Spaces:
Sleeping
Sleeping
File size: 15,489 Bytes
81cf53b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# %%capture\n",
"# !pip install huggingface-hub hf-transfer langchain llama-cpp-python langchain-community"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from langchain.callbacks.manager import CallbackManager\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
"from langchain.chains import LLMChain\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain_community.llms import LlamaCpp\n",
"\n",
"import gradio as gr "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def build_llm_chain():\n",
"\n",
" MODEL_PATH = \"../models/llama-2-7b-chat.Q5_K_M.gguf\"\n",
"\n",
" template = \"\"\"\n",
"\n",
" You are a helpful AI Assistant created by Mohammed Vasim. He is an AI Engineer and Specialist.\n",
" \n",
" Question: {question}\n",
"\n",
" Answer: helpful answer\"\"\"\n",
"\n",
" prompt = PromptTemplate.from_template(template)\n",
"\n",
" # Callbacks support token-wise streaming\n",
" callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
"\n",
" # Make sure the model path is correct for your system!\n",
" llm = LlamaCpp(\n",
" model_path=MODEL_PATH,\n",
" temperature=0.75,\n",
" max_tokens=2000,\n",
" top_p=1,\n",
" callback_manager=callback_manager,\n",
" verbose=True, # Verbose is required to pass to the callback manager\n",
" )\n",
"\n",
" llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
"\n",
" # question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
" # llm_chain.run(question)\n",
"\n",
" return llm_chain"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from ../models/llama-2-7b-chat.Q5_K_M.gguf (version GGUF V2)\n",
"llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
"llama_model_loader: - kv 0: general.architecture str = llama\n",
"llama_model_loader: - kv 1: general.name str = LLaMA v2\n",
"llama_model_loader: - kv 2: llama.context_length u32 = 4096\n",
"llama_model_loader: - kv 3: llama.embedding_length u32 = 4096\n",
"llama_model_loader: - kv 4: llama.block_count u32 = 32\n",
"llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008\n",
"llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128\n",
"llama_model_loader: - kv 7: llama.attention.head_count u32 = 32\n",
"llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32\n",
"llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001\n",
"llama_model_loader: - kv 10: general.file_type u32 = 17\n",
"llama_model_loader: - kv 11: tokenizer.ggml.model str = llama\n",
"llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
"llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...\n",
"llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
"llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1\n",
"llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2\n",
"llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0\n",
"llama_model_loader: - kv 18: general.quantization_version u32 = 2\n",
"llama_model_loader: - type f32: 65 tensors\n",
"llama_model_loader: - type q5_K: 193 tensors\n",
"llama_model_loader: - type q6_K: 33 tensors\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n",
"llm_load_print_meta: format = GGUF V2\n",
"llm_load_print_meta: arch = llama\n",
"llm_load_print_meta: vocab type = SPM\n",
"llm_load_print_meta: n_vocab = 32000\n",
"llm_load_print_meta: n_merges = 0\n",
"llm_load_print_meta: n_ctx_train = 4096\n",
"llm_load_print_meta: n_embd = 4096\n",
"llm_load_print_meta: n_head = 32\n",
"llm_load_print_meta: n_head_kv = 32\n",
"llm_load_print_meta: n_layer = 32\n",
"llm_load_print_meta: n_rot = 128\n",
"llm_load_print_meta: n_embd_head_k = 128\n",
"llm_load_print_meta: n_embd_head_v = 128\n",
"llm_load_print_meta: n_gqa = 1\n",
"llm_load_print_meta: n_embd_k_gqa = 4096\n",
"llm_load_print_meta: n_embd_v_gqa = 4096\n",
"llm_load_print_meta: f_norm_eps = 0.0e+00\n",
"llm_load_print_meta: f_norm_rms_eps = 1.0e-06\n",
"llm_load_print_meta: f_clamp_kqv = 0.0e+00\n",
"llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
"llm_load_print_meta: n_ff = 11008\n",
"llm_load_print_meta: n_expert = 0\n",
"llm_load_print_meta: n_expert_used = 0\n",
"llm_load_print_meta: rope scaling = linear\n",
"llm_load_print_meta: freq_base_train = 10000.0\n",
"llm_load_print_meta: freq_scale_train = 1\n",
"llm_load_print_meta: n_yarn_orig_ctx = 4096\n",
"llm_load_print_meta: rope_finetuned = unknown\n",
"llm_load_print_meta: model type = 7B\n",
"llm_load_print_meta: model ftype = Q5_K - Medium\n",
"llm_load_print_meta: model params = 6.74 B\n",
"llm_load_print_meta: model size = 4.45 GiB (5.68 BPW) \n",
"llm_load_print_meta: general.name = LLaMA v2\n",
"llm_load_print_meta: BOS token = 1 '<s>'\n",
"llm_load_print_meta: EOS token = 2 '</s>'\n",
"llm_load_print_meta: UNK token = 0 '<unk>'\n",
"llm_load_print_meta: LF token = 13 '<0x0A>'\n",
"llm_load_tensors: ggml ctx size = 0.11 MiB\n",
"llm_load_tensors: CPU buffer size = 4560.87 MiB\n",
"...................................................................................................\n",
"llama_new_context_with_model: n_ctx = 512\n",
"llama_new_context_with_model: freq_base = 10000.0\n",
"llama_new_context_with_model: freq_scale = 1\n",
"llama_kv_cache_init: CPU KV buffer size = 256.00 MiB\n",
"llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB\n",
"llama_new_context_with_model: CPU input buffer size = 0.14 MiB\n",
"llama_new_context_with_model: CPU compute buffer size = 1.10 MiB\n",
"llama_new_context_with_model: graph splits (measure): 1\n",
"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | \n",
"Model metadata: {'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.context_length': '4096', 'general.name': 'LLaMA v2', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '11008', 'llama.attention.layer_norm_rms_epsilon': '0.000001', 'llama.rope.dimension_count': '128', 'llama.attention.head_count': '32', 'tokenizer.ggml.bos_token_id': '1', 'llama.block_count': '32', 'llama.attention.head_count_kv': '32', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '17'}\n"
]
},
{
"ename": "ValidationError",
"evalue": "1 validation error for LlamaCpp\ncallback_manager\n instance of BaseCallbackManager expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseCallbackManager)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m title \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWelcome Open Source LLM\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3\u001b[0m description \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis is a Llama-2-GGUF\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m chain \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_llm_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manswer_query\u001b[39m(message, history):\n\u001b[1;32m 8\u001b[0m message \u001b[38;5;241m=\u001b[39m chain\u001b[38;5;241m.\u001b[39mrun(message)\n",
"Cell \u001b[0;32mIn[8], line 19\u001b[0m, in \u001b[0;36mbuild_llm_chain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m callback_manager \u001b[38;5;241m=\u001b[39m CallbackManager([StreamingStdOutCallbackHandler()])\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# Make sure the model path is correct for your system!\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m llm \u001b[38;5;241m=\u001b[39m \u001b[43mLlamaCpp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mMODEL_PATH\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.75\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2000\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallback_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Verbose is required to pass to the callback manager\u001b[39;49;00m\n\u001b[1;32m 26\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m llm_chain \u001b[38;5;241m=\u001b[39m LLMChain(prompt\u001b[38;5;241m=\u001b[39mprompt, llm\u001b[38;5;241m=\u001b[39mllm)\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\u001b[39;00m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# llm_chain.run(question)\u001b[39;00m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/langchain_core/load/serializable.py:107\u001b[0m, in \u001b[0;36mSerializable.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 107\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lc_kwargs \u001b[38;5;241m=\u001b[39m kwargs\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pydantic/v1/main.py:341\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(__pydantic_self__, **data)\u001b[0m\n\u001b[1;32m 339\u001b[0m values, fields_set, validation_error \u001b[38;5;241m=\u001b[39m validate_model(__pydantic_self__\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, data)\n\u001b[1;32m 340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validation_error:\n\u001b[0;32m--> 341\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m validation_error\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 343\u001b[0m object_setattr(__pydantic_self__, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__dict__\u001b[39m\u001b[38;5;124m'\u001b[39m, values)\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for LlamaCpp\ncallback_manager\n instance of BaseCallbackManager expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseCallbackManager)"
]
}
],
"source": [
"\n",
"title = \"Welcome Open Source LLM\"\n",
"\n",
"description = \"This is a Llama-2-GGUF\"\n",
"\n",
"chain = build_llm_chain()\n",
"\n",
"def answer_query(message, history):\n",
" message = chain.run(message)\n",
" return message \n",
"\n",
"# Gradio chat interface\n",
"gr.ChatInterface(\n",
" fn=answer_query,\n",
" title=title,\n",
" description=description,\n",
" additional_inputs=[gr.Textbox(\"You are helpful assistant.\")],\n",
" additional_inputs_accordion=\"📝 System prompt\",\n",
" examples=[\n",
" [\"What is a Large Language Model?\"],\n",
" [\"What's 9+2-1?\"],\n",
" [\"Write Python code to print the Fibonacci sequence\"]\n",
" ]\n",
").queue().launch(server_name=\"0.0.0.0\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.0.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|