diff --git "a/main.log" "b/main.log"
new file mode 100644--- /dev/null
+++ "b/main.log"
@@ -0,0 +1,2169 @@
+[1709240349] Log start
+[1709240349] Cmd: /main -m phigment6-slerp_Q3_K_M.gguf -p "What is a Large Language Model?" -n 512 --temp 1
+[1709240349] main: build = 0 (unknown)
+[1709240349] main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
+[1709240349] main: seed  = 1709240349
+[1709240349] main: llama backend init
+[1709240349] main: load the model and apply lora adapter, if any
+[1709240350] llama_model_loader: loaded meta data with 21 key-value pairs and 453 tensors from phigment6-slerp_Q3_K_M.gguf (version GGUF V3 (latest))
+[1709240350] llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+[1709240350] llama_model_loader: - kv   0:                       general.architecture str              = phi2
+[1709240350] llama_model_loader: - kv   1:                               general.name str              = Phi2
+[1709240350] llama_model_loader: - kv   2:                        phi2.context_length u32              = 2048
+[1709240350] llama_model_loader: - kv   3:                      phi2.embedding_length u32              = 2560
+[1709240350] llama_model_loader: - kv   4:                   phi2.feed_forward_length u32              = 10240
+[1709240350] llama_model_loader: - kv   5:                           phi2.block_count u32              = 32
+[1709240350] llama_model_loader: - kv   6:                  phi2.attention.head_count u32              = 32
+[1709240350] llama_model_loader: - kv   7:               phi2.attention.head_count_kv u32              = 32
+[1709240350] llama_model_loader: - kv   8:          phi2.attention.layer_norm_epsilon f32              = 0.000010
+[1709240350] llama_model_loader: - kv   9:                  phi2.rope.dimension_count u32              = 32
+[1709240350] llama_model_loader: - kv  10:                          general.file_type u32              = 12
+[1709240350] llama_model_loader: - kv  11:               tokenizer.ggml.add_bos_token bool             = false
+[1709240350] llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = gpt2
+[1709240350] llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,51200]   = ["!", "\"", "#", "$", "%", "&", "'", ...
+[1709240350] llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,51200]   = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+[1709240350] llama_model_loader: - kv  15:                      tokenizer.ggml.merges arr[str,50000]   = ["Ġ t", "Ġ a", "h e", "i n", "r e",...
+[1709240350] llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 50256
+[1709240350] llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 50295
+[1709240350] llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 50256
+[1709240350] llama_model_loader: - kv  19:            tokenizer.ggml.padding_token_id u32              = 50256
+[1709240350] llama_model_loader: - kv  20:               general.quantization_version u32              = 2
+[1709240350] llama_model_loader: - type  f32:  259 tensors
+[1709240350] llama_model_loader: - type q3_K:   97 tensors
+[1709240350] llama_model_loader: - type q4_K:   90 tensors
+[1709240350] llama_model_loader: - type q5_K:    6 tensors
+[1709240350] llama_model_loader: - type q6_K:    1 tensors
+[1709240350] llm_load_vocab: mismatch in special tokens definition ( 910/51200 vs 944/51200 ).
+[1709240350] llm_load_print_meta: format           = GGUF V3 (latest)
+[1709240350] llm_load_print_meta: arch             = phi2
+[1709240350] llm_load_print_meta: vocab type       = BPE
+[1709240350] llm_load_print_meta: n_vocab          = 51200
+[1709240350] llm_load_print_meta: n_merges         = 50000
+[1709240350] llm_load_print_meta: n_ctx_train      = 2048
+[1709240350] llm_load_print_meta: n_embd           = 2560
+[1709240350] llm_load_print_meta: n_head           = 32
+[1709240350] llm_load_print_meta: n_head_kv        = 32
+[1709240350] llm_load_print_meta: n_layer          = 32
+[1709240350] llm_load_print_meta: n_rot            = 32
+[1709240350] llm_load_print_meta: n_embd_head_k    = 80
+[1709240350] llm_load_print_meta: n_embd_head_v    = 80
+[1709240350] llm_load_print_meta: n_gqa            = 1
+[1709240350] llm_load_print_meta: n_embd_k_gqa     = 2560
+[1709240350] llm_load_print_meta: n_embd_v_gqa     = 2560
+[1709240350] llm_load_print_meta: f_norm_eps       = 1.0e-05
+[1709240350] llm_load_print_meta: f_norm_rms_eps   = 0.0e+00
+[1709240350] llm_load_print_meta: f_clamp_kqv      = 0.0e+00
+[1709240350] llm_load_print_meta: f_max_alibi_bias = 0.0e+00
+[1709240350] llm_load_print_meta: n_ff             = 10240
+[1709240350] llm_load_print_meta: n_expert         = 0
+[1709240350] llm_load_print_meta: n_expert_used    = 0
+[1709240350] llm_load_print_meta: pooling type     = 0
+[1709240350] llm_load_print_meta: rope type        = 2
+[1709240350] llm_load_print_meta: rope scaling     = linear
+[1709240350] llm_load_print_meta: freq_base_train  = 10000.0
+[1709240350] llm_load_print_meta: freq_scale_train = 1
+[1709240350] llm_load_print_meta: n_yarn_orig_ctx  = 2048
+[1709240350] llm_load_print_meta: rope_finetuned   = unknown
+[1709240350] llm_load_print_meta: model type       = 3B
+[1709240350] llm_load_print_meta: model ftype      = Q3_K - Medium
+[1709240350] llm_load_print_meta: model params     = 2.78 B
+[1709240350] llm_load_print_meta: model size       = 1.33 GiB (4.12 BPW) 
+[1709240350] llm_load_print_meta: general.name     = Phi2
+[1709240350] llm_load_print_meta: BOS token        = 50256 '<|endoftext|>'
+[1709240350] llm_load_print_meta: EOS token        = 50295 '<|im_end|>'
+[1709240350] llm_load_print_meta: UNK token        = 50256 '<|endoftext|>'
+[1709240350] llm_load_print_meta: PAD token        = 50256 '<|endoftext|>'
+[1709240350] llm_load_print_meta: LF token         = 128 'Ä'
+[1709240350] llm_load_tensors: ggml ctx size =    0.17 MiB
+[1709240354] llm_load_tensors:        CPU buffer size =  1364.59 MiB
+[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] .[1709240354] 
+[1709240354] llama_new_context_with_model: n_ctx      = 512
+[1709240354] llama_new_context_with_model: freq_base  = 10000.0
+[1709240354] llama_new_context_with_model: freq_scale = 1
+[1709240354] llama_kv_cache_init:        CPU KV buffer size =   160.00 MiB
+[1709240354] llama_new_context_with_model: KV self size  =  160.00 MiB, K (f16):   80.00 MiB, V (f16):   80.00 MiB
+[1709240354] llama_new_context_with_model:        CPU input buffer size   =     7.01 MiB
+[1709240354] llama_new_context_with_model:        CPU compute buffer size =   105.00 MiB
+[1709240354] llama_new_context_with_model: graph splits (measure): 1
+[1709240354] warming up the model with an empty run
+[1709240354] n_ctx: 512
+[1709240354] 
+[1709240354] system_info: n_threads = 16 / 32 | 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 | 
+[1709240354] add_bos: 0
+[1709240354] tokenize the prompt
+[1709240354] prompt: "What is a Large Language Model?"
+[1709240354] tokens: [ 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30 ]
+[1709240354] recalculate the cached logits (check): embd_inp.empty() false, n_matching_session_tokens 0, embd_inp.size() 7, session_tokens.size() 0, embd_inp.size() 7
+[1709240354] inp_pfx: [ '':628, '###':21017, ' Instruction':46486, ':':25, '':628 ]
+[1709240354] inp_sfx: [ '':628, '###':21017, ' Response':18261, ':':25, '':628 ]
+[1709240354] cml_pfx: [ '':198, '<|im_start|>':50296, 'user':7220, '':198 ]
+[1709240354] cml_sfx: [ '':50295, '':198, '<|im_start|>':50296, 'ass':562, 'istant':10167, '':198 ]
+[1709240354] sampling: 
+	repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
+	top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 1.000
+	mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
+[1709240354] sampling order: 
+CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature 
+[1709240354] generate: n_ctx = 512, n_batch = 512, n_predict = 512, n_keep = 0
+[1709240354] 
+
+[1709240354] embd_inp.size(): 7, n_consumed: 0
+[1709240354] eval: [ 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30 ]
+[1709240354] n_past = 7
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198 ]
+[1709240354] n_remain: 511
+[1709240354] eval: [ '':198 ]
+[1709240354] n_past = 8
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12 ]
+[1709240354] n_remain: 510
+[1709240354] eval: [ '-':12 ]
+[1709240354] n_past = 9
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317 ]
+[1709240354] n_remain: 509
+[1709240354] eval: [ ' A':317 ]
+[1709240354] n_past = 10
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317, ' large':1588 ]
+[1709240354] n_remain: 508
+[1709240354] eval: [ ' large':1588 ]
+[1709240354] n_past = 11
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317, ' large':1588, ' language':3303 ]
+[1709240354] n_remain: 507
+[1709240354] eval: [ ' language':3303 ]
+[1709240354] n_past = 12
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317, ' large':1588, ' language':3303, ' model':2746 ]
+[1709240354] n_remain: 506
+[1709240354] eval: [ ' model':2746 ]
+[1709240354] n_past = 13
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317, ' large':1588, ' language':3303, ' model':2746, ' is':318 ]
+[1709240354] n_remain: 505
+[1709240354] eval: [ ' is':318 ]
+[1709240354] n_past = 14
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317, ' large':1588, ' language':3303, ' model':2746, ' is':318, ' a':257 ]
+[1709240354] n_remain: 504
+[1709240354] eval: [ ' a':257 ]
+[1709240354] n_past = 15
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317, ' large':1588, ' language':3303, ' model':2746, ' is':318, ' a':257, ' deep':2769 ]
+[1709240354] n_remain: 503
+[1709240354] eval: [ ' deep':2769 ]
+[1709240354] n_past = 16
+[1709240354] last: [ '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, '!':0, 'What':2061, ' is':318, ' a':257, ' Large':13601, ' Language':15417, ' Model':9104, '?':30, '':198, '-':12, ' A':317, ' large':1588, ' language':3303, ' model':2746, ' is':318, ' a':257, ' deep':2769, ' neural':17019 ]
+[1709240354] n_remain: 502
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+[1709240372] after swap: n_past = 256, n_past_guidance = 0
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+[1709240372] n_remain: 0
+[1709240372] 
+[1709240372] llama_print_timings:        load time =    4291.49 ms
+[1709240372] llama_print_timings:      sample time =     286.05 ms /   512 runs   (    0.56 ms per token,  1789.91 tokens per second)
+[1709240372] llama_print_timings: prompt eval time =     121.80 ms /     7 tokens (   17.40 ms per token,    57.47 tokens per second)
+[1709240372] llama_print_timings:        eval time =   17381.92 ms /   511 runs   (   34.02 ms per token,    29.40 tokens per second)
+[1709240372] llama_print_timings:       total time =   18092.58 ms /   518 tokens
+[1709240372] Log end