|
| 1054 |
+
past_key_values=next_cache,
|
| 1055 |
+
hidden_states=all_hidden_states,
|
| 1056 |
+
attentions=all_self_attns,
|
| 1057 |
+
)
|
| 1058 |
+
|
| 1059 |
+
def _update_causal_mask(
|
| 1060 |
+
self,
|
| 1061 |
+
attention_mask: torch.Tensor,
|
| 1062 |
+
input_tensor: torch.Tensor,
|
| 1063 |
+
cache_position: torch.Tensor,
|
| 1064 |
+
past_key_values: Cache,
|
| 1065 |
+
output_attentions: bool,
|
| 1066 |
+
):
|
| 1067 |
+
if self.config._attn_implementation == "flash_attention_2":
|
| 1068 |
+
if attention_mask is not None and 0.0 in attention_mask:
|
| 1069 |
+
return attention_mask
|
| 1070 |
+
return None
|
| 1071 |
+
|
| 1072 |
+
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
| 1073 |
+
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
| 1074 |
+
# to infer the attention mask.
|
| 1075 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
| 1076 |
+
using_static_cache = isinstance(past_key_values, StaticCache)
|
| 1077 |
+
|
| 1078 |
+
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
| 1079 |
+
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
|
| 1080 |
+
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
| 1081 |
+
attention_mask,
|
| 1082 |
+
inputs_embeds=input_tensor,
|
| 1083 |
+
past_key_values_length=past_seen_tokens,
|
| 1084 |
+
is_training=self.training,
|
| 1085 |
+
):
|
| 1086 |
+
return None
|
| 1087 |
+
|
| 1088 |
+
dtype, device = input_tensor.dtype, input_tensor.device
|
| 1089 |
+
min_dtype = torch.finfo(dtype).min
|
| 1090 |
+
sequence_length = input_tensor.shape[1]
|
| 1091 |
+
if using_static_cache:
|
| 1092 |
+
target_length = past_key_values.get_max_length()
|
| 1093 |
+
else:
|
| 1094 |
+
target_length = (
|
| 1095 |
+
attention_mask.shape[-1]
|
| 1096 |
+
if isinstance(attention_mask, torch.Tensor)
|
| 1097 |
+
else past_seen_tokens + sequence_length + 1
|
| 1098 |
+
)
|
| 1099 |
+
|
| 1100 |
+
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
| 1101 |
+
causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
| 1102 |
+
attention_mask,
|
| 1103 |
+
sequence_length=sequence_length,
|
| 1104 |
+
target_length=target_length,
|
| 1105 |
+
dtype=dtype,
|
| 1106 |
+
device=device,
|
| 1107 |
+
min_dtype=min_dtype,
|
| 1108 |
+
cache_position=cache_position,
|
| 1109 |
+
batch_size=input_tensor.shape[0],
|
| 1110 |
+
)
|
| 1111 |
+
|
| 1112 |
+
if (
|
| 1113 |
+
self.config._attn_implementation == "sdpa"
|
| 1114 |
+
and attention_mask is not None
|
| 1115 |
+
and attention_mask.device.type == "cuda"
|
| 1116 |
+
and not output_attentions
|
| 1117 |
+
):
|
| 1118 |
+
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
| 1119 |
+
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
| 1120 |
+
# Details: https://github.com/pytorch/pytorch/issues/110213
|
| 1121 |
+
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
| 1122 |
+
|
| 1123 |
+
return causal_mask
|
| 1124 |
+
|
| 1125 |
+
|
| 1126 |
+
class HyperCLOVAXForCausalLM(HyperCLOVAXPreTrainedModel, GenerationMixin):
|
| 1127 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 1128 |
+
|
| 1129 |
+
def __init__(self, config):
|
| 1130 |
+
super().__init__(config)
|
| 1131 |
+
self.model = HyperCLOVAXModel(config)
|
| 1132 |
+
self.vocab_size = config.vocab_size
|
| 1133 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 1134 |
+
|
| 1135 |
+
# Initialize weights and apply final processing
|
| 1136 |
+
self.post_init()
|
| 1137 |
+
|
| 1138 |
+
def _get_apply_liger_kernel_converter(self):
|
| 1139 |
+
return _apply_liger_kernel_to_instance
|
| 1140 |
+
|
| 1141 |
+
def get_input_embeddings(self):
|
| 1142 |
+
return self.model.embed_tokens
|
| 1143 |
+
|
| 1144 |
+
def set_input_embeddings(self, value):
|
| 1145 |
+
self.model.embed_tokens = value
|
| 1146 |
+
|
| 1147 |
+
def get_output_embeddings(self):
|
| 1148 |
+
return self.lm_head
|
| 1149 |
+
|
| 1150 |
+
def set_output_embeddings(self, new_embeddings):
|
| 1151 |
+
self.lm_head = new_embeddings
|
| 1152 |
+
|
| 1153 |
+
def set_decoder(self, decoder):
|
| 1154 |
+
self.model = decoder
|
| 1155 |
+
|
| 1156 |
+
def get_decoder(self):
|
| 1157 |
+
return self.model
|
| 1158 |
+
|
| 1159 |
+
@add_start_docstrings_to_model_forward(HyperCLOVAX_INPUTS_DOCSTRING)
|
| 1160 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
| 1161 |
+
def forward(
|
| 1162 |
+
self,
|
| 1163 |
+
input_ids: torch.LongTensor = None,
|
| 1164 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1165 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1166 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
| 1167 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1168 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1169 |
+
use_cache: Optional[bool] = None,
|
| 1170 |
+
output_attentions: Optional[bool] = None,
|
| 1171 |
+
output_hidden_states: Optional[bool] = None,
|
| 1172 |
+
return_dict: Optional[bool] = None,
|
| 1173 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 1174 |
+
num_logits_to_keep: int = 0,
|
| 1175 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 1176 |
+
r"""
|
| 1177 |
+
Args:
|
| 1178 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 1179 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
| 1180 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 1181 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
| 1182 |
+
|
| 1183 |
+
num_logits_to_keep (`int`, *optional*):
|
| 1184 |
+
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
|
| 1185 |
+
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
|
| 1186 |
+
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
|
| 1187 |
+
|
| 1188 |
+
Returns:
|
| 1189 |
+
|
| 1190 |
+
Example:
|
| 1191 |
+
|
| 1192 |
+
```python
|
| 1193 |
+
>>> from transformers import AutoTokenizer, HyperCLOVAXForCausalLM
|
| 1194 |
+
|
| 1195 |
+
>>> model = HyperCLOVAXForCausalLM.from_pretrained(YOUR_DIR)
|
| 1196 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(YOUR_DIR)
|
| 1197 |
+
|
| 1198 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
| 1199 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
| 1200 |
+
|
| 1201 |
+
>>> # Generate
|
| 1202 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
| 1203 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
| 1204 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
| 1205 |
+
```"""
|
| 1206 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 1207 |
+
output_hidden_states = (
|
| 1208 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 1209 |
+
)
|
| 1210 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1211 |
+
|
| 1212 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
| 1213 |
+
outputs = self.model(
|
| 1214 |
+
input_ids=input_ids,
|
| 1215 |
+
attention_mask=attention_mask,
|
| 1216 |
+
position_ids=position_ids,
|
| 1217 |
+
past_key_values=past_key_values,
|
| 1218 |
+
inputs_embeds=inputs_embeds,
|
| 1219 |
+
use_cache=use_cache,
|
| 1220 |
+
output_attentions=output_attentions,
|
| 1221 |
+
output_hidden_states=output_hidden_states,
|
| 1222 |
+
return_dict=return_dict,
|
| 1223 |
+
cache_position=cache_position,
|
| 1224 |
+
)
|
| 1225 |
+
|
| 1226 |
+
hidden_states = outputs[0]
|
| 1227 |
+
if self.config.pretraining_tp > 1:
|
| 1228 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
| 1229 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 1230 |
+
logits = torch.cat(logits, dim=-1)
|
| 1231 |
+
else:
|
| 1232 |
+
if labels is None and not is_torchdynamo_compiling():
|
| 1233 |
+
logger.warning_once(
|
| 1234 |
+
"Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
|
| 1235 |
+
)
|
| 1236 |
+
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
| 1237 |
+
# TODO: remove the float() operation in v4.46
|
| 1238 |
+
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
|
| 1239 |
+
|
| 1240 |
+
logits = logits * self.config.logits_scaling # mup
|
| 1241 |
+
|
| 1242 |
+
loss = None
|
| 1243 |
+
if labels is not None:
|
| 1244 |
+
# Upcast to float if we need to compute the loss to avoid potential precision issues
|
| 1245 |
+
logits = logits.float()
|
| 1246 |
+
# Shift so that tokens < n predict n
|
| 1247 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
| 1248 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 1249 |
+
# Flatten the tokens
|
| 1250 |
+
loss_fct = CrossEntropyLoss()
|
| 1251 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
| 1252 |
+
shift_labels = shift_labels.view(-1)
|
| 1253 |
+
# Enable model parallelism
|
| 1254 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
| 1255 |
+
loss = loss_fct(shift_logits, shift_labels)
|
| 1256 |
+
|
| 1257 |
+
if not return_dict:
|
| 1258 |
+
output = (logits,) + outputs[1:]
|
| 1259 |
+
return (loss,) + output if loss is not None else output
|
| 1260 |
+
|
| 1261 |
+
return CausalLMOutputWithPast(
|
| 1262 |
+
loss=loss,
|
| 1263 |
+
logits=logits,
|
| 1264 |
+
past_key_values=outputs.past_key_values,
|
| 1265 |
+
hidden_states=outputs.hidden_states,
|
| 1266 |
+
attentions=outputs.attentions,
|
| 1267 |
+
)
|
| 1268 |
+
|
| 1269 |
+
def prepare_inputs_for_generation(
|
| 1270 |
+
self,
|
| 1271 |
+
input_ids,
|
| 1272 |
+
past_key_values=None,
|
| 1273 |
+
attention_mask=None,
|
| 1274 |
+
inputs_embeds=None,
|
| 1275 |
+
cache_position=None,
|
| 1276 |
+
position_ids=None,
|
| 1277 |
+
use_cache=True,
|
| 1278 |
+
num_logits_to_keep=None,
|
| 1279 |
+
**kwargs,
|
| 1280 |
+
):
|
| 1281 |
+
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
|
| 1282 |
+
# Exception 1: when passing input_embeds, input_ids may be missing entries
|
| 1283 |
+
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
|
| 1284 |
+
if past_key_values is not None:
|
| 1285 |
+
if inputs_embeds is not None: # Exception 1
|
| 1286 |
+
input_ids = input_ids[:, -cache_position.shape[0] :]
|
| 1287 |
+
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
|
| 1288 |
+
input_ids = input_ids[:, cache_position]
|
| 1289 |
+
|
| 1290 |
+
if attention_mask is not None and position_ids is None:
|
| 1291 |
+
# create position_ids on the fly for batch generation
|
| 1292 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 1293 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 1294 |
+
if past_key_values:
|
| 1295 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
| 1296 |
+
|
| 1297 |
+
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
|
| 1298 |
+
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
|
| 1299 |
+
|
| 1300 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 1301 |
+
if inputs_embeds is not None and cache_position[0] == 0:
|
| 1302 |
+
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
|
| 1303 |
+
else:
|
| 1304 |
+
# The clone here is for the same reason as for `position_ids`.
|
| 1305 |
+
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
|
| 1306 |
+
|
| 1307 |
+
if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
|
| 1308 |
+
if model_inputs["inputs_embeds"] is not None:
|
| 1309 |
+
batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
|
| 1310 |
+
device = model_inputs["inputs_embeds"].device
|
| 1311 |
+
else:
|
| 1312 |
+
batch_size, sequence_length = model_inputs["input_ids"].shape
|
| 1313 |
+
device = model_inputs["input_ids"].device
|
| 1314 |
+
|
| 1315 |
+
dtype = self.lm_head.weight.dtype
|
| 1316 |
+
min_dtype = torch.finfo(dtype).min
|
| 1317 |
+
|
| 1318 |
+
attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
| 1319 |
+
attention_mask,
|
| 1320 |
+
sequence_length=sequence_length,
|
| 1321 |
+
target_length=past_key_values.get_max_length(),
|
| 1322 |
+
dtype=dtype,
|
| 1323 |
+
device=device,
|
| 1324 |
+
min_dtype=min_dtype,
|
| 1325 |
+
cache_position=cache_position,
|
| 1326 |
+
batch_size=batch_size,
|
| 1327 |
+
)
|
| 1328 |
+
|
| 1329 |
+
if num_logits_to_keep is not None:
|
| 1330 |
+
model_inputs["num_logits_to_keep"] = num_logits_to_keep
|
| 1331 |
+
|
| 1332 |
+
model_inputs.update(
|
| 1333 |
+
{
|
| 1334 |
+
"position_ids": position_ids,
|
| 1335 |
+
"cache_position": cache_position,
|
| 1336 |
+
"past_key_values": past_key_values,
|
| 1337 |
+
"use_cache": use_cache,
|
| 1338 |
+
"attention_mask": attention_mask,
|
| 1339 |
+
}
|
| 1340 |
+
)
|
| 1341 |
+
return model_inputs
|
| 1342 |
+
|
| 1343 |
+
|
| 1344 |
+
@add_start_docstrings(
|
| 1345 |
+
"""
|
| 1346 |
+
The HyperCLOVAX Model transformer with a sequence classification head on top (linear layer).
|
| 1347 |
+
|
| 1348 |
+
[`HyperCLOVAXForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
| 1349 |
+
(e.g. GPT-2) do.
|
| 1350 |
+
|
| 1351 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
| 1352 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
| 1353 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
| 1354 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
| 1355 |
+
each row of the batch).
|
| 1356 |
+
""",
|
| 1357 |
+
HyperCLOVAX_START_DOCSTRING,
|
| 1358 |
+
)
|
| 1359 |
+
class HyperCLOVAXForSequenceClassification(HyperCLOVAXPreTrainedModel):
|
| 1360 |
+
def __init__(self, config):
|
| 1361 |
+
super().__init__(config)
|
| 1362 |
+
self.num_labels = config.num_labels
|
| 1363 |
+
self.model = HyperCLOVAXModel(config)
|
| 1364 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
| 1365 |
+
|
| 1366 |
+
# Initialize weights and apply final processing
|
| 1367 |
+
self.post_init()
|
| 1368 |
+
|
| 1369 |
+
def get_input_embeddings(self):
|
| 1370 |
+
return self.model.embed_tokens
|
| 1371 |
+
|
| 1372 |
+
def set_input_embeddings(self, value):
|
| 1373 |
+
self.model.embed_tokens = value
|
| 1374 |
+
|
| 1375 |
+
@add_start_docstrings_to_model_forward(HyperCLOVAX_INPUTS_DOCSTRING)
|
| 1376 |
+
def forward(
|
| 1377 |
+
self,
|
| 1378 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 1379 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1380 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1381 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
| 1382 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1383 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1384 |
+
use_cache: Optional[bool] = None,
|
| 1385 |
+
output_attentions: Optional[bool] = None,
|
| 1386 |
+
output_hidden_states: Optional[bool] = None,
|
| 1387 |
+
return_dict: Optional[bool] = None,
|
| 1388 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
| 1389 |
+
r"""
|
| 1390 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1391 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 1392 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1393 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1394 |
+
"""
|
| 1395 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1396 |
+
|
| 1397 |
+
transformer_outputs = self.model(
|
| 1398 |
+
input_ids,
|
| 1399 |
+
attention_mask=attention_mask,
|
| 1400 |
+
position_ids=position_ids,
|
| 1401 |
+
past_key_values=past_key_values,
|
| 1402 |
+
inputs_embeds=inputs_embeds,
|
| 1403 |
+
use_cache=use_cache,
|
| 1404 |
+
output_attentions=output_attentions,
|
| 1405 |
+
output_hidden_states=output_hidden_states,
|
| 1406 |
+
return_dict=return_dict,
|
| 1407 |
+
)
|
| 1408 |
+
hidden_states = transformer_outputs[0]
|
| 1409 |
+
logits = self.score(hidden_states)
|
| 1410 |
+
|
| 1411 |
+
if input_ids is not None:
|
| 1412 |
+
batch_size = input_ids.shape[0]
|
| 1413 |
+
else:
|
| 1414 |
+
batch_size = inputs_embeds.shape[0]
|
| 1415 |
+
|
| 1416 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
| 1417 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
| 1418 |
+
if self.config.pad_token_id is None:
|
| 1419 |
+
sequence_lengths = -1
|
| 1420 |
+
else:
|
| 1421 |
+
if input_ids is not None:
|
| 1422 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
| 1423 |
+
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
| 1424 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
| 1425 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
| 1426 |
+
else:
|
| 1427 |
+
sequence_lengths = -1
|
| 1428 |
+
|
| 1429 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
| 1430 |
+
|
| 1431 |
+
loss = None
|
| 1432 |
+
if labels is not None:
|
| 1433 |
+
labels = labels.to(logits.device)
|
| 1434 |
+
if self.config.problem_type is None:
|
| 1435 |
+
if self.num_labels == 1:
|
| 1436 |
+
self.config.problem_type = "regression"
|
| 1437 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
| 1438 |
+
self.config.problem_type = "single_label_classification"
|
| 1439 |
+
else:
|
| 1440 |
+
self.config.problem_type = "multi_label_classification"
|
| 1441 |
+
|
| 1442 |
+
if self.config.problem_type == "regression":
|
| 1443 |
+
loss_fct = MSELoss()
|
| 1444 |
+
if self.num_labels == 1:
|
| 1445 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
| 1446 |
+
else:
|
| 1447 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1448 |
+
elif self.config.problem_type == "single_label_classification":
|
| 1449 |
+
loss_fct = CrossEntropyLoss()
|
| 1450 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
| 1451 |
+
elif self.config.problem_type == "multi_label_classification":
|
| 1452 |
+
loss_fct = BCEWithLogitsLoss()
|
| 1453 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1454 |
+
if not return_dict:
|
| 1455 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
| 1456 |
+
return ((loss,) + output) if loss is not None else output
|
| 1457 |
+
|
| 1458 |
+
return SequenceClassifierOutputWithPast(
|
| 1459 |
+
loss=loss,
|
| 1460 |
+
logits=pooled_logits,
|
| 1461 |
+
past_key_values=transformer_outputs.past_key_values,
|
| 1462 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 1463 |
+
attentions=transformer_outputs.attentions,
|
| 1464 |
+
)
|
| 1465 |
+
|
| 1466 |
+
|
| 1467 |
+
@add_start_docstrings(
|
| 1468 |
+
"""
|
| 1469 |
+
The HyperCLOVAX Model transformer with a span classification head on top for extractive question-answering tasks like
|
| 1470 |
+
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
| 1471 |
+
""",
|
| 1472 |
+
HyperCLOVAX_START_DOCSTRING,
|
| 1473 |
+
)
|
| 1474 |
+
class HyperCLOVAXForQuestionAnswering(HyperCLOVAXPreTrainedModel):
|
| 1475 |
+
base_model_prefix = "transformer"
|
| 1476 |
+
|
| 1477 |
+
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->HyperCLOVAX
|
| 1478 |
+
def __init__(self, config):
|
| 1479 |
+
super().__init__(config)
|
| 1480 |
+
self.transformer = HyperCLOVAXModel(config)
|
| 1481 |
+
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
| 1482 |
+
|
| 1483 |
+
# Initialize weights and apply final processing
|
| 1484 |
+
self.post_init()
|
| 1485 |
+
|
| 1486 |
+
def get_input_embeddings(self):
|
| 1487 |
+
return self.transformer.embed_tokens
|
| 1488 |
+
|
| 1489 |
+
def set_input_embeddings(self, value):
|
| 1490 |
+
self.transformer.embed_tokens = value
|
| 1491 |
+
|
| 1492 |
+
@add_start_docstrings_to_model_forward(HyperCLOVAX_INPUTS_DOCSTRING)
|
| 1493 |
+
def forward(
|
| 1494 |
+
self,
|
| 1495 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 1496 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
| 1497 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1498 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
| 1499 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1500 |
+
start_positions: Optional[torch.LongTensor] = None,
|
| 1501 |
+
end_positions: Optional[torch.LongTensor] = None,
|
| 1502 |
+
output_attentions: Optional[bool] = None,
|
| 1503 |
+
output_hidden_states: Optional[bool] = None,
|
| 1504 |
+
return_dict: Optional[bool] = None,
|
| 1505 |
+
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
| 1506 |
+
r"""
|
| 1507 |
+
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1508 |
+
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
| 1509 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
| 1510 |
+
are not taken into account for computing the loss.
|
| 1511 |
+
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1512 |
+
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
| 1513 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
| 1514 |
+
are not taken into account for computing the loss.
|
| 1515 |
+
"""
|
| 1516 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1517 |
+
|
| 1518 |
+
outputs = self.transformer(
|
| 1519 |
+
input_ids,
|
| 1520 |
+
attention_mask=attention_mask,
|
| 1521 |
+
position_ids=position_ids,
|
| 1522 |
+
past_key_values=past_key_values,
|
| 1523 |
+
inputs_embeds=inputs_embeds,
|
| 1524 |
+
output_attentions=output_attentions,
|
| 1525 |
+
output_hidden_states=output_hidden_states,
|
| 1526 |
+
return_dict=return_dict,
|
| 1527 |
+
)
|
| 1528 |
+
|
| 1529 |
+
sequence_output = outputs[0]
|
| 1530 |
+
|
| 1531 |
+
logits = self.qa_outputs(sequence_output)
|
| 1532 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
| 1533 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
| 1534 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
| 1535 |
+
|
| 1536 |
+
total_loss = None
|
| 1537 |
+
if start_positions is not None and end_positions is not None:
|
| 1538 |
+
# If we are on multi-GPU, split add a dimension
|
| 1539 |
+
if len(start_positions.size()) > 1:
|
| 1540 |
+
start_positions = start_positions.squeeze(-1).to(start_logits.device)
|
| 1541 |
+
if len(end_positions.size()) > 1:
|
| 1542 |
+
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
| 1543 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
| 1544 |
+
ignored_index = start_logits.size(1)
|
| 1545 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
| 1546 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
| 1547 |
+
|
| 1548 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
| 1549 |
+
start_loss = loss_fct(start_logits, start_positions)
|
| 1550 |
+
end_loss = loss_fct(end_logits, end_positions)
|
| 1551 |
+
total_loss = (start_loss + end_loss) / 2
|
| 1552 |
+
|
| 1553 |
+
if not return_dict:
|
| 1554 |
+
output = (start_logits, end_logits) + outputs[2:]
|
| 1555 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
| 1556 |
+
|
| 1557 |
+
return QuestionAnsweringModelOutput(
|
| 1558 |
+
loss=total_loss,
|
| 1559 |
+
start_logits=start_logits,
|
| 1560 |
+
end_logits=end_logits,
|
| 1561 |
+
hidden_states=outputs.hidden_states,
|
| 1562 |
+
attentions=outputs.attentions,
|
| 1563 |
+
)
|
| 1564 |
+
|
| 1565 |
+
|
| 1566 |
+
@add_start_docstrings(
|
| 1567 |
+
"""
|
| 1568 |
+
The HyperCLOVAX Model transformer with a token classification head on top (a linear layer on top of the hidden-states
|
| 1569 |
+
output) e.g. for Named-Entity-Recognition (NER) tasks.
|
| 1570 |
+
""",
|
| 1571 |
+
HyperCLOVAX_START_DOCSTRING,
|
| 1572 |
+
)
|
| 1573 |
+
class HyperCLOVAXForTokenClassification(HyperCLOVAXPreTrainedModel):
|
| 1574 |
+
def __init__(self, config):
|
| 1575 |
+
super().__init__(config)
|
| 1576 |
+
self.num_labels = config.num_labels
|
| 1577 |
+
self.model = HyperCLOVAXModel(config)
|
| 1578 |
+
if getattr(config, "classifier_dropout", None) is not None:
|
| 1579 |
+
classifier_dropout = config.classifier_dropout
|
| 1580 |
+
elif getattr(config, "hidden_dropout", None) is not None:
|
| 1581 |
+
classifier_dropout = config.hidden_dropout
|
| 1582 |
+
else:
|
| 1583 |
+
classifier_dropout = 0.1
|
| 1584 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
| 1585 |
+
self.score = nn.Linear(config.hidden_size, config.num_labels)
|
| 1586 |
+
|
| 1587 |
+
# Initialize weights and apply final processing
|
| 1588 |
+
self.post_init()
|
| 1589 |
+
|
| 1590 |
+
def get_input_embeddings(self):
|
| 1591 |
+
return self.model.embed_tokens
|
| 1592 |
+
|
| 1593 |
+
def set_input_embeddings(self, value):
|
| 1594 |
+
self.model.embed_tokens = value
|
| 1595 |
+
|
| 1596 |
+
@add_start_docstrings_to_model_forward(HyperCLOVAX_INPUTS_DOCSTRING)
|
| 1597 |
+
def forward(
|
| 1598 |
+
self,
|
| 1599 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 1600 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1601 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1602 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1603 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1604 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1605 |
+
use_cache: Optional[bool] = None,
|
| 1606 |
+
output_attentions: Optional[bool] = None,
|
| 1607 |
+
output_hidden_states: Optional[bool] = None,
|
| 1608 |
+
return_dict: Optional[bool] = None,
|
| 1609 |
+
) -> Union[Tuple, TokenClassifierOutput]:
|
| 1610 |
+
r"""
|
| 1611 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1612 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 1613 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1614 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1615 |
+
"""
|
| 1616 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1617 |
+
|
| 1618 |
+
outputs = self.model(
|
| 1619 |
+
input_ids,
|
| 1620 |
+
attention_mask=attention_mask,
|
| 1621 |
+
position_ids=position_ids,
|
| 1622 |
+
past_key_values=past_key_values,
|
| 1623 |
+
inputs_embeds=inputs_embeds,
|
| 1624 |
+
use_cache=use_cache,
|
| 1625 |
+
output_attentions=output_attentions,
|
| 1626 |
+
output_hidden_states=output_hidden_states,
|
| 1627 |
+
return_dict=return_dict,
|
| 1628 |
+
)
|
| 1629 |
+
sequence_output = outputs[0]
|
| 1630 |
+
sequence_output = self.dropout(sequence_output)
|
| 1631 |
+
logits = self.score(sequence_output)
|
| 1632 |
+
|
| 1633 |
+
loss = None
|
| 1634 |
+
if labels is not None:
|
| 1635 |
+
loss_fct = CrossEntropyLoss()
|
| 1636 |
+
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
| 1637 |
+
|
| 1638 |
+
if not return_dict:
|
| 1639 |
+
output = (logits,) + outputs[2:]
|
| 1640 |
+
return ((loss,) + output) if loss is not None else output
|
| 1641 |
+
|
| 1642 |
+
return TokenClassifierOutput(
|
| 1643 |
+
loss=loss,
|
| 1644 |
+
logits=logits,
|
| 1645 |
+
hidden_states=outputs.hidden_states,
|
| 1646 |
+
attentions=outputs.attentions,
|
| 1647 |
+
)
|
| 1648 |
+
|
| 1649 |
+
|
| 1650 |
+
################################################################################################
|
| 1651 |
+
################################################################################################
|
| 1652 |
+
"""
|
| 1653 |
+
liger kernel monkey patching
|
| 1654 |
+
https://github.com/linkedin/Liger-Kernel/blob/v0.5.2/src/liger_kernel/transformers/monkey_patch.py
|
| 1655 |
+
"""
|
| 1656 |
+
|
| 1657 |
+
import inspect
|
| 1658 |
+
import logging
|
| 1659 |
+
from functools import partial
|
| 1660 |
+
from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
|
| 1661 |
+
|
| 1662 |
+
import torch
|
| 1663 |
+
import torch.nn.functional as F
|
| 1664 |
+
import transformers
|
| 1665 |
+
from packaging import version
|
| 1666 |
+
from torch.nn import CrossEntropyLoss
|
| 1667 |
+
from transformers import PreTrainedModel
|
| 1668 |
+
|
| 1669 |
+
if TYPE_CHECKING:
|
| 1670 |
+
from transformers.cache_utils import Cache
|
| 1671 |
+
|
| 1672 |
+
import sys
|
| 1673 |
+
|
| 1674 |
+
from packaging.version import parse
|
| 1675 |
+
|
| 1676 |
+
if sys.version_info < (3, 8):
|
| 1677 |
+
import importlib_metadata
|
| 1678 |
+
else:
|
| 1679 |
+
import importlib.metadata as importlib_metadata
|
| 1680 |
+
|
| 1681 |
+
try:
|
| 1682 |
+
from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
|
| 1683 |
+
from liger_kernel.transformers.functional import liger_cross_entropy
|
| 1684 |
+
from liger_kernel.transformers.fused_linear_cross_entropy import (
|
| 1685 |
+
LigerFusedLinearCrossEntropyLoss,
|
| 1686 |
+
)
|
| 1687 |
+
from liger_kernel.transformers.rms_norm import LigerRMSNorm
|
| 1688 |
+
from liger_kernel.transformers.rope import liger_rotary_pos_emb
|
| 1689 |
+
from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
|
| 1690 |
+
|
| 1691 |
+
_is_liger_kernel_available = True
|
| 1692 |
+
|
| 1693 |
+
LIGER_KERNEL_MATCHING_VERSION = parse("0.5.2")
|
| 1694 |
+
liger_kernel_version = parse(importlib_metadata.version("liger_kernel"))
|
| 1695 |
+
_is_liger_kernel_version_matching = (
|
| 1696 |
+
liger_kernel_version.major,
|
| 1697 |
+
liger_kernel_version.minor,
|
| 1698 |
+
liger_kernel_version.release[-1],
|
| 1699 |
+
) == (
|
| 1700 |
+
LIGER_KERNEL_MATCHING_VERSION.major,
|
| 1701 |
+
LIGER_KERNEL_MATCHING_VERSION.minor,
|
| 1702 |
+
LIGER_KERNEL_MATCHING_VERSION.release[-1],
|
| 1703 |
+
)
|
| 1704 |
+
except Exception:
|
| 1705 |
+
_is_liger_kernel_available = False
|
| 1706 |
+
_is_liger_kernel_version_matching = False
|
| 1707 |
+
|
| 1708 |
+
|
| 1709 |
+
def lce_forward_deprecated(
|
| 1710 |
+
self,
|
| 1711 |
+
input_ids: torch.LongTensor = None,
|
| 1712 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1713 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1714 |
+
past_key_values: Optional[Union["Cache", List[torch.FloatTensor]]] = None,
|
| 1715 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1716 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1717 |
+
use_cache: Optional[bool] = None,
|
| 1718 |
+
output_attentions: Optional[bool] = None,
|
| 1719 |
+
output_hidden_states: Optional[bool] = None,
|
| 1720 |
+
return_dict: Optional[bool] = None,
|
| 1721 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 1722 |
+
num_logits_to_keep: int = 0,
|
| 1723 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 1724 |
+
|
| 1725 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 1726 |
+
output_hidden_states = (
|
| 1727 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 1728 |
+
)
|
| 1729 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1730 |
+
|
| 1731 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
| 1732 |
+
outputs = self.model(
|
| 1733 |
+
input_ids=input_ids,
|
| 1734 |
+
attention_mask=attention_mask,
|
| 1735 |
+
position_ids=position_ids,
|
| 1736 |
+
past_key_values=past_key_values,
|
| 1737 |
+
inputs_embeds=inputs_embeds,
|
| 1738 |
+
use_cache=use_cache,
|
| 1739 |
+
output_attentions=output_attentions,
|
| 1740 |
+
output_hidden_states=output_hidden_states,
|
| 1741 |
+
return_dict=return_dict,
|
| 1742 |
+
cache_position=cache_position,
|
| 1743 |
+
)
|
| 1744 |
+
hidden_states = outputs[0]
|
| 1745 |
+
|
| 1746 |
+
loss = None
|
| 1747 |
+
logits = None
|
| 1748 |
+
|
| 1749 |
+
if self.training and (labels is not None):
|
| 1750 |
+
if num_logits_to_keep != 0:
|
| 1751 |
+
hidden_states = hidden_states[:, -num_logits_to_keep:, :] # not sure if it has bug
|
| 1752 |
+
hidden_states = hidden_states * self.config.logits_scaling ## muP
|
| 1753 |
+
|
| 1754 |
+
shift_hidden_states = hidden_states[..., :-1, :].contiguous()
|
| 1755 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 1756 |
+
|
| 1757 |
+
# flatten tokens
|
| 1758 |
+
shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
|
| 1759 |
+
shift_labels = shift_labels.view(-1)
|
| 1760 |
+
|
| 1761 |
+
lce = LigerFusedLinearCrossEntropyLoss()
|
| 1762 |
+
loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
|
| 1763 |
+
|
| 1764 |
+
else:
|
| 1765 |
+
assert self.config.pretraining_tp == 1, "not supported"
|
| 1766 |
+
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
|
| 1767 |
+
logits = logits * self.config.logits_scaling ## muP
|
| 1768 |
+
|
| 1769 |
+
if labels is not None:
|
| 1770 |
+
# Upcast to float if we need to compute the loss to avoid potential precision issues
|
| 1771 |
+
logits = logits.float()
|
| 1772 |
+
# Shift so that tokens < n predict n
|
| 1773 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
| 1774 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 1775 |
+
# Flatten the tokens
|
| 1776 |
+
loss_fct = CrossEntropyLoss()
|
| 1777 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
| 1778 |
+
shift_labels = shift_labels.view(-1)
|
| 1779 |
+
# Enable model parallelism
|
| 1780 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
| 1781 |
+
loss = loss_fct(shift_logits, shift_labels)
|
| 1782 |
+
|
| 1783 |
+
if not return_dict:
|
| 1784 |
+
output = (logits,) + outputs[1:]
|
| 1785 |
+
return (loss,) + output if loss is not None else output
|
| 1786 |
+
|
| 1787 |
+
return CausalLMOutputWithPast(
|
| 1788 |
+
loss=loss,
|
| 1789 |
+
logits=logits,
|
| 1790 |
+
past_key_values=outputs.past_key_values,
|
| 1791 |
+
hidden_states=outputs.hidden_states,
|
| 1792 |
+
attentions=outputs.attentions,
|
| 1793 |
+
)
|
| 1794 |
+
|
| 1795 |
+
|
| 1796 |
+
def _bind_method_to_module(module, method_name: str, new_method: Callable):
|
| 1797 |
+
# Binds a new method to a module instance so that self is passed as the first argument
|
| 1798 |
+
module.__dict__[method_name] = new_method.__get__(module, module.__class__)
|
| 1799 |
+
|
| 1800 |
+
|
| 1801 |
+
def _patch_rms_norm_module(module, offset=0.0, eps=1e-6, casting_mode="llama", in_place=True):
|
| 1802 |
+
module.offset = offset
|
| 1803 |
+
module.casting_mode = casting_mode
|
| 1804 |
+
module.variance_epsilon = getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps
|
| 1805 |
+
module.in_place = in_place
|
| 1806 |
+
_bind_method_to_module(module, "forward", LigerRMSNorm.forward)
|
| 1807 |
+
_bind_method_to_module(module, "extra_repr", LigerRMSNorm.extra_repr)
|
| 1808 |
+
|
| 1809 |
+
|
| 1810 |
+
def apply_liger_kernel_to_hyperclovax(
|
| 1811 |
+
rope: bool = True,
|
| 1812 |
+
cross_entropy: bool = False,
|
| 1813 |
+
fused_linear_cross_entropy: bool = True,
|
| 1814 |
+
rms_norm: bool = True,
|
| 1815 |
+
swiglu: bool = True,
|
| 1816 |
+
model: PreTrainedModel = None,
|
| 1817 |
+
) -> None:
|
| 1818 |
+
|
| 1819 |
+
assert not cross_entropy, "not supported"
|
| 1820 |
+
if rope:
|
| 1821 |
+
apply_rotary_pos_emb = liger_rotary_pos_emb
|
| 1822 |
+
if rms_norm:
|
| 1823 |
+
HyperCLOVAXRMSNorm = LigerRMSNorm
|
| 1824 |
+
if swiglu:
|
| 1825 |
+
HyperCLOVAXMLP = LigerSwiGLUMLP
|
| 1826 |
+
# to use VLM forward in VLM repo
|
| 1827 |
+
# if fused_linear_cross_entropy:
|
| 1828 |
+
# HyperCLOVAXForCausalLM.forward = lce_forward_deprecated
|
| 1829 |
+
|
| 1830 |
+
if model is not None:
|
| 1831 |
+
# The model instance already exists, so we need to additionally patch the
|
| 1832 |
+
# instance variables that reference already-instantiated modules (e.g. LlamaRMSNorm or LlamaMLP)
|
| 1833 |
+
|
| 1834 |
+
# get the base model from the model instance
|
| 1835 |
+
base_model: HyperCLOVAXModel = getattr(model, model.base_model_prefix, model)
|
| 1836 |
+
|
| 1837 |
+
if rms_norm:
|
| 1838 |
+
_patch_rms_norm_module(base_model.norm)
|
| 1839 |
+
|
| 1840 |
+
for decoder_layer in base_model.layers:
|
| 1841 |
+
if swiglu:
|
| 1842 |
+
_bind_method_to_module(decoder_layer.mlp, "forward", LigerSwiGLUMLP.forward)
|
| 1843 |
+
if rms_norm:
|
| 1844 |
+
_patch_rms_norm_module(decoder_layer.input_layernorm)
|
| 1845 |
+
_patch_rms_norm_module(decoder_layer.post_attention_layernorm)
|
| 1846 |
+
if decoder_layer.use_post_norm:
|
| 1847 |
+
_patch_rms_norm_module(decoder_layer.post_norm1)
|
| 1848 |
+
_patch_rms_norm_module(decoder_layer.post_norm2)
|
| 1849 |
+
|
| 1850 |
+
|
| 1851 |
+
def _apply_liger_kernel_to_instance(model: PreTrainedModel, **kwargs) -> None:
|
| 1852 |
+
model_type = getattr(model, "config", None) and getattr(model.config, "model_type", None)
|
| 1853 |
+
assert model_type == "hyperclovax"
|
| 1854 |
+
apply_fn = apply_liger_kernel_to_hyperclovax
|
| 1855 |
+
apply_fn_signature = inspect.signature(apply_fn)
|
| 1856 |
+
|
| 1857 |
+
# Filter out the keyword arguments that are not supported by the apply function
|
| 1858 |
+
applicable_kwargs = {key: value for key, value in kwargs.items() if key in apply_fn_signature.parameters}
|
| 1859 |
+
logger.info(
|
| 1860 |
+
f"Applying Liger kernels to model instance with model type: {model_type} with kwargs: {applicable_kwargs}"
|
| 1861 |
+
)
|
| 1862 |
+
apply_fn(model=model, **applicable_kwargs)
|
| 1863 |
+
|
| 1864 |
+
|
| 1865 |
+
################################################################################################
|
| 1866 |
+
################################################################################################
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|endoftext|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|im_end|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"image_token": "<|IMAGE_PAD|>",
|
| 17 |
+
"pad_token": {
|
| 18 |
+
"content": "<|endoftext|>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
},
|
| 24 |
+
"sep_token": {
|
| 25 |
+
"content": "<|endoftext|>",
|
| 26 |
+
"lstrip": false,
|
| 27 |
+
"normalized": false,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
},
|
| 31 |
+
"unk_token": {
|
| 32 |
+
"content": "<|endoftext|>",
|
| 33 |
+
"lstrip": false,
|
| 34 |
+
"normalized": false,
|
| 35 |
+
"rstrip": false,
|
| 36 |
+
"single_word": false
|
| 37 |
+
},
|
| 38 |
+
"video_token": "<|VIDEO_PAD|>"
|
| 39 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2079 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"0": {
|
| 5 |
+
"content": "<|endoftext|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"128000": {
|
| 13 |
+
"content": "<|im_start|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"128001": {
|
| 21 |
+
"content": "<|im_end|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"128002": {
|
| 29 |
+
"content": "<|stop|>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
"128003": {
|
| 37 |
+
"content": "<|endofturn|>",
|
| 38 |
+
"lstrip": false,
|
| 39 |
+
"normalized": false,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": true
|
| 43 |
+
},
|
| 44 |
+
"128004": {
|
| 45 |
+
"content": "<|fim_prefix|>",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false,
|
| 50 |
+
"special": true
|
| 51 |
+
},
|
| 52 |
+
"128005": {
|
| 53 |
+
"content": "<|fim_middle|>",
|
| 54 |
+
"lstrip": false,
|
| 55 |
+
"normalized": false,
|
| 56 |
+
"rstrip": false,
|
| 57 |
+
"single_word": false,
|
| 58 |
+
"special": true
|
| 59 |
+
},
|
| 60 |
+
"128006": {
|
| 61 |
+
"content": "<|fim_suffix|>",
|
| 62 |
+
"lstrip": false,
|
| 63 |
+
"normalized": false,
|
| 64 |
+
"rstrip": false,
|
| 65 |
+
"single_word": false,
|
| 66 |
+
"special": true
|
| 67 |
+
},
|
| 68 |
+
"128007": {
|
| 69 |
+
"content": "<repo_name>",
|
| 70 |
+
"lstrip": false,
|
| 71 |
+
"normalized": false,
|
| 72 |
+
"rstrip": false,
|
| 73 |
+
"single_word": false,
|
| 74 |
+
"special": true
|
| 75 |
+
},
|
| 76 |
+
"128008": {
|
| 77 |
+
"content": "<file_sep>",
|
| 78 |
+
"lstrip": false,
|
| 79 |
+
"normalized": false,
|
| 80 |
+
"rstrip": false,
|
| 81 |
+
"single_word": false,
|
| 82 |
+
"special": true
|
| 83 |
+
},
|
| 84 |
+
"128009": {
|
| 85 |
+
"content": "<issue_start>",
|
| 86 |
+
"lstrip": false,
|
| 87 |
+
"normalized": false,
|
| 88 |
+
"rstrip": false,
|
| 89 |
+
"single_word": false,
|
| 90 |
+
"special": true
|
| 91 |
+
},
|
| 92 |
+
"128010": {
|
| 93 |
+
"content": "<issue_comment>",
|
| 94 |
+
"lstrip": false,
|
| 95 |
+
"normalized": false,
|
| 96 |
+
"rstrip": false,
|
| 97 |
+
"single_word": false,
|
| 98 |
+
"special": true
|
| 99 |
+
},
|
| 100 |
+
"128011": {
|
| 101 |
+
"content": "<issue_closed>",
|
| 102 |
+
"lstrip": false,
|
| 103 |
+
"normalized": false,
|
| 104 |
+
"rstrip": false,
|
| 105 |
+
"single_word": false,
|
| 106 |
+
"special": true
|
| 107 |
+
},
|
| 108 |
+
"128012": {
|
| 109 |
+
"content": "<jupyter_start>",
|
| 110 |
+
"lstrip": false,
|
| 111 |
+
"normalized": false,
|
| 112 |
+
"rstrip": false,
|
| 113 |
+
"single_word": false,
|
| 114 |
+
"special": true
|
| 115 |
+
},
|
| 116 |
+
"128013": {
|
| 117 |
+
"content": "<jupyter_text>",
|
| 118 |
+
"lstrip": false,
|
| 119 |
+
"normalized": false,
|
| 120 |
+
"rstrip": false,
|
| 121 |
+
"single_word": false,
|
| 122 |
+
"special": true
|
| 123 |
+
},
|
| 124 |
+
"128014": {
|
| 125 |
+
"content": "<jupyter_code>",
|
| 126 |
+
"lstrip": false,
|
| 127 |
+
"normalized": false,
|
| 128 |
+
"rstrip": false,
|
| 129 |
+
"single_word": false,
|
| 130 |
+
"special": true
|
| 131 |
+
},
|
| 132 |
+
"128015": {
|
| 133 |
+
"content": "<jupyter_output>",
|
| 134 |
+
"lstrip": false,
|
| 135 |
+
"normalized": false,
|
| 136 |
+
"rstrip": false,
|
| 137 |
+
"single_word": false,
|
| 138 |
+
"special": true
|
| 139 |
+
},
|
| 140 |
+
"128016": {
|
| 141 |
+
"content": "<jupyter_script>",
|
| 142 |
+
"lstrip": false,
|
| 143 |
+
"normalized": false,
|
| 144 |
+
"rstrip": false,
|
| 145 |
+
"single_word": false,
|
| 146 |
+
"special": true
|
| 147 |
+
},
|
| 148 |
+
"128017": {
|
| 149 |
+
"content": "<empty_output>",
|
| 150 |
+
"lstrip": false,
|
| 151 |
+
"normalized": false,
|
| 152 |
+
"rstrip": false,
|
| 153 |
+
"single_word": false,
|
| 154 |
+
"special": true
|
| 155 |
+
},
|
| 156 |
+
"128018": {
|
| 157 |
+
"content": "<code_to_intermediate>",
|
| 158 |
+
"lstrip": false,
|
| 159 |
+
"normalized": false,
|
| 160 |
+
"rstrip": false,
|
| 161 |
+
"single_word": false,
|
| 162 |
+
"special": true
|
| 163 |
+
},
|
| 164 |
+
"128019": {
|
| 165 |
+
"content": "<intermediate_to_code>",
|
| 166 |
+
"lstrip": false,
|
| 167 |
+
"normalized": false,
|
| 168 |
+
"rstrip": false,
|
| 169 |
+
"single_word": false,
|
| 170 |
+
"special": true
|
| 171 |
+
},
|
| 172 |
+
"128020": {
|
| 173 |
+
"content": "<pr>",
|
| 174 |
+
"lstrip": false,
|
| 175 |
+
"normalized": false,
|
| 176 |
+
"rstrip": false,
|
| 177 |
+
"single_word": false,
|
| 178 |
+
"special": true
|
| 179 |
+
},
|
| 180 |
+
"128021": {
|
| 181 |
+
"content": "<pr_status>",
|
| 182 |
+
"lstrip": false,
|
| 183 |
+
"normalized": false,
|
| 184 |
+
"rstrip": false,
|
| 185 |
+
"single_word": false,
|
| 186 |
+
"special": true
|
| 187 |
+
},
|
| 188 |
+
"128022": {
|
| 189 |
+
"content": "<pr_is_merged>",
|
| 190 |
+
"lstrip": false,
|
| 191 |
+
"normalized": false,
|
| 192 |
+
"rstrip": false,
|
| 193 |
+
"single_word": false,
|
| 194 |
+
"special": true
|
| 195 |
+
},
|
| 196 |
+
"128023": {
|
| 197 |
+
"content": "<pr_base>",
|
| 198 |
+
"lstrip": false,
|
| 199 |
+
"normalized": false,
|
| 200 |
+
"rstrip": false,
|
| 201 |
+
"single_word": false,
|
| 202 |
+
"special": true
|
| 203 |
+
},
|
| 204 |
+
"128024": {
|
| 205 |
+
"content": "<pr_file>",
|
| 206 |
+
"lstrip": false,
|
| 207 |
+
"normalized": false,
|
| 208 |
+
"rstrip": false,
|
| 209 |
+
"single_word": false,
|
| 210 |
+
"special": true
|
| 211 |
+
},
|
| 212 |
+
"128025": {
|
| 213 |
+
"content": "<pr_base_code>",
|
| 214 |
+
"lstrip": false,
|
| 215 |
+
"normalized": false,
|
| 216 |
+
"rstrip": false,
|
| 217 |
+
"single_word": false,
|
| 218 |
+
"special": true
|
| 219 |
+
},
|
| 220 |
+
"128026": {
|
| 221 |
+
"content": "<pr_diff>",
|
| 222 |
+
"lstrip": false,
|
| 223 |
+
"normalized": false,
|
| 224 |
+
"rstrip": false,
|
| 225 |
+
"single_word": false,
|
| 226 |
+
"special": true
|
| 227 |
+
},
|
| 228 |
+
"128027": {
|
| 229 |
+
"content": "<pr_diff_hunk>",
|
| 230 |
+
"lstrip": false,
|
| 231 |
+
"normalized": false,
|
| 232 |
+
"rstrip": false,
|
| 233 |
+
"single_word": false,
|
| 234 |
+
"special": true
|
| 235 |
+
},
|
| 236 |
+
"128028": {
|
| 237 |
+
"content": "<pr_comment>",
|
| 238 |
+
"lstrip": false,
|
| 239 |
+
"normalized": false,
|
| 240 |
+
"rstrip": false,
|
| 241 |
+
"single_word": false,
|
| 242 |
+
"special": true
|
| 243 |
+
},
|
| 244 |
+
"128029": {
|
| 245 |
+
"content": "<pr_event_id>",
|
| 246 |
+
"lstrip": false,
|
| 247 |
+
"normalized": false,
|
| 248 |
+
"rstrip": false,
|
| 249 |
+
"single_word": false,
|
| 250 |
+
"special": true
|
| 251 |
+
},
|
| 252 |
+
"128030": {
|
| 253 |
+
"content": "<pr_review>",
|
| 254 |
+
"lstrip": false,
|
| 255 |
+
"normalized": false,
|
| 256 |
+
"rstrip": false,
|
| 257 |
+
"single_word": false,
|
| 258 |
+
"special": true
|
| 259 |
+
},
|
| 260 |
+
"128031": {
|
| 261 |
+
"content": "<pr_review_state>",
|
| 262 |
+
"lstrip": false,
|
| 263 |
+
"normalized": false,
|
| 264 |
+
"rstrip": false,
|
| 265 |
+
"single_word": false,
|
| 266 |
+
"special": true
|
| 267 |
+
},
|
| 268 |
+
"128032": {
|
| 269 |
+
"content": "<pr_review_comment>",
|
| 270 |
+
"lstrip": false,
|
| 271 |
+
"normalized": false,
|
| 272 |
+
"rstrip": false,
|
| 273 |
+
"single_word": false,
|
| 274 |
+
"special": true
|
| 275 |
+
},
|
| 276 |
+
"128033": {
|
| 277 |
+
"content": "<pr_in_reply_to_review_id>",
|
| 278 |
+
"lstrip": false,
|
| 279 |
+
"normalized": false,
|
| 280 |
+
"rstrip": false,
|
| 281 |
+
"single_word": false,
|
| 282 |
+
"special": true
|
| 283 |
+
},
|
| 284 |
+
"128034": {
|
| 285 |
+
"content": "<pr_in_reply_to_comment_id>",
|
| 286 |
+
"lstrip": false,
|
| 287 |
+
"normalized": false,
|
| 288 |
+
"rstrip": false,
|
| 289 |
+
"single_word": false,
|
| 290 |
+
"special": true
|
| 291 |
+
},
|
| 292 |
+
"128035": {
|
| 293 |
+
"content": "<pr_diff_hunk_comment_line>",
|
| 294 |
+
"lstrip": false,
|
| 295 |
+
"normalized": false,
|
| 296 |
+
"rstrip": false,
|
| 297 |
+
"single_word": false,
|
| 298 |
+
"special": true
|
| 299 |
+
},
|
| 300 |
+
"128036": {
|
| 301 |
+
"content": "<NAME>",
|
| 302 |
+
"lstrip": false,
|
| 303 |
+
"normalized": false,
|
| 304 |
+
"rstrip": false,
|
| 305 |
+
"single_word": false,
|
| 306 |
+
"special": true
|
| 307 |
+
},
|
| 308 |
+
"128037": {
|
| 309 |
+
"content": "<EMAIL>",
|
| 310 |
+
"lstrip": false,
|
| 311 |
+
"normalized": false,
|
| 312 |
+
"rstrip": false,
|
| 313 |
+
"single_word": false,
|
| 314 |
+
"special": true
|
| 315 |
+
},
|
| 316 |
+
"128038": {
|
| 317 |
+
"content": "<KEY>",
|
| 318 |
+
"lstrip": false,
|
| 319 |
+
"normalized": false,
|
| 320 |
+
"rstrip": false,
|
| 321 |
+
"single_word": false,
|
| 322 |
+
"special": true
|
| 323 |
+
},
|
| 324 |
+
"128039": {
|
| 325 |
+
"content": "<PASSWORD>",
|
| 326 |
+
"lstrip": false,
|
| 327 |
+
"normalized": false,
|
| 328 |
+
"rstrip": false,
|
| 329 |
+
"single_word": false,
|
| 330 |
+
"special": true
|
| 331 |
+
},
|
| 332 |
+
"128040": {
|
| 333 |
+
"content": "<think>",
|
| 334 |
+
"lstrip": false,
|
| 335 |
+
"normalized": false,
|
| 336 |
+
"rstrip": false,
|
| 337 |
+
"single_word": false,
|
| 338 |
+
"special": false
|
| 339 |
+
},
|
| 340 |
+
"128041": {
|
| 341 |
+
"content": "</think>",
|
| 342 |
+
"lstrip": false,
|
| 343 |
+
"normalized": false,
|
| 344 |
+
"rstrip": false,
|
| 345 |
+
"single_word": false,
|
| 346 |
+
"special": false
|
| 347 |
+
},
|
| 348 |
+
"128042": {
|
| 349 |
+
"content": "<tool_call>",
|
| 350 |
+
"lstrip": false,
|
| 351 |
+
"normalized": false,
|
| 352 |
+
"rstrip": false,
|
| 353 |
+
"single_word": false,
|
| 354 |
+
"special": false
|
| 355 |
+
},
|
| 356 |
+
"128043": {
|
| 357 |
+
"content": "</tool_call>",
|
| 358 |
+
"lstrip": false,
|
| 359 |
+
"normalized": false,
|
| 360 |
+
"rstrip": false,
|
| 361 |
+
"single_word": false,
|
| 362 |
+
"special": false
|
| 363 |
+
},
|
| 364 |
+
"128044": {
|
| 365 |
+
"content": "<arg_key>",
|
| 366 |
+
"lstrip": false,
|
| 367 |
+
"normalized": false,
|
| 368 |
+
"rstrip": false,
|
| 369 |
+
"single_word": false,
|
| 370 |
+
"special": false
|
| 371 |
+
},
|
| 372 |
+
"128045": {
|
| 373 |
+
"content": "</arg_key>",
|
| 374 |
+
"lstrip": false,
|
| 375 |
+
"normalized": false,
|
| 376 |
+
"rstrip": false,
|
| 377 |
+
"single_word": false,
|
| 378 |
+
"special": false
|
| 379 |
+
},
|
| 380 |
+
"128046": {
|
| 381 |
+
"content": "<arg_value>",
|
| 382 |
+
"lstrip": false,
|
| 383 |
+
"normalized": false,
|
| 384 |
+
"rstrip": false,
|
| 385 |
+
"single_word": false,
|
| 386 |
+
"special": false
|
| 387 |
+
},
|
| 388 |
+
"128047": {
|
| 389 |
+
"content": "</arg_value>",
|
| 390 |
+
"lstrip": false,
|
| 391 |
+
"normalized": false,
|
| 392 |
+
"rstrip": false,
|
| 393 |
+
"single_word": false,
|
| 394 |
+
"special": false
|
| 395 |
+
},
|
| 396 |
+
"128048": {
|
| 397 |
+
"content": "<tool_response>",
|
| 398 |
+
"lstrip": false,
|
| 399 |
+
"normalized": false,
|
| 400 |
+
"rstrip": false,
|
| 401 |
+
"single_word": false,
|
| 402 |
+
"special": false
|
| 403 |
+
},
|
| 404 |
+
"128049": {
|
| 405 |
+
"content": "</tool_response>",
|
| 406 |
+
"lstrip": false,
|
| 407 |
+
"normalized": false,
|
| 408 |
+
"rstrip": false,
|
| 409 |
+
"single_word": false,
|
| 410 |
+
"special": false
|
| 411 |
+
},
|
| 412 |
+
"128050": {
|
| 413 |
+
"content": "<tools>",
|
| 414 |
+
"lstrip": false,
|
| 415 |
+
"normalized": false,
|
| 416 |
+
"rstrip": false,
|
| 417 |
+
"single_word": false,
|
| 418 |
+
"special": false
|
| 419 |
+
},
|
| 420 |
+
"128051": {
|
| 421 |
+
"content": "</tools>",
|
| 422 |
+
"lstrip": false,
|
| 423 |
+
"normalized": false,
|
| 424 |
+
"rstrip": false,
|
| 425 |
+
"single_word": false,
|
| 426 |
+
"special": false
|
| 427 |
+
},
|
| 428 |
+
"128052": {
|
| 429 |
+
"content": "<|mime_start|>",
|
| 430 |
+
"lstrip": false,
|
| 431 |
+
"normalized": false,
|
| 432 |
+
"rstrip": false,
|
| 433 |
+
"single_word": false,
|
| 434 |
+
"special": true
|
| 435 |
+
},
|
| 436 |
+
"128053": {
|
| 437 |
+
"content": "<|mime_end|>",
|
| 438 |
+
"lstrip": false,
|
| 439 |
+
"normalized": false,
|
| 440 |
+
"rstrip": false,
|
| 441 |
+
"single_word": false,
|
| 442 |
+
"special": true
|
| 443 |
+
},
|
| 444 |
+
"128054": {
|
| 445 |
+
"content": "<|document_start|>",
|
| 446 |
+
"lstrip": false,
|
| 447 |
+
"normalized": false,
|
| 448 |
+
"rstrip": false,
|
| 449 |
+
"single_word": false,
|
| 450 |
+
"special": true
|
| 451 |
+
},
|
| 452 |
+
"128055": {
|
| 453 |
+
"content": "<|document_end|>",
|
| 454 |
+
"lstrip": false,
|
| 455 |
+
"normalized": false,
|
| 456 |
+
"rstrip": false,
|
| 457 |
+
"single_word": false,
|
| 458 |
+
"special": true
|
| 459 |
+
},
|
| 460 |
+
"128056": {
|
| 461 |
+
"content": "<|image_start|>",
|
| 462 |
+
"lstrip": false,
|
| 463 |
+
"normalized": false,
|
| 464 |
+
"rstrip": false,
|
| 465 |
+
"single_word": false,
|
| 466 |
+
"special": true
|
| 467 |
+
},
|
| 468 |
+
"128057": {
|
| 469 |
+
"content": "<|image_end|>",
|
| 470 |
+
"lstrip": false,
|
| 471 |
+
"normalized": false,
|
| 472 |
+
"rstrip": false,
|
| 473 |
+
"single_word": false,
|
| 474 |
+
"special": true
|
| 475 |
+
},
|
| 476 |
+
"128058": {
|
| 477 |
+
"content": "<|video_start|>",
|
| 478 |
+
"lstrip": false,
|
| 479 |
+
"normalized": false,
|
| 480 |
+
"rstrip": false,
|
| 481 |
+
"single_word": false,
|
| 482 |
+
"special": true
|
| 483 |
+
},
|
| 484 |
+
"128059": {
|
| 485 |
+
"content": "<|video_end|>",
|
| 486 |
+
"lstrip": false,
|
| 487 |
+
"normalized": false,
|
| 488 |
+
"rstrip": false,
|
| 489 |
+
"single_word": false,
|
| 490 |
+
"special": true
|
| 491 |
+
},
|
| 492 |
+
"128060": {
|
| 493 |
+
"content": "<|IMAGE_PAD|>",
|
| 494 |
+
"lstrip": false,
|
| 495 |
+
"normalized": false,
|
| 496 |
+
"rstrip": false,
|
| 497 |
+
"single_word": false,
|
| 498 |
+
"special": true
|
| 499 |
+
},
|
| 500 |
+
"128061": {
|
| 501 |
+
"content": "<|VIDEO_PAD|>",
|
| 502 |
+
"lstrip": false,
|
| 503 |
+
"normalized": false,
|
| 504 |
+
"rstrip": false,
|
| 505 |
+
"single_word": false,
|
| 506 |
+
"special": true
|
| 507 |
+
},
|
| 508 |
+
"128062": {
|
| 509 |
+
"content": "<|vision_aux_start|>",
|
| 510 |
+
"lstrip": false,
|
| 511 |
+
"normalized": false,
|
| 512 |
+
"rstrip": false,
|
| 513 |
+
"single_word": false,
|
| 514 |
+
"special": true
|
| 515 |
+
},
|
| 516 |
+
"128063": {
|
| 517 |
+
"content": "<|vision_aux_end|>",
|
| 518 |
+
"lstrip": false,
|
| 519 |
+
"normalized": false,
|
| 520 |
+
"rstrip": false,
|
| 521 |
+
"single_word": false,
|
| 522 |
+
"special": true
|
| 523 |
+
},
|
| 524 |
+
"128064": {
|
| 525 |
+
"content": "<|code_switching|>",
|
| 526 |
+
"lstrip": false,
|
| 527 |
+
"normalized": false,
|
| 528 |
+
"rstrip": false,
|
| 529 |
+
"single_word": false,
|
| 530 |
+
"special": true
|
| 531 |
+
},
|
| 532 |
+
"128065": {
|
| 533 |
+
"content": "<|back_translation|>",
|
| 534 |
+
"lstrip": false,
|
| 535 |
+
"normalized": false,
|
| 536 |
+
"rstrip": false,
|
| 537 |
+
"single_word": false,
|
| 538 |
+
"special": true
|
| 539 |
+
},
|
| 540 |
+
"128066": {
|
| 541 |
+
"content": "<|instruction_pretraining|>",
|
| 542 |
+
"lstrip": false,
|
| 543 |
+
"normalized": false,
|
| 544 |
+
"rstrip": false,
|
| 545 |
+
"single_word": false,
|
| 546 |
+
"special": true
|
| 547 |
+
},
|
| 548 |
+
"128067": {
|
| 549 |
+
"content": "<|_placeholder_067|>",
|
| 550 |
+
"lstrip": false,
|
| 551 |
+
"normalized": false,
|
| 552 |
+
"rstrip": false,
|
| 553 |
+
"single_word": false,
|
| 554 |
+
"special": true
|
| 555 |
+
},
|
| 556 |
+
"128068": {
|
| 557 |
+
"content": "<|_placeholder_068|>",
|
| 558 |
+
"lstrip": false,
|
| 559 |
+
"normalized": false,
|
| 560 |
+
"rstrip": false,
|
| 561 |
+
"single_word": false,
|
| 562 |
+
"special": true
|
| 563 |
+
},
|
| 564 |
+
"128069": {
|
| 565 |
+
"content": "<|_placeholder_069|>",
|
| 566 |
+
"lstrip": false,
|
| 567 |
+
"normalized": false,
|
| 568 |
+
"rstrip": false,
|
| 569 |
+
"single_word": false,
|
| 570 |
+
"special": true
|
| 571 |
+
},
|
| 572 |
+
"128070": {
|
| 573 |
+
"content": "<|_placeholder_070|>",
|
| 574 |
+
"lstrip": false,
|
| 575 |
+
"normalized": false,
|
| 576 |
+
"rstrip": false,
|
| 577 |
+
"single_word": false,
|
| 578 |
+
"special": true
|
| 579 |
+
},
|
| 580 |
+
"128071": {
|
| 581 |
+
"content": "<|_placeholder_071|>",
|
| 582 |
+
"lstrip": false,
|
| 583 |
+
"normalized": false,
|
| 584 |
+
"rstrip": false,
|
| 585 |
+
"single_word": false,
|
| 586 |
+
"special": true
|
| 587 |
+
},
|
| 588 |
+
"128072": {
|
| 589 |
+
"content": "<|_placeholder_072|>",
|
| 590 |
+
"lstrip": false,
|
| 591 |
+
"normalized": false,
|
| 592 |
+
"rstrip": false,
|
| 593 |
+
"single_word": false,
|
| 594 |
+
"special": true
|
| 595 |
+
},
|
| 596 |
+
"128073": {
|
| 597 |
+
"content": "<|_placeholder_073|>",
|
| 598 |
+
"lstrip": false,
|
| 599 |
+
"normalized": false,
|
| 600 |
+
"rstrip": false,
|
| 601 |
+
"single_word": false,
|
| 602 |
+
"special": true
|
| 603 |
+
},
|
| 604 |
+
"128074": {
|
| 605 |
+
"content": "<|_placeholder_074|>",
|
| 606 |
+
"lstrip": false,
|
| 607 |
+
"normalized": false,
|
| 608 |
+
"rstrip": false,
|
| 609 |
+
"single_word": false,
|
| 610 |
+
"special": true
|
| 611 |
+
},
|
| 612 |
+
"128075": {
|
| 613 |
+
"content": "<|_placeholder_075|>",
|
| 614 |
+
"lstrip": false,
|
| 615 |
+
"normalized": false,
|
| 616 |
+
"rstrip": false,
|
| 617 |
+
"single_word": false,
|
| 618 |
+
"special": true
|
| 619 |
+
},
|
| 620 |
+
"128076": {
|
| 621 |
+
"content": "<|_placeholder_076|>",
|
| 622 |
+
"lstrip": false,
|
| 623 |
+
"normalized": false,
|
| 624 |
+
"rstrip": false,
|
| 625 |
+
"single_word": false,
|
| 626 |
+
"special": true
|
| 627 |
+
},
|
| 628 |
+
"128077": {
|
| 629 |
+
"content": "<|_placeholder_077|>",
|
| 630 |
+
"lstrip": false,
|
| 631 |
+
"normalized": false,
|
| 632 |
+
"rstrip": false,
|
| 633 |
+
"single_word": false,
|
| 634 |
+
"special": true
|
| 635 |
+
},
|
| 636 |
+
"128078": {
|
| 637 |
+
"content": "<|_placeholder_078|>",
|
| 638 |
+
"lstrip": false,
|
| 639 |
+
"normalized": false,
|
| 640 |
+
"rstrip": false,
|
| 641 |
+
"single_word": false,
|
| 642 |
+
"special": true
|
| 643 |
+
},
|
| 644 |
+
"128079": {
|
| 645 |
+
"content": "<|_placeholder_079|>",
|
| 646 |
+
"lstrip": false,
|
| 647 |
+
"normalized": false,
|
| 648 |
+
"rstrip": false,
|
| 649 |
+
"single_word": false,
|
| 650 |
+
"special": true
|
| 651 |
+
},
|
| 652 |
+
"128080": {
|
| 653 |
+
"content": "<|_placeholder_080|>",
|
| 654 |
+
"lstrip": false,
|
| 655 |
+
"normalized": false,
|
| 656 |
+
"rstrip": false,
|
| 657 |
+
"single_word": false,
|
| 658 |
+
"special": true
|
| 659 |
+
},
|
| 660 |
+
"128081": {
|
| 661 |
+
"content": "<|_placeholder_081|>",
|
| 662 |
+
"lstrip": false,
|
| 663 |
+
"normalized": false,
|
| 664 |
+
"rstrip": false,
|
| 665 |
+
"single_word": false,
|
| 666 |
+
"special": true
|
| 667 |
+
},
|
| 668 |
+
"128082": {
|
| 669 |
+
"content": "<|_placeholder_082|>",
|
| 670 |
+
"lstrip": false,
|
| 671 |
+
"normalized": false,
|
| 672 |
+
"rstrip": false,
|
| 673 |
+
"single_word": false,
|
| 674 |
+
"special": true
|
| 675 |
+
},
|
| 676 |
+
"128083": {
|
| 677 |
+
"content": "<|_placeholder_083|>",
|
| 678 |
+
"lstrip": false,
|
| 679 |
+
"normalized": false,
|
| 680 |
+
"rstrip": false,
|
| 681 |
+
"single_word": false,
|
| 682 |
+
"special": true
|
| 683 |
+
},
|
| 684 |
+
"128084": {
|
| 685 |
+
"content": "<|_placeholder_084|>",
|
| 686 |
+
"lstrip": false,
|
| 687 |
+
"normalized": false,
|
| 688 |
+
"rstrip": false,
|
| 689 |
+
"single_word": false,
|
| 690 |
+
"special": true
|
| 691 |
+
},
|
| 692 |
+
"128085": {
|
| 693 |
+
"content": "<|_placeholder_085|>",
|
| 694 |
+
"lstrip": false,
|
| 695 |
+
"normalized": false,
|
| 696 |
+
"rstrip": false,
|
| 697 |
+
"single_word": false,
|
| 698 |
+
"special": true
|
| 699 |
+
},
|
| 700 |
+
"128086": {
|
| 701 |
+
"content": "<|_placeholder_086|>",
|
| 702 |
+
"lstrip": false,
|
| 703 |
+
"normalized": false,
|
| 704 |
+
"rstrip": false,
|
| 705 |
+
"single_word": false,
|
| 706 |
+
"special": true
|
| 707 |
+
},
|
| 708 |
+
"128087": {
|
| 709 |
+
"content": "<|_placeholder_087|>",
|
| 710 |
+
"lstrip": false,
|
| 711 |
+
"normalized": false,
|
| 712 |
+
"rstrip": false,
|
| 713 |
+
"single_word": false,
|
| 714 |
+
"special": true
|
| 715 |
+
},
|
| 716 |
+
"128088": {
|
| 717 |
+
"content": "<|_placeholder_088|>",
|
| 718 |
+
"lstrip": false,
|
| 719 |
+
"normalized": false,
|
| 720 |
+
"rstrip": false,
|
| 721 |
+
"single_word": false,
|
| 722 |
+
"special": true
|
| 723 |
+
},
|
| 724 |
+
"128089": {
|
| 725 |
+
"content": "<|_placeholder_089|>",
|
| 726 |
+
"lstrip": false,
|
| 727 |
+
"normalized": false,
|
| 728 |
+
"rstrip": false,
|
| 729 |
+
"single_word": false,
|
| 730 |
+
"special": true
|
| 731 |
+
},
|
| 732 |
+
"128090": {
|
| 733 |
+
"content": "<|_placeholder_090|>",
|
| 734 |
+
"lstrip": false,
|
| 735 |
+
"normalized": false,
|
| 736 |
+
"rstrip": false,
|
| 737 |
+
"single_word": false,
|
| 738 |
+
"special": true
|
| 739 |
+
},
|
| 740 |
+
"128091": {
|
| 741 |
+
"content": "<|_placeholder_091|>",
|
| 742 |
+
"lstrip": false,
|
| 743 |
+
"normalized": false,
|
| 744 |
+
"rstrip": false,
|
| 745 |
+
"single_word": false,
|
| 746 |
+
"special": true
|
| 747 |
+
},
|
| 748 |
+
"128092": {
|
| 749 |
+
"content": "<|_placeholder_092|>",
|
| 750 |
+
"lstrip": false,
|
| 751 |
+
"normalized": false,
|
| 752 |
+
"rstrip": false,
|
| 753 |
+
"single_word": false,
|
| 754 |
+
"special": true
|
| 755 |
+
},
|
| 756 |
+
"128093": {
|
| 757 |
+
"content": "<|_placeholder_093|>",
|
| 758 |
+
"lstrip": false,
|
| 759 |
+
"normalized": false,
|
| 760 |
+
"rstrip": false,
|
| 761 |
+
"single_word": false,
|
| 762 |
+
"special": true
|
| 763 |
+
},
|
| 764 |
+
"128094": {
|
| 765 |
+
"content": "<|_placeholder_094|>",
|
| 766 |
+
"lstrip": false,
|
| 767 |
+
"normalized": false,
|
| 768 |
+
"rstrip": false,
|
| 769 |
+
"single_word": false,
|
| 770 |
+
"special": true
|
| 771 |
+
},
|
| 772 |
+
"128095": {
|
| 773 |
+
"content": "<|_placeholder_095|>",
|
| 774 |
+
"lstrip": false,
|
| 775 |
+
"normalized": false,
|
| 776 |
+
"rstrip": false,
|
| 777 |
+
"single_word": false,
|
| 778 |
+
"special": true
|
| 779 |
+
},
|
| 780 |
+
"128096": {
|
| 781 |
+
"content": "<|_placeholder_096|>",
|
| 782 |
+
"lstrip": false,
|
| 783 |
+
"normalized": false,
|
| 784 |
+
"rstrip": false,
|
| 785 |
+
"single_word": false,
|
| 786 |
+
"special": true
|
| 787 |
+
},
|
| 788 |
+
"128097": {
|
| 789 |
+
"content": "<|_placeholder_097|>",
|
| 790 |
+
"lstrip": false,
|
| 791 |
+
"normalized": false,
|
| 792 |
+
"rstrip": false,
|
| 793 |
+
"single_word": false,
|
| 794 |
+
"special": true
|
| 795 |
+
},
|
| 796 |
+
"128098": {
|
| 797 |
+
"content": "<|_placeholder_098|>",
|
| 798 |
+
"lstrip": false,
|
| 799 |
+
"normalized": false,
|
| 800 |
+
"rstrip": false,
|
| 801 |
+
"single_word": false,
|
| 802 |
+
"special": true
|
| 803 |
+
},
|
| 804 |
+
"128099": {
|
| 805 |
+
"content": "<|_placeholder_099|>",
|
| 806 |
+
"lstrip": false,
|
| 807 |
+
"normalized": false,
|
| 808 |
+
"rstrip": false,
|
| 809 |
+
"single_word": false,
|
| 810 |
+
"special": true
|
| 811 |
+
},
|
| 812 |
+
"128100": {
|
| 813 |
+
"content": "<|_placeholder_100|>",
|
| 814 |
+
"lstrip": false,
|
| 815 |
+
"normalized": false,
|
| 816 |
+
"rstrip": false,
|
| 817 |
+
"single_word": false,
|
| 818 |
+
"special": true
|
| 819 |
+
},
|
| 820 |
+
"128101": {
|
| 821 |
+
"content": "<|_placeholder_101|>",
|
| 822 |
+
"lstrip": false,
|
| 823 |
+
"normalized": false,
|
| 824 |
+
"rstrip": false,
|
| 825 |
+
"single_word": false,
|
| 826 |
+
"special": true
|
| 827 |
+
},
|
| 828 |
+
"128102": {
|
| 829 |
+
"content": "<|_placeholder_102|>",
|
| 830 |
+
"lstrip": false,
|
| 831 |
+
"normalized": false,
|
| 832 |
+
"rstrip": false,
|
| 833 |
+
"single_word": false,
|
| 834 |
+
"special": true
|
| 835 |
+
},
|
| 836 |
+
"128103": {
|
| 837 |
+
"content": "<|_placeholder_103|>",
|
| 838 |
+
"lstrip": false,
|
| 839 |
+
"normalized": false,
|
| 840 |
+
"rstrip": false,
|
| 841 |
+
"single_word": false,
|
| 842 |
+
"special": true
|
| 843 |
+
},
|
| 844 |
+
"128104": {
|
| 845 |
+
"content": "<|_placeholder_104|>",
|
| 846 |
+
"lstrip": false,
|
| 847 |
+
"normalized": false,
|
| 848 |
+
"rstrip": false,
|
| 849 |
+
"single_word": false,
|
| 850 |
+
"special": true
|
| 851 |
+
},
|
| 852 |
+
"128105": {
|
| 853 |
+
"content": "<|_placeholder_105|>",
|
| 854 |
+
"lstrip": false,
|
| 855 |
+
"normalized": false,
|
| 856 |
+
"rstrip": false,
|
| 857 |
+
"single_word": false,
|
| 858 |
+
"special": true
|
| 859 |
+
},
|
| 860 |
+
"128106": {
|
| 861 |
+
"content": "<|_placeholder_106|>",
|
| 862 |
+
"lstrip": false,
|
| 863 |
+
"normalized": false,
|
| 864 |
+
"rstrip": false,
|
| 865 |
+
"single_word": false,
|
| 866 |
+
"special": true
|
| 867 |
+
},
|
| 868 |
+
"128107": {
|
| 869 |
+
"content": "<|_placeholder_107|>",
|
| 870 |
+
"lstrip": false,
|
| 871 |
+
"normalized": false,
|
| 872 |
+
"rstrip": false,
|
| 873 |
+
"single_word": false,
|
| 874 |
+
"special": true
|
| 875 |
+
},
|
| 876 |
+
"128108": {
|
| 877 |
+
"content": "<|_placeholder_108|>",
|
| 878 |
+
"lstrip": false,
|
| 879 |
+
"normalized": false,
|
| 880 |
+
"rstrip": false,
|
| 881 |
+
"single_word": false,
|
| 882 |
+
"special": true
|
| 883 |
+
},
|
| 884 |
+
"128109": {
|
| 885 |
+
"content": "<|_placeholder_109|>",
|
| 886 |
+
"lstrip": false,
|
| 887 |
+
"normalized": false,
|
| 888 |
+
"rstrip": false,
|
| 889 |
+
"single_word": false,
|
| 890 |
+
"special": true
|
| 891 |
+
},
|
| 892 |
+
"128110": {
|
| 893 |
+
"content": "<|_placeholder_110|>",
|
| 894 |
+
"lstrip": false,
|
| 895 |
+
"normalized": false,
|
| 896 |
+
"rstrip": false,
|
| 897 |
+
"single_word": false,
|
| 898 |
+
"special": true
|
| 899 |
+
},
|
| 900 |
+
"128111": {
|
| 901 |
+
"content": "<|_placeholder_111|>",
|
| 902 |
+
"lstrip": false,
|
| 903 |
+
"normalized": false,
|
| 904 |
+
"rstrip": false,
|
| 905 |
+
"single_word": false,
|
| 906 |
+
"special": true
|
| 907 |
+
},
|
| 908 |
+
"128112": {
|
| 909 |
+
"content": "<|_placeholder_112|>",
|
| 910 |
+
"lstrip": false,
|
| 911 |
+
"normalized": false,
|
| 912 |
+
"rstrip": false,
|
| 913 |
+
"single_word": false,
|
| 914 |
+
"special": true
|
| 915 |
+
},
|
| 916 |
+
"128113": {
|
| 917 |
+
"content": "<|_placeholder_113|>",
|
| 918 |
+
"lstrip": false,
|
| 919 |
+
"normalized": false,
|
| 920 |
+
"rstrip": false,
|
| 921 |
+
"single_word": false,
|
| 922 |
+
"special": true
|
| 923 |
+
},
|
| 924 |
+
"128114": {
|
| 925 |
+
"content": "<|_placeholder_114|>",
|
| 926 |
+
"lstrip": false,
|
| 927 |
+
"normalized": false,
|
| 928 |
+
"rstrip": false,
|
| 929 |
+
"single_word": false,
|
| 930 |
+
"special": true
|
| 931 |
+
},
|
| 932 |
+
"128115": {
|
| 933 |
+
"content": "<|_placeholder_115|>",
|
| 934 |
+
"lstrip": false,
|
| 935 |
+
"normalized": false,
|
| 936 |
+
"rstrip": false,
|
| 937 |
+
"single_word": false,
|
| 938 |
+
"special": true
|
| 939 |
+
},
|
| 940 |
+
"128116": {
|
| 941 |
+
"content": "<|_placeholder_116|>",
|
| 942 |
+
"lstrip": false,
|
| 943 |
+
"normalized": false,
|
| 944 |
+
"rstrip": false,
|
| 945 |
+
"single_word": false,
|
| 946 |
+
"special": true
|
| 947 |
+
},
|
| 948 |
+
"128117": {
|
| 949 |
+
"content": "<|_placeholder_117|>",
|
| 950 |
+
"lstrip": false,
|
| 951 |
+
"normalized": false,
|
| 952 |
+
"rstrip": false,
|
| 953 |
+
"single_word": false,
|
| 954 |
+
"special": true
|
| 955 |
+
},
|
| 956 |
+
"128118": {
|
| 957 |
+
"content": "<|_placeholder_118|>",
|
| 958 |
+
"lstrip": false,
|
| 959 |
+
"normalized": false,
|
| 960 |
+
"rstrip": false,
|
| 961 |
+
"single_word": false,
|
| 962 |
+
"special": true
|
| 963 |
+
},
|
| 964 |
+
"128119": {
|
| 965 |
+
"content": "<|_placeholder_119|>",
|
| 966 |
+
"lstrip": false,
|
| 967 |
+
"normalized": false,
|
| 968 |
+
"rstrip": false,
|
| 969 |
+
"single_word": false,
|
| 970 |
+
"special": true
|
| 971 |
+
},
|
| 972 |
+
"128120": {
|
| 973 |
+
"content": "<|_placeholder_120|>",
|
| 974 |
+
"lstrip": false,
|
| 975 |
+
"normalized": false,
|
| 976 |
+
"rstrip": false,
|
| 977 |
+
"single_word": false,
|
| 978 |
+
"special": true
|
| 979 |
+
},
|
| 980 |
+
"128121": {
|
| 981 |
+
"content": "<|_placeholder_121|>",
|
| 982 |
+
"lstrip": false,
|
| 983 |
+
"normalized": false,
|
| 984 |
+
"rstrip": false,
|
| 985 |
+
"single_word": false,
|
| 986 |
+
"special": true
|
| 987 |
+
},
|
| 988 |
+
"128122": {
|
| 989 |
+
"content": "<|_placeholder_122|>",
|
| 990 |
+
"lstrip": false,
|
| 991 |
+
"normalized": false,
|
| 992 |
+
"rstrip": false,
|
| 993 |
+
"single_word": false,
|
| 994 |
+
"special": true
|
| 995 |
+
},
|
| 996 |
+
"128123": {
|
| 997 |
+
"content": "<|_placeholder_123|>",
|
| 998 |
+
"lstrip": false,
|
| 999 |
+
"normalized": false,
|
| 1000 |
+
"rstrip": false,
|
| 1001 |
+
"single_word": false,
|
| 1002 |
+
"special": true
|
| 1003 |
+
},
|
| 1004 |
+
"128124": {
|
| 1005 |
+
"content": "<|_placeholder_124|>",
|
| 1006 |
+
"lstrip": false,
|
| 1007 |
+
"normalized": false,
|
| 1008 |
+
"rstrip": false,
|
| 1009 |
+
"single_word": false,
|
| 1010 |
+
"special": true
|
| 1011 |
+
},
|
| 1012 |
+
"128125": {
|
| 1013 |
+
"content": "<|_placeholder_125|>",
|
| 1014 |
+
"lstrip": false,
|
| 1015 |
+
"normalized": false,
|
| 1016 |
+
"rstrip": false,
|
| 1017 |
+
"single_word": false,
|
| 1018 |
+
"special": true
|
| 1019 |
+
},
|
| 1020 |
+
"128126": {
|
| 1021 |
+
"content": "<|_placeholder_126|>",
|
| 1022 |
+
"lstrip": false,
|
| 1023 |
+
"normalized": false,
|
| 1024 |
+
"rstrip": false,
|
| 1025 |
+
"single_word": false,
|
| 1026 |
+
"special": true
|
| 1027 |
+
},
|
| 1028 |
+
"128127": {
|
| 1029 |
+
"content": "<|_placeholder_127|>",
|
| 1030 |
+
"lstrip": false,
|
| 1031 |
+
"normalized": false,
|
| 1032 |
+
"rstrip": false,
|
| 1033 |
+
"single_word": false,
|
| 1034 |
+
"special": true
|
| 1035 |
+
},
|
| 1036 |
+
"128128": {
|
| 1037 |
+
"content": "<|_placeholder_128|>",
|
| 1038 |
+
"lstrip": false,
|
| 1039 |
+
"normalized": false,
|
| 1040 |
+
"rstrip": false,
|
| 1041 |
+
"single_word": false,
|
| 1042 |
+
"special": true
|
| 1043 |
+
},
|
| 1044 |
+
"128129": {
|
| 1045 |
+
"content": "<|_placeholder_129|>",
|
| 1046 |
+
"lstrip": false,
|
| 1047 |
+
"normalized": false,
|
| 1048 |
+
"rstrip": false,
|
| 1049 |
+
"single_word": false,
|
| 1050 |
+
"special": true
|
| 1051 |
+
},
|
| 1052 |
+
"128130": {
|
| 1053 |
+
"content": "<|_placeholder_130|>",
|
| 1054 |
+
"lstrip": false,
|
| 1055 |
+
"normalized": false,
|
| 1056 |
+
"rstrip": false,
|
| 1057 |
+
"single_word": false,
|
| 1058 |
+
"special": true
|
| 1059 |
+
},
|
| 1060 |
+
"128131": {
|
| 1061 |
+
"content": "<|_placeholder_131|>",
|
| 1062 |
+
"lstrip": false,
|
| 1063 |
+
"normalized": false,
|
| 1064 |
+
"rstrip": false,
|
| 1065 |
+
"single_word": false,
|
| 1066 |
+
"special": true
|
| 1067 |
+
},
|
| 1068 |
+
"128132": {
|
| 1069 |
+
"content": "<|_placeholder_132|>",
|
| 1070 |
+
"lstrip": false,
|
| 1071 |
+
"normalized": false,
|
| 1072 |
+
"rstrip": false,
|
| 1073 |
+
"single_word": false,
|
| 1074 |
+
"special": true
|
| 1075 |
+
},
|
| 1076 |
+
"128133": {
|
| 1077 |
+
"content": "<|_placeholder_133|>",
|
| 1078 |
+
"lstrip": false,
|
| 1079 |
+
"normalized": false,
|
| 1080 |
+
"rstrip": false,
|
| 1081 |
+
"single_word": false,
|
| 1082 |
+
"special": true
|
| 1083 |
+
},
|
| 1084 |
+
"128134": {
|
| 1085 |
+
"content": "<|_placeholder_134|>",
|
| 1086 |
+
"lstrip": false,
|
| 1087 |
+
"normalized": false,
|
| 1088 |
+
"rstrip": false,
|
| 1089 |
+
"single_word": false,
|
| 1090 |
+
"special": true
|
| 1091 |
+
},
|
| 1092 |
+
"128135": {
|
| 1093 |
+
"content": "<|_placeholder_135|>",
|
| 1094 |
+
"lstrip": false,
|
| 1095 |
+
"normalized": false,
|
| 1096 |
+
"rstrip": false,
|
| 1097 |
+
"single_word": false,
|
| 1098 |
+
"special": true
|
| 1099 |
+
},
|
| 1100 |
+
"128136": {
|
| 1101 |
+
"content": "<|_placeholder_136|>",
|
| 1102 |
+
"lstrip": false,
|
| 1103 |
+
"normalized": false,
|
| 1104 |
+
"rstrip": false,
|
| 1105 |
+
"single_word": false,
|
| 1106 |
+
"special": true
|
| 1107 |
+
},
|
| 1108 |
+
"128137": {
|
| 1109 |
+
"content": "<|_placeholder_137|>",
|
| 1110 |
+
"lstrip": false,
|
| 1111 |
+
"normalized": false,
|
| 1112 |
+
"rstrip": false,
|
| 1113 |
+
"single_word": false,
|
| 1114 |
+
"special": true
|
| 1115 |
+
},
|
| 1116 |
+
"128138": {
|
| 1117 |
+
"content": "<|_placeholder_138|>",
|
| 1118 |
+
"lstrip": false,
|
| 1119 |
+
"normalized": false,
|
| 1120 |
+
"rstrip": false,
|
| 1121 |
+
"single_word": false,
|
| 1122 |
+
"special": true
|
| 1123 |
+
},
|
| 1124 |
+
"128139": {
|
| 1125 |
+
"content": "<|_placeholder_139|>",
|
| 1126 |
+
"lstrip": false,
|
| 1127 |
+
"normalized": false,
|
| 1128 |
+
"rstrip": false,
|
| 1129 |
+
"single_word": false,
|
| 1130 |
+
"special": true
|
| 1131 |
+
},
|
| 1132 |
+
"128140": {
|
| 1133 |
+
"content": "<|_placeholder_140|>",
|
| 1134 |
+
"lstrip": false,
|
| 1135 |
+
"normalized": false,
|
| 1136 |
+
"rstrip": false,
|
| 1137 |
+
"single_word": false,
|
| 1138 |
+
"special": true
|
| 1139 |
+
},
|
| 1140 |
+
"128141": {
|
| 1141 |
+
"content": "<|_placeholder_141|>",
|
| 1142 |
+
"lstrip": false,
|
| 1143 |
+
"normalized": false,
|
| 1144 |
+
"rstrip": false,
|
| 1145 |
+
"single_word": false,
|
| 1146 |
+
"special": true
|
| 1147 |
+
},
|
| 1148 |
+
"128142": {
|
| 1149 |
+
"content": "<|_placeholder_142|>",
|
| 1150 |
+
"lstrip": false,
|
| 1151 |
+
"normalized": false,
|
| 1152 |
+
"rstrip": false,
|
| 1153 |
+
"single_word": false,
|
| 1154 |
+
"special": true
|
| 1155 |
+
},
|
| 1156 |
+
"128143": {
|
| 1157 |
+
"content": "<|_placeholder_143|>",
|
| 1158 |
+
"lstrip": false,
|
| 1159 |
+
"normalized": false,
|
| 1160 |
+
"rstrip": false,
|
| 1161 |
+
"single_word": false,
|
| 1162 |
+
"special": true
|
| 1163 |
+
},
|
| 1164 |
+
"128144": {
|
| 1165 |
+
"content": "<|_placeholder_144|>",
|
| 1166 |
+
"lstrip": false,
|
| 1167 |
+
"normalized": false,
|
| 1168 |
+
"rstrip": false,
|
| 1169 |
+
"single_word": false,
|
| 1170 |
+
"special": true
|
| 1171 |
+
},
|
| 1172 |
+
"128145": {
|
| 1173 |
+
"content": "<|_placeholder_145|>",
|
| 1174 |
+
"lstrip": false,
|
| 1175 |
+
"normalized": false,
|
| 1176 |
+
"rstrip": false,
|
| 1177 |
+
"single_word": false,
|
| 1178 |
+
"special": true
|
| 1179 |
+
},
|
| 1180 |
+
"128146": {
|
| 1181 |
+
"content": "<|_placeholder_146|>",
|
| 1182 |
+
"lstrip": false,
|
| 1183 |
+
"normalized": false,
|
| 1184 |
+
"rstrip": false,
|
| 1185 |
+
"single_word": false,
|
| 1186 |
+
"special": true
|
| 1187 |
+
},
|
| 1188 |
+
"128147": {
|
| 1189 |
+
"content": "<|_placeholder_147|>",
|
| 1190 |
+
"lstrip": false,
|
| 1191 |
+
"normalized": false,
|
| 1192 |
+
"rstrip": false,
|
| 1193 |
+
"single_word": false,
|
| 1194 |
+
"special": true
|
| 1195 |
+
},
|
| 1196 |
+
"128148": {
|
| 1197 |
+
"content": "<|_placeholder_148|>",
|
| 1198 |
+
"lstrip": false,
|
| 1199 |
+
"normalized": false,
|
| 1200 |
+
"rstrip": false,
|
| 1201 |
+
"single_word": false,
|
| 1202 |
+
"special": true
|
| 1203 |
+
},
|
| 1204 |
+
"128149": {
|
| 1205 |
+
"content": "<|_placeholder_149|>",
|
| 1206 |
+
"lstrip": false,
|
| 1207 |
+
"normalized": false,
|
| 1208 |
+
"rstrip": false,
|
| 1209 |
+
"single_word": false,
|
| 1210 |
+
"special": true
|
| 1211 |
+
},
|
| 1212 |
+
"128150": {
|
| 1213 |
+
"content": "<|_placeholder_150|>",
|
| 1214 |
+
"lstrip": false,
|
| 1215 |
+
"normalized": false,
|
| 1216 |
+
"rstrip": false,
|
| 1217 |
+
"single_word": false,
|
| 1218 |
+
"special": true
|
| 1219 |
+
},
|
| 1220 |
+
"128151": {
|
| 1221 |
+
"content": "<|_placeholder_151|>",
|
| 1222 |
+
"lstrip": false,
|
| 1223 |
+
"normalized": false,
|
| 1224 |
+
"rstrip": false,
|
| 1225 |
+
"single_word": false,
|
| 1226 |
+
"special": true
|
| 1227 |
+
},
|
| 1228 |
+
"128152": {
|
| 1229 |
+
"content": "<|_placeholder_152|>",
|
| 1230 |
+
"lstrip": false,
|
| 1231 |
+
"normalized": false,
|
| 1232 |
+
"rstrip": false,
|
| 1233 |
+
"single_word": false,
|
| 1234 |
+
"special": true
|
| 1235 |
+
},
|
| 1236 |
+
"128153": {
|
| 1237 |
+
"content": "<|_placeholder_153|>",
|
| 1238 |
+
"lstrip": false,
|
| 1239 |
+
"normalized": false,
|
| 1240 |
+
"rstrip": false,
|
| 1241 |
+
"single_word": false,
|
| 1242 |
+
"special": true
|
| 1243 |
+
},
|
| 1244 |
+
"128154": {
|
| 1245 |
+
"content": "<|_placeholder_154|>",
|
| 1246 |
+
"lstrip": false,
|
| 1247 |
+
"normalized": false,
|
| 1248 |
+
"rstrip": false,
|
| 1249 |
+
"single_word": false,
|
| 1250 |
+
"special": true
|
| 1251 |
+
},
|
| 1252 |
+
"128155": {
|
| 1253 |
+
"content": "<|_placeholder_155|>",
|
| 1254 |
+
"lstrip": false,
|
| 1255 |
+
"normalized": false,
|
| 1256 |
+
"rstrip": false,
|
| 1257 |
+
"single_word": false,
|
| 1258 |
+
"special": true
|
| 1259 |
+
},
|
| 1260 |
+
"128156": {
|
| 1261 |
+
"content": "<|_placeholder_156|>",
|
| 1262 |
+
"lstrip": false,
|
| 1263 |
+
"normalized": false,
|
| 1264 |
+
"rstrip": false,
|
| 1265 |
+
"single_word": false,
|
| 1266 |
+
"special": true
|
| 1267 |
+
},
|
| 1268 |
+
"128157": {
|
| 1269 |
+
"content": "<|_placeholder_157|>",
|
| 1270 |
+
"lstrip": false,
|
| 1271 |
+
"normalized": false,
|
| 1272 |
+
"rstrip": false,
|
| 1273 |
+
"single_word": false,
|
| 1274 |
+
"special": true
|
| 1275 |
+
},
|
| 1276 |
+
"128158": {
|
| 1277 |
+
"content": "<|_placeholder_158|>",
|
| 1278 |
+
"lstrip": false,
|
| 1279 |
+
"normalized": false,
|
| 1280 |
+
"rstrip": false,
|
| 1281 |
+
"single_word": false,
|
| 1282 |
+
"special": true
|
| 1283 |
+
},
|
| 1284 |
+
"128159": {
|
| 1285 |
+
"content": "<|_placeholder_159|>",
|
| 1286 |
+
"lstrip": false,
|
| 1287 |
+
"normalized": false,
|
| 1288 |
+
"rstrip": false,
|
| 1289 |
+
"single_word": false,
|
| 1290 |
+
"special": true
|
| 1291 |
+
},
|
| 1292 |
+
"128160": {
|
| 1293 |
+
"content": "<|_placeholder_160|>",
|
| 1294 |
+
"lstrip": false,
|
| 1295 |
+
"normalized": false,
|
| 1296 |
+
"rstrip": false,
|
| 1297 |
+
"single_word": false,
|
| 1298 |
+
"special": true
|
| 1299 |
+
},
|
| 1300 |
+
"128161": {
|
| 1301 |
+
"content": "<|_placeholder_161|>",
|
| 1302 |
+
"lstrip": false,
|
| 1303 |
+
"normalized": false,
|
| 1304 |
+
"rstrip": false,
|
| 1305 |
+
"single_word": false,
|
| 1306 |
+
"special": true
|
| 1307 |
+
},
|
| 1308 |
+
"128162": {
|
| 1309 |
+
"content": "<|_placeholder_162|>",
|
| 1310 |
+
"lstrip": false,
|
| 1311 |
+
"normalized": false,
|
| 1312 |
+
"rstrip": false,
|
| 1313 |
+
"single_word": false,
|
| 1314 |
+
"special": true
|
| 1315 |
+
},
|
| 1316 |
+
"128163": {
|
| 1317 |
+
"content": "<|_placeholder_163|>",
|
| 1318 |
+
"lstrip": false,
|
| 1319 |
+
"normalized": false,
|
| 1320 |
+
"rstrip": false,
|
| 1321 |
+
"single_word": false,
|
| 1322 |
+
"special": true
|
| 1323 |
+
},
|
| 1324 |
+
"128164": {
|
| 1325 |
+
"content": "<|_placeholder_164|>",
|
| 1326 |
+
"lstrip": false,
|
| 1327 |
+
"normalized": false,
|
| 1328 |
+
"rstrip": false,
|
| 1329 |
+
"single_word": false,
|
| 1330 |
+
"special": true
|
| 1331 |
+
},
|
| 1332 |
+
"128165": {
|
| 1333 |
+
"content": "<|_placeholder_165|>",
|
| 1334 |
+
"lstrip": false,
|
| 1335 |
+
"normalized": false,
|
| 1336 |
+
"rstrip": false,
|
| 1337 |
+
"single_word": false,
|
| 1338 |
+
"special": true
|
| 1339 |
+
},
|
| 1340 |
+
"128166": {
|
| 1341 |
+
"content": "<|_placeholder_166|>",
|
| 1342 |
+
"lstrip": false,
|
| 1343 |
+
"normalized": false,
|
| 1344 |
+
"rstrip": false,
|
| 1345 |
+
"single_word": false,
|
| 1346 |
+
"special": true
|
| 1347 |
+
},
|
| 1348 |
+
"128167": {
|
| 1349 |
+
"content": "<|_placeholder_167|>",
|
| 1350 |
+
"lstrip": false,
|
| 1351 |
+
"normalized": false,
|
| 1352 |
+
"rstrip": false,
|
| 1353 |
+
"single_word": false,
|
| 1354 |
+
"special": true
|
| 1355 |
+
},
|
| 1356 |
+
"128168": {
|
| 1357 |
+
"content": "<|_placeholder_168|>",
|
| 1358 |
+
"lstrip": false,
|
| 1359 |
+
"normalized": false,
|
| 1360 |
+
"rstrip": false,
|
| 1361 |
+
"single_word": false,
|
| 1362 |
+
"special": true
|
| 1363 |
+
},
|
| 1364 |
+
"128169": {
|
| 1365 |
+
"content": "<|_placeholder_169|>",
|
| 1366 |
+
"lstrip": false,
|
| 1367 |
+
"normalized": false,
|
| 1368 |
+
"rstrip": false,
|
| 1369 |
+
"single_word": false,
|
| 1370 |
+
"special": true
|
| 1371 |
+
},
|
| 1372 |
+
"128170": {
|
| 1373 |
+
"content": "<|_placeholder_170|>",
|
| 1374 |
+
"lstrip": false,
|
| 1375 |
+
"normalized": false,
|
| 1376 |
+
"rstrip": false,
|
| 1377 |
+
"single_word": false,
|
| 1378 |
+
"special": true
|
| 1379 |
+
},
|
| 1380 |
+
"128171": {
|
| 1381 |
+
"content": "<|_placeholder_171|>",
|
| 1382 |
+
"lstrip": false,
|
| 1383 |
+
"normalized": false,
|
| 1384 |
+
"rstrip": false,
|
| 1385 |
+
"single_word": false,
|
| 1386 |
+
"special": true
|
| 1387 |
+
},
|
| 1388 |
+
"128172": {
|
| 1389 |
+
"content": "<|_placeholder_172|>",
|
| 1390 |
+
"lstrip": false,
|
| 1391 |
+
"normalized": false,
|
| 1392 |
+
"rstrip": false,
|
| 1393 |
+
"single_word": false,
|
| 1394 |
+
"special": true
|
| 1395 |
+
},
|
| 1396 |
+
"128173": {
|
| 1397 |
+
"content": "<|_placeholder_173|>",
|
| 1398 |
+
"lstrip": false,
|
| 1399 |
+
"normalized": false,
|
| 1400 |
+
"rstrip": false,
|
| 1401 |
+
"single_word": false,
|
| 1402 |
+
"special": true
|
| 1403 |
+
},
|
| 1404 |
+
"128174": {
|
| 1405 |
+
"content": "<|_placeholder_174|>",
|
| 1406 |
+
"lstrip": false,
|
| 1407 |
+
"normalized": false,
|
| 1408 |
+
"rstrip": false,
|
| 1409 |
+
"single_word": false,
|
| 1410 |
+
"special": true
|
| 1411 |
+
},
|
| 1412 |
+
"128175": {
|
| 1413 |
+
"content": "<|_placeholder_175|>",
|
| 1414 |
+
"lstrip": false,
|
| 1415 |
+
"normalized": false,
|
| 1416 |
+
"rstrip": false,
|
| 1417 |
+
"single_word": false,
|
| 1418 |
+
"special": true
|
| 1419 |
+
},
|
| 1420 |
+
"128176": {
|
| 1421 |
+
"content": "<|_placeholder_176|>",
|
| 1422 |
+
"lstrip": false,
|
| 1423 |
+
"normalized": false,
|
| 1424 |
+
"rstrip": false,
|
| 1425 |
+
"single_word": false,
|
| 1426 |
+
"special": true
|
| 1427 |
+
},
|
| 1428 |
+
"128177": {
|
| 1429 |
+
"content": "<|_placeholder_177|>",
|
| 1430 |
+
"lstrip": false,
|
| 1431 |
+
"normalized": false,
|
| 1432 |
+
"rstrip": false,
|
| 1433 |
+
"single_word": false,
|
| 1434 |
+
"special": true
|
| 1435 |
+
},
|
| 1436 |
+
"128178": {
|
| 1437 |
+
"content": "<|_placeholder_178|>",
|
| 1438 |
+
"lstrip": false,
|
| 1439 |
+
"normalized": false,
|
| 1440 |
+
"rstrip": false,
|
| 1441 |
+
"single_word": false,
|
| 1442 |
+
"special": true
|
| 1443 |
+
},
|
| 1444 |
+
"128179": {
|
| 1445 |
+
"content": "<|_placeholder_179|>",
|
| 1446 |
+
"lstrip": false,
|
| 1447 |
+
"normalized": false,
|
| 1448 |
+
"rstrip": false,
|
| 1449 |
+
"single_word": false,
|
| 1450 |
+
"special": true
|
| 1451 |
+
},
|
| 1452 |
+
"128180": {
|
| 1453 |
+
"content": "<|_placeholder_180|>",
|
| 1454 |
+
"lstrip": false,
|
| 1455 |
+
"normalized": false,
|
| 1456 |
+
"rstrip": false,
|
| 1457 |
+
"single_word": false,
|
| 1458 |
+
"special": true
|
| 1459 |
+
},
|
| 1460 |
+
"128181": {
|
| 1461 |
+
"content": "<|_placeholder_181|>",
|
| 1462 |
+
"lstrip": false,
|
| 1463 |
+
"normalized": false,
|
| 1464 |
+
"rstrip": false,
|
| 1465 |
+
"single_word": false,
|
| 1466 |
+
"special": true
|
| 1467 |
+
},
|
| 1468 |
+
"128182": {
|
| 1469 |
+
"content": "<|_placeholder_182|>",
|
| 1470 |
+
"lstrip": false,
|
| 1471 |
+
"normalized": false,
|
| 1472 |
+
"rstrip": false,
|
| 1473 |
+
"single_word": false,
|
| 1474 |
+
"special": true
|
| 1475 |
+
},
|
| 1476 |
+
"128183": {
|
| 1477 |
+
"content": "<|_placeholder_183|>",
|
| 1478 |
+
"lstrip": false,
|
| 1479 |
+
"normalized": false,
|
| 1480 |
+
"rstrip": false,
|
| 1481 |
+
"single_word": false,
|
| 1482 |
+
"special": true
|
| 1483 |
+
},
|
| 1484 |
+
"128184": {
|
| 1485 |
+
"content": "<|_placeholder_184|>",
|
| 1486 |
+
"lstrip": false,
|
| 1487 |
+
"normalized": false,
|
| 1488 |
+
"rstrip": false,
|
| 1489 |
+
"single_word": false,
|
| 1490 |
+
"special": true
|
| 1491 |
+
},
|
| 1492 |
+
"128185": {
|
| 1493 |
+
"content": "<|_placeholder_185|>",
|
| 1494 |
+
"lstrip": false,
|
| 1495 |
+
"normalized": false,
|
| 1496 |
+
"rstrip": false,
|
| 1497 |
+
"single_word": false,
|
| 1498 |
+
"special": true
|
| 1499 |
+
},
|
| 1500 |
+
"128186": {
|
| 1501 |
+
"content": "<|_placeholder_186|>",
|
| 1502 |
+
"lstrip": false,
|
| 1503 |
+
"normalized": false,
|
| 1504 |
+
"rstrip": false,
|
| 1505 |
+
"single_word": false,
|
| 1506 |
+
"special": true
|
| 1507 |
+
},
|
| 1508 |
+
"128187": {
|
| 1509 |
+
"content": "<|_placeholder_187|>",
|
| 1510 |
+
"lstrip": false,
|
| 1511 |
+
"normalized": false,
|
| 1512 |
+
"rstrip": false,
|
| 1513 |
+
"single_word": false,
|
| 1514 |
+
"special": true
|
| 1515 |
+
},
|
| 1516 |
+
"128188": {
|
| 1517 |
+
"content": "<|_placeholder_188|>",
|
| 1518 |
+
"lstrip": false,
|
| 1519 |
+
"normalized": false,
|
| 1520 |
+
"rstrip": false,
|
| 1521 |
+
"single_word": false,
|
| 1522 |
+
"special": true
|
| 1523 |
+
},
|
| 1524 |
+
"128189": {
|
| 1525 |
+
"content": "<|_placeholder_189|>",
|
| 1526 |
+
"lstrip": false,
|
| 1527 |
+
"normalized": false,
|
| 1528 |
+
"rstrip": false,
|
| 1529 |
+
"single_word": false,
|
| 1530 |
+
"special": true
|
| 1531 |
+
},
|
| 1532 |
+
"128190": {
|
| 1533 |
+
"content": "<|_placeholder_190|>",
|
| 1534 |
+
"lstrip": false,
|
| 1535 |
+
"normalized": false,
|
| 1536 |
+
"rstrip": false,
|
| 1537 |
+
"single_word": false,
|
| 1538 |
+
"special": true
|
| 1539 |
+
},
|
| 1540 |
+
"128191": {
|
| 1541 |
+
"content": "<|_placeholder_191|>",
|
| 1542 |
+
"lstrip": false,
|
| 1543 |
+
"normalized": false,
|
| 1544 |
+
"rstrip": false,
|
| 1545 |
+
"single_word": false,
|
| 1546 |
+
"special": true
|
| 1547 |
+
},
|
| 1548 |
+
"128192": {
|
| 1549 |
+
"content": "<|_placeholder_192|>",
|
| 1550 |
+
"lstrip": false,
|
| 1551 |
+
"normalized": false,
|
| 1552 |
+
"rstrip": false,
|
| 1553 |
+
"single_word": false,
|
| 1554 |
+
"special": true
|
| 1555 |
+
},
|
| 1556 |
+
"128193": {
|
| 1557 |
+
"content": "<|_placeholder_193|>",
|
| 1558 |
+
"lstrip": false,
|
| 1559 |
+
"normalized": false,
|
| 1560 |
+
"rstrip": false,
|
| 1561 |
+
"single_word": false,
|
| 1562 |
+
"special": true
|
| 1563 |
+
},
|
| 1564 |
+
"128194": {
|
| 1565 |
+
"content": "<|_placeholder_194|>",
|
| 1566 |
+
"lstrip": false,
|
| 1567 |
+
"normalized": false,
|
| 1568 |
+
"rstrip": false,
|
| 1569 |
+
"single_word": false,
|
| 1570 |
+
"special": true
|
| 1571 |
+
},
|
| 1572 |
+
"128195": {
|
| 1573 |
+
"content": "<|_placeholder_195|>",
|
| 1574 |
+
"lstrip": false,
|
| 1575 |
+
"normalized": false,
|
| 1576 |
+
"rstrip": false,
|
| 1577 |
+
"single_word": false,
|
| 1578 |
+
"special": true
|
| 1579 |
+
},
|
| 1580 |
+
"128196": {
|
| 1581 |
+
"content": "<|_placeholder_196|>",
|
| 1582 |
+
"lstrip": false,
|
| 1583 |
+
"normalized": false,
|
| 1584 |
+
"rstrip": false,
|
| 1585 |
+
"single_word": false,
|
| 1586 |
+
"special": true
|
| 1587 |
+
},
|
| 1588 |
+
"128197": {
|
| 1589 |
+
"content": "<|_placeholder_197|>",
|
| 1590 |
+
"lstrip": false,
|
| 1591 |
+
"normalized": false,
|
| 1592 |
+
"rstrip": false,
|
| 1593 |
+
"single_word": false,
|
| 1594 |
+
"special": true
|
| 1595 |
+
},
|
| 1596 |
+
"128198": {
|
| 1597 |
+
"content": "<|_placeholder_198|>",
|
| 1598 |
+
"lstrip": false,
|
| 1599 |
+
"normalized": false,
|
| 1600 |
+
"rstrip": false,
|
| 1601 |
+
"single_word": false,
|
| 1602 |
+
"special": true
|
| 1603 |
+
},
|
| 1604 |
+
"128199": {
|
| 1605 |
+
"content": "<|_placeholder_199|>",
|
| 1606 |
+
"lstrip": false,
|
| 1607 |
+
"normalized": false,
|
| 1608 |
+
"rstrip": false,
|
| 1609 |
+
"single_word": false,
|
| 1610 |
+
"special": true
|
| 1611 |
+
},
|
| 1612 |
+
"128200": {
|
| 1613 |
+
"content": "<|_placeholder_200|>",
|
| 1614 |
+
"lstrip": false,
|
| 1615 |
+
"normalized": false,
|
| 1616 |
+
"rstrip": false,
|
| 1617 |
+
"single_word": false,
|
| 1618 |
+
"special": true
|
| 1619 |
+
},
|
| 1620 |
+
"128201": {
|
| 1621 |
+
"content": "<|_placeholder_201|>",
|
| 1622 |
+
"lstrip": false,
|
| 1623 |
+
"normalized": false,
|
| 1624 |
+
"rstrip": false,
|
| 1625 |
+
"single_word": false,
|
| 1626 |
+
"special": true
|
| 1627 |
+
},
|
| 1628 |
+
"128202": {
|
| 1629 |
+
"content": "<|_placeholder_202|>",
|
| 1630 |
+
"lstrip": false,
|
| 1631 |
+
"normalized": false,
|
| 1632 |
+
"rstrip": false,
|
| 1633 |
+
"single_word": false,
|
| 1634 |
+
"special": true
|
| 1635 |
+
},
|
| 1636 |
+
"128203": {
|
| 1637 |
+
"content": "<|_placeholder_203|>",
|
| 1638 |
+
"lstrip": false,
|
| 1639 |
+
"normalized": false,
|
| 1640 |
+
"rstrip": false,
|
| 1641 |
+
"single_word": false,
|
| 1642 |
+
"special": true
|
| 1643 |
+
},
|
| 1644 |
+
"128204": {
|
| 1645 |
+
"content": "<|_placeholder_204|>",
|
| 1646 |
+
"lstrip": false,
|
| 1647 |
+
"normalized": false,
|
| 1648 |
+
"rstrip": false,
|
| 1649 |
+
"single_word": false,
|
| 1650 |
+
"special": true
|
| 1651 |
+
},
|
| 1652 |
+
"128205": {
|
| 1653 |
+
"content": "<|_placeholder_205|>",
|
| 1654 |
+
"lstrip": false,
|
| 1655 |
+
"normalized": false,
|
| 1656 |
+
"rstrip": false,
|
| 1657 |
+
"single_word": false,
|
| 1658 |
+
"special": true
|
| 1659 |
+
},
|
| 1660 |
+
"128206": {
|
| 1661 |
+
"content": "<|_placeholder_206|>",
|
| 1662 |
+
"lstrip": false,
|
| 1663 |
+
"normalized": false,
|
| 1664 |
+
"rstrip": false,
|
| 1665 |
+
"single_word": false,
|
| 1666 |
+
"special": true
|
| 1667 |
+
},
|
| 1668 |
+
"128207": {
|
| 1669 |
+
"content": "<|_placeholder_207|>",
|
| 1670 |
+
"lstrip": false,
|
| 1671 |
+
"normalized": false,
|
| 1672 |
+
"rstrip": false,
|
| 1673 |
+
"single_word": false,
|
| 1674 |
+
"special": true
|
| 1675 |
+
},
|
| 1676 |
+
"128208": {
|
| 1677 |
+
"content": "<|_placeholder_208|>",
|
| 1678 |
+
"lstrip": false,
|
| 1679 |
+
"normalized": false,
|
| 1680 |
+
"rstrip": false,
|
| 1681 |
+
"single_word": false,
|
| 1682 |
+
"special": true
|
| 1683 |
+
},
|
| 1684 |
+
"128209": {
|
| 1685 |
+
"content": "<|_placeholder_209|>",
|
| 1686 |
+
"lstrip": false,
|
| 1687 |
+
"normalized": false,
|
| 1688 |
+
"rstrip": false,
|
| 1689 |
+
"single_word": false,
|
| 1690 |
+
"special": true
|
| 1691 |
+
},
|
| 1692 |
+
"128210": {
|
| 1693 |
+
"content": "<|_placeholder_210|>",
|
| 1694 |
+
"lstrip": false,
|
| 1695 |
+
"normalized": false,
|
| 1696 |
+
"rstrip": false,
|
| 1697 |
+
"single_word": false,
|
| 1698 |
+
"special": true
|
| 1699 |
+
},
|
| 1700 |
+
"128211": {
|
| 1701 |
+
"content": "<|_placeholder_211|>",
|
| 1702 |
+
"lstrip": false,
|
| 1703 |
+
"normalized": false,
|
| 1704 |
+
"rstrip": false,
|
| 1705 |
+
"single_word": false,
|
| 1706 |
+
"special": true
|
| 1707 |
+
},
|
| 1708 |
+
"128212": {
|
| 1709 |
+
"content": "<|_placeholder_212|>",
|
| 1710 |
+
"lstrip": false,
|
| 1711 |
+
"normalized": false,
|
| 1712 |
+
"rstrip": false,
|
| 1713 |
+
"single_word": false,
|
| 1714 |
+
"special": true
|
| 1715 |
+
},
|
| 1716 |
+
"128213": {
|
| 1717 |
+
"content": "<|_placeholder_213|>",
|
| 1718 |
+
"lstrip": false,
|
| 1719 |
+
"normalized": false,
|
| 1720 |
+
"rstrip": false,
|
| 1721 |
+
"single_word": false,
|
| 1722 |
+
"special": true
|
| 1723 |
+
},
|
| 1724 |
+
"128214": {
|
| 1725 |
+
"content": "<|_placeholder_214|>",
|
| 1726 |
+
"lstrip": false,
|
| 1727 |
+
"normalized": false,
|
| 1728 |
+
"rstrip": false,
|
| 1729 |
+
"single_word": false,
|
| 1730 |
+
"special": true
|
| 1731 |
+
},
|
| 1732 |
+
"128215": {
|
| 1733 |
+
"content": "<|_placeholder_215|>",
|
| 1734 |
+
"lstrip": false,
|
| 1735 |
+
"normalized": false,
|
| 1736 |
+
"rstrip": false,
|
| 1737 |
+
"single_word": false,
|
| 1738 |
+
"special": true
|
| 1739 |
+
},
|
| 1740 |
+
"128216": {
|
| 1741 |
+
"content": "<|_placeholder_216|>",
|
| 1742 |
+
"lstrip": false,
|
| 1743 |
+
"normalized": false,
|
| 1744 |
+
"rstrip": false,
|
| 1745 |
+
"single_word": false,
|
| 1746 |
+
"special": true
|
| 1747 |
+
},
|
| 1748 |
+
"128217": {
|
| 1749 |
+
"content": "<|_placeholder_217|>",
|
| 1750 |
+
"lstrip": false,
|
| 1751 |
+
"normalized": false,
|
| 1752 |
+
"rstrip": false,
|
| 1753 |
+
"single_word": false,
|
| 1754 |
+
"special": true
|
| 1755 |
+
},
|
| 1756 |
+
"128218": {
|
| 1757 |
+
"content": "<|_placeholder_218|>",
|
| 1758 |
+
"lstrip": false,
|
| 1759 |
+
"normalized": false,
|
| 1760 |
+
"rstrip": false,
|
| 1761 |
+
"single_word": false,
|
| 1762 |
+
"special": true
|
| 1763 |
+
},
|
| 1764 |
+
"128219": {
|
| 1765 |
+
"content": "<|_placeholder_219|>",
|
| 1766 |
+
"lstrip": false,
|
| 1767 |
+
"normalized": false,
|
| 1768 |
+
"rstrip": false,
|
| 1769 |
+
"single_word": false,
|
| 1770 |
+
"special": true
|
| 1771 |
+
},
|
| 1772 |
+
"128220": {
|
| 1773 |
+
"content": "<|_placeholder_220|>",
|
| 1774 |
+
"lstrip": false,
|
| 1775 |
+
"normalized": false,
|
| 1776 |
+
"rstrip": false,
|
| 1777 |
+
"single_word": false,
|
| 1778 |
+
"special": true
|
| 1779 |
+
},
|
| 1780 |
+
"128221": {
|
| 1781 |
+
"content": "<|_placeholder_221|>",
|
| 1782 |
+
"lstrip": false,
|
| 1783 |
+
"normalized": false,
|
| 1784 |
+
"rstrip": false,
|
| 1785 |
+
"single_word": false,
|
| 1786 |
+
"special": true
|
| 1787 |
+
},
|
| 1788 |
+
"128222": {
|
| 1789 |
+
"content": "<|_placeholder_222|>",
|
| 1790 |
+
"lstrip": false,
|
| 1791 |
+
"normalized": false,
|
| 1792 |
+
"rstrip": false,
|
| 1793 |
+
"single_word": false,
|
| 1794 |
+
"special": true
|
| 1795 |
+
},
|
| 1796 |
+
"128223": {
|
| 1797 |
+
"content": "<|_placeholder_223|>",
|
| 1798 |
+
"lstrip": false,
|
| 1799 |
+
"normalized": false,
|
| 1800 |
+
"rstrip": false,
|
| 1801 |
+
"single_word": false,
|
| 1802 |
+
"special": true
|
| 1803 |
+
},
|
| 1804 |
+
"128224": {
|
| 1805 |
+
"content": "<|_placeholder_224|>",
|
| 1806 |
+
"lstrip": false,
|
| 1807 |
+
"normalized": false,
|
| 1808 |
+
"rstrip": false,
|
| 1809 |
+
"single_word": false,
|
| 1810 |
+
"special": true
|
| 1811 |
+
},
|
| 1812 |
+
"128225": {
|
| 1813 |
+
"content": "<|_placeholder_225|>",
|
| 1814 |
+
"lstrip": false,
|
| 1815 |
+
"normalized": false,
|
| 1816 |
+
"rstrip": false,
|
| 1817 |
+
"single_word": false,
|
| 1818 |
+
"special": true
|
| 1819 |
+
},
|
| 1820 |
+
"128226": {
|
| 1821 |
+
"content": "<|_placeholder_226|>",
|
| 1822 |
+
"lstrip": false,
|
| 1823 |
+
"normalized": false,
|
| 1824 |
+
"rstrip": false,
|
| 1825 |
+
"single_word": false,
|
| 1826 |
+
"special": true
|
| 1827 |
+
},
|
| 1828 |
+
"128227": {
|
| 1829 |
+
"content": "<|_placeholder_227|>",
|
| 1830 |
+
"lstrip": false,
|
| 1831 |
+
"normalized": false,
|
| 1832 |
+
"rstrip": false,
|
| 1833 |
+
"single_word": false,
|
| 1834 |
+
"special": true
|
| 1835 |
+
},
|
| 1836 |
+
"128228": {
|
| 1837 |
+
"content": "<|_placeholder_228|>",
|
| 1838 |
+
"lstrip": false,
|
| 1839 |
+
"normalized": false,
|
| 1840 |
+
"rstrip": false,
|
| 1841 |
+
"single_word": false,
|
| 1842 |
+
"special": true
|
| 1843 |
+
},
|
| 1844 |
+
"128229": {
|
| 1845 |
+
"content": "<|_placeholder_229|>",
|
| 1846 |
+
"lstrip": false,
|
| 1847 |
+
"normalized": false,
|
| 1848 |
+
"rstrip": false,
|
| 1849 |
+
"single_word": false,
|
| 1850 |
+
"special": true
|
| 1851 |
+
},
|
| 1852 |
+
"128230": {
|
| 1853 |
+
"content": "<|_placeholder_230|>",
|
| 1854 |
+
"lstrip": false,
|
| 1855 |
+
"normalized": false,
|
| 1856 |
+
"rstrip": false,
|
| 1857 |
+
"single_word": false,
|
| 1858 |
+
"special": true
|
| 1859 |
+
},
|
| 1860 |
+
"128231": {
|
| 1861 |
+
"content": "<|_placeholder_231|>",
|
| 1862 |
+
"lstrip": false,
|
| 1863 |
+
"normalized": false,
|
| 1864 |
+
"rstrip": false,
|
| 1865 |
+
"single_word": false,
|
| 1866 |
+
"special": true
|
| 1867 |
+
},
|
| 1868 |
+
"128232": {
|
| 1869 |
+
"content": "<|_placeholder_232|>",
|
| 1870 |
+
"lstrip": false,
|
| 1871 |
+
"normalized": false,
|
| 1872 |
+
"rstrip": false,
|
| 1873 |
+
"single_word": false,
|
| 1874 |
+
"special": true
|
| 1875 |
+
},
|
| 1876 |
+
"128233": {
|
| 1877 |
+
"content": "<|_placeholder_233|>",
|
| 1878 |
+
"lstrip": false,
|
| 1879 |
+
"normalized": false,
|
| 1880 |
+
"rstrip": false,
|
| 1881 |
+
"single_word": false,
|
| 1882 |
+
"special": true
|
| 1883 |
+
},
|
| 1884 |
+
"128234": {
|
| 1885 |
+
"content": "<|_placeholder_234|>",
|
| 1886 |
+
"lstrip": false,
|
| 1887 |
+
"normalized": false,
|
| 1888 |
+
"rstrip": false,
|
| 1889 |
+
"single_word": false,
|
| 1890 |
+
"special": true
|
| 1891 |
+
},
|
| 1892 |
+
"128235": {
|
| 1893 |
+
"content": "<|_placeholder_235|>",
|
| 1894 |
+
"lstrip": false,
|
| 1895 |
+
"normalized": false,
|
| 1896 |
+
"rstrip": false,
|
| 1897 |
+
"single_word": false,
|
| 1898 |
+
"special": true
|
| 1899 |
+
},
|
| 1900 |
+
"128236": {
|
| 1901 |
+
"content": "<|_placeholder_236|>",
|
| 1902 |
+
"lstrip": false,
|
| 1903 |
+
"normalized": false,
|
| 1904 |
+
"rstrip": false,
|
| 1905 |
+
"single_word": false,
|
| 1906 |
+
"special": true
|
| 1907 |
+
},
|
| 1908 |
+
"128237": {
|
| 1909 |
+
"content": "<|_placeholder_237|>",
|
| 1910 |
+
"lstrip": false,
|
| 1911 |
+
"normalized": false,
|
| 1912 |
+
"rstrip": false,
|
| 1913 |
+
"single_word": false,
|
| 1914 |
+
"special": true
|
| 1915 |
+
},
|
| 1916 |
+
"128238": {
|
| 1917 |
+
"content": "<|_placeholder_238|>",
|
| 1918 |
+
"lstrip": false,
|
| 1919 |
+
"normalized": false,
|
| 1920 |
+
"rstrip": false,
|
| 1921 |
+
"single_word": false,
|
| 1922 |
+
"special": true
|
| 1923 |
+
},
|
| 1924 |
+
"128239": {
|
| 1925 |
+
"content": "<|_placeholder_239|>",
|
| 1926 |
+
"lstrip": false,
|
| 1927 |
+
"normalized": false,
|
| 1928 |
+
"rstrip": false,
|
| 1929 |
+
"single_word": false,
|
| 1930 |
+
"special": true
|
| 1931 |
+
},
|
| 1932 |
+
"128240": {
|
| 1933 |
+
"content": "<|_placeholder_240|>",
|
| 1934 |
+
"lstrip": false,
|
| 1935 |
+
"normalized": false,
|
| 1936 |
+
"rstrip": false,
|
| 1937 |
+
"single_word": false,
|
| 1938 |
+
"special": true
|
| 1939 |
+
},
|
| 1940 |
+
"128241": {
|
| 1941 |
+
"content": "<|_placeholder_241|>",
|
| 1942 |
+
"lstrip": false,
|
| 1943 |
+
"normalized": false,
|
| 1944 |
+
"rstrip": false,
|
| 1945 |
+
"single_word": false,
|
| 1946 |
+
"special": true
|
| 1947 |
+
},
|
| 1948 |
+
"128242": {
|
| 1949 |
+
"content": "<|_placeholder_242|>",
|
| 1950 |
+
"lstrip": false,
|
| 1951 |
+
"normalized": false,
|
| 1952 |
+
"rstrip": false,
|
| 1953 |
+
"single_word": false,
|
| 1954 |
+
"special": true
|
| 1955 |
+
},
|
| 1956 |
+
"128243": {
|
| 1957 |
+
"content": "<|_placeholder_243|>",
|
| 1958 |
+
"lstrip": false,
|
| 1959 |
+
"normalized": false,
|
| 1960 |
+
"rstrip": false,
|
| 1961 |
+
"single_word": false,
|
| 1962 |
+
"special": true
|
| 1963 |
+
},
|
| 1964 |
+
"128244": {
|
| 1965 |
+
"content": "<|_placeholder_244|>",
|
| 1966 |
+
"lstrip": false,
|
| 1967 |
+
"normalized": false,
|
| 1968 |
+
"rstrip": false,
|
| 1969 |
+
"single_word": false,
|
| 1970 |
+
"special": true
|
| 1971 |
+
},
|
| 1972 |
+
"128245": {
|
| 1973 |
+
"content": "<|_placeholder_245|>",
|
| 1974 |
+
"lstrip": false,
|
| 1975 |
+
"normalized": false,
|
| 1976 |
+
"rstrip": false,
|
| 1977 |
+
"single_word": false,
|
| 1978 |
+
"special": true
|
| 1979 |
+
},
|
| 1980 |
+
"128246": {
|
| 1981 |
+
"content": "<|_placeholder_246|>",
|
| 1982 |
+
"lstrip": false,
|
| 1983 |
+
"normalized": false,
|
| 1984 |
+
"rstrip": false,
|
| 1985 |
+
"single_word": false,
|
| 1986 |
+
"special": true
|
| 1987 |
+
},
|
| 1988 |
+
"128247": {
|
| 1989 |
+
"content": "<|_placeholder_247|>",
|
| 1990 |
+
"lstrip": false,
|
| 1991 |
+
"normalized": false,
|
| 1992 |
+
"rstrip": false,
|
| 1993 |
+
"single_word": false,
|
| 1994 |
+
"special": true
|
| 1995 |
+
},
|
| 1996 |
+
"128248": {
|
| 1997 |
+
"content": "<|_placeholder_248|>",
|
| 1998 |
+
"lstrip": false,
|
| 1999 |
+
"normalized": false,
|
| 2000 |
+
"rstrip": false,
|
| 2001 |
+
"single_word": false,
|
| 2002 |
+
"special": true
|
| 2003 |
+
},
|
| 2004 |
+
"128249": {
|
| 2005 |
+
"content": "<|_placeholder_249|>",
|
| 2006 |
+
"lstrip": false,
|
| 2007 |
+
"normalized": false,
|
| 2008 |
+
"rstrip": false,
|
| 2009 |
+
"single_word": false,
|
| 2010 |
+
"special": true
|
| 2011 |
+
},
|
| 2012 |
+
"128250": {
|
| 2013 |
+
"content": "<|_placeholder_250|>",
|
| 2014 |
+
"lstrip": false,
|
| 2015 |
+
"normalized": false,
|
| 2016 |
+
"rstrip": false,
|
| 2017 |
+
"single_word": false,
|
| 2018 |
+
"special": true
|
| 2019 |
+
},
|
| 2020 |
+
"128251": {
|
| 2021 |
+
"content": "<|_placeholder_251|>",
|
| 2022 |
+
"lstrip": false,
|
| 2023 |
+
"normalized": false,
|
| 2024 |
+
"rstrip": false,
|
| 2025 |
+
"single_word": false,
|
| 2026 |
+
"special": true
|
| 2027 |
+
},
|
| 2028 |
+
"128252": {
|
| 2029 |
+
"content": "<|_placeholder_252|>",
|
| 2030 |
+
"lstrip": false,
|
| 2031 |
+
"normalized": false,
|
| 2032 |
+
"rstrip": false,
|
| 2033 |
+
"single_word": false,
|
| 2034 |
+
"special": true
|
| 2035 |
+
},
|
| 2036 |
+
"128253": {
|
| 2037 |
+
"content": "<|_placeholder_253|>",
|
| 2038 |
+
"lstrip": false,
|
| 2039 |
+
"normalized": false,
|
| 2040 |
+
"rstrip": false,
|
| 2041 |
+
"single_word": false,
|
| 2042 |
+
"special": true
|
| 2043 |
+
},
|
| 2044 |
+
"128254": {
|
| 2045 |
+
"content": "<|_placeholder_254|>",
|
| 2046 |
+
"lstrip": false,
|
| 2047 |
+
"normalized": false,
|
| 2048 |
+
"rstrip": false,
|
| 2049 |
+
"single_word": false,
|
| 2050 |
+
"special": true
|
| 2051 |
+
},
|
| 2052 |
+
"128255": {
|
| 2053 |
+
"content": "<|_placeholder_255|>",
|
| 2054 |
+
"lstrip": false,
|
| 2055 |
+
"normalized": false,
|
| 2056 |
+
"rstrip": false,
|
| 2057 |
+
"single_word": false,
|
| 2058 |
+
"special": true
|
| 2059 |
+
}
|
| 2060 |
+
},
|
| 2061 |
+
"auto_map": {
|
| 2062 |
+
"AutoProcessor": "processing_vlm.HCXVisionV2Processor"
|
| 2063 |
+
},
|
| 2064 |
+
"bos_token": "<|endoftext|>",
|
| 2065 |
+
"clean_up_tokenization_spaces": true,
|
| 2066 |
+
"eos_token": "<|im_end|>",
|
| 2067 |
+
"extra_special_tokens": {
|
| 2068 |
+
"image_token": "<|IMAGE_PAD|>",
|
| 2069 |
+
"video_token": "<|VIDEO_PAD|>"
|
| 2070 |
+
},
|
| 2071 |
+
"image_token": "<|IMAGE_PAD|>",
|
| 2072 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 2073 |
+
"pad_token": "<|endoftext|>",
|
| 2074 |
+
"processor_class": "HCXVisionV2Processor",
|
| 2075 |
+
"sep_token": "<|endoftext|>",
|
| 2076 |
+
"tokenizer_class": "GPT2Tokenizer",
|
| 2077 |
+
"unk_token": "<|endoftext|>",
|
| 2078 |
+
"video_token": "<|VIDEO_PAD|>"
|
| 2079 |
+
}
|
vocab.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|