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docs: Remove non-rendering images & output spamming from doc ntbks (#19475)
Looking at tokens / page of our docs, we see a few outliers: <img width="761" alt="image" src="https://github.com/langchain-ai/langchain/assets/122662504/677aa2d6-0a29-45e4-882a-db2bbf46d02b"> It is due to non-rendering images in one case, and output spamming. Clean these, along with other cases of excessing output spamming in docs. All get sucked into chat-langchain for retrieval.
This commit is contained in:
Lance Martin
GitHub
parent
b617085af0
commit
db7403d667
@ -227,373 +227,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "07c0d04e",
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"execution_count": null,
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"id": "18d10bc3-ede6-4410-a867-7c623a0efdb8",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from /home/martin/Models/llama-2-7b-chat.Q4_0.gguf (version GGUF V2)\n",
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"llama_model_loader: - tensor 0: token_embd.weight q4_0 [ 4096, 32000, 1, 1 ]\n",
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"llama_model_loader: - tensor 2: blk.0.ffn_down.weight q4_0 [ 11008, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q4_0 [ 4096, 11008, 1, 1 ]\n",
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"llama_model_loader: - tensor 4: blk.0.ffn_up.weight q4_0 [ 4096, 11008, 1, 1 ]\n",
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"llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 18: blk.1.attn_v.weight q4_0 [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 19: blk.10.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 217: output.weight q6_K [ 4096, 32000, 1, 1 ]\n",
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"llama_model_loader: - tensor 218: blk.24.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 236: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 239: blk.26.ffn_up.weight q4_0 [ 4096, 11008, 1, 1 ]\n",
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"llama_model_loader: - tensor 245: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 254: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 256: blk.28.ffn_gate.weight q4_0 [ 4096, 11008, 1, 1 ]\n",
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"llama_model_loader: - tensor 263: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 272: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 278: blk.30.attn_output.weight q4_0 [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 281: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
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"llama_model_loader: - tensor 282: blk.31.ffn_down.weight q4_0 [ 11008, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 283: blk.31.ffn_gate.weight q4_0 [ 4096, 11008, 1, 1 ]\n",
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"llama_model_loader: - tensor 284: blk.31.ffn_up.weight q4_0 [ 4096, 11008, 1, 1 ]\n",
|
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"llama_model_loader: - tensor 285: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 286: blk.31.attn_k.weight q4_0 [ 4096, 4096, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 287: blk.31.attn_output.weight q4_0 [ 4096, 4096, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 288: blk.31.attn_q.weight q4_0 [ 4096, 4096, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 289: blk.31.attn_v.weight q4_0 [ 4096, 4096, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 290: output_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - kv 0: general.architecture str \n",
|
||||
"llama_model_loader: - kv 1: general.name str \n",
|
||||
"llama_model_loader: - kv 2: llama.context_length u32 \n",
|
||||
"llama_model_loader: - kv 3: llama.embedding_length u32 \n",
|
||||
"llama_model_loader: - kv 4: llama.block_count u32 \n",
|
||||
"llama_model_loader: - kv 5: llama.feed_forward_length u32 \n",
|
||||
"llama_model_loader: - kv 6: llama.rope.dimension_count u32 \n",
|
||||
"llama_model_loader: - kv 7: llama.attention.head_count u32 \n",
|
||||
"llama_model_loader: - kv 8: llama.attention.head_count_kv u32 \n",
|
||||
"llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 \n",
|
||||
"llama_model_loader: - kv 10: general.file_type u32 \n",
|
||||
"llama_model_loader: - kv 11: tokenizer.ggml.model str \n",
|
||||
"llama_model_loader: - kv 12: tokenizer.ggml.tokens arr \n",
|
||||
"llama_model_loader: - kv 13: tokenizer.ggml.scores arr \n",
|
||||
"llama_model_loader: - kv 14: tokenizer.ggml.token_type arr \n",
|
||||
"llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 \n",
|
||||
"llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 \n",
|
||||
"llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 \n",
|
||||
"llama_model_loader: - kv 18: general.quantization_version u32 \n",
|
||||
"llama_model_loader: - type f32: 65 tensors\n",
|
||||
"llama_model_loader: - type q4_0: 225 tensors\n",
|
||||
"llama_model_loader: - type q6_K: 1 tensors\n",
|
||||
"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_gqa = 1\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: 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 = mostly Q4_0\n",
|
||||
"llm_load_print_meta: model params = 6.74 B\n",
|
||||
"llm_load_print_meta: model size = 3.56 GiB (4.54 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 MB\n",
|
||||
"llm_load_tensors: mem required = 3647.97 MB\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_new_context_with_model: kv self size = 256.00 MB\n",
|
||||
"llama_build_graph: non-view tensors processed: 740/740\n",
|
||||
"llama_new_context_with_model: compute buffer total size = 2.66 MB\n",
|
||||
"AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 0 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from os.path import expanduser\n",
|
||||
"\n",
|
||||
@ -731,7 +368,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.11.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
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|
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@ -375,490 +375,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"llama_model_loader: loaded meta data with 17 key-value pairs and 363 tensors from /Users/rlm/Desktop/Code/llama/code-llama/codellama-13b-instruct.Q4_K_M.gguf (version GGUF V1 (latest))\n",
|
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"llama_model_loader: - tensor 0: token_embd.weight q4_0 [ 5120, 32016, 1, 1 ]\n",
|
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"llama_model_loader: - tensor 1: output_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 2: output.weight f16 [ 5120, 32016, 1, 1 ]\n",
|
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"llama_model_loader: - tensor 3: blk.0.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 4: blk.0.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 5: blk.0.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 6: blk.0.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 7: blk.0.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 8: blk.0.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 9: blk.0.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 10: blk.0.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 11: blk.0.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 12: blk.1.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 13: blk.1.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 14: blk.1.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 15: blk.1.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 16: blk.1.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 17: blk.1.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 18: blk.1.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 19: blk.1.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 24: blk.2.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 25: blk.2.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 26: blk.2.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 27: blk.2.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 28: blk.2.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 35: blk.3.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 36: blk.3.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 39: blk.4.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 43: blk.4.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 51: blk.5.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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"llama_model_loader: - tensor 52: blk.5.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
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"llama_model_loader: - tensor 56: blk.5.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 57: blk.6.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
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|
||||
"llama_model_loader: - tensor 308: blk.33.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 309: blk.34.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 310: blk.34.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 311: blk.34.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 312: blk.34.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 313: blk.34.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 314: blk.34.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 315: blk.34.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 316: blk.34.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 317: blk.34.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 318: blk.35.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
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"llama_model_loader: - tensor 319: blk.35.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 320: blk.35.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 321: blk.35.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 322: blk.35.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 323: blk.35.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 324: blk.35.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 325: blk.35.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 326: blk.35.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 327: blk.36.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 328: blk.36.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
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"llama_model_loader: - tensor 329: blk.36.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 330: blk.36.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 331: blk.36.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 332: blk.36.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 333: blk.36.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 334: blk.36.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 335: blk.36.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 336: blk.37.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 337: blk.37.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 338: blk.37.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 339: blk.37.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 340: blk.37.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 341: blk.37.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 342: blk.37.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 343: blk.37.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 344: blk.37.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 345: blk.38.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 346: blk.38.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 347: blk.38.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 348: blk.38.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 349: blk.38.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 350: blk.38.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 351: blk.38.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 352: blk.38.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 353: blk.38.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 354: blk.39.attn_q.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 355: blk.39.attn_k.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 356: blk.39.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 357: blk.39.attn_output.weight q4_K [ 5120, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 358: blk.39.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 359: blk.39.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 360: blk.39.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 361: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - tensor 362: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - kv 0: general.architecture str \n",
|
||||
"llama_model_loader: - kv 1: general.name str \n",
|
||||
"llama_model_loader: - kv 2: llama.context_length u32 \n",
|
||||
"llama_model_loader: - kv 3: llama.embedding_length u32 \n",
|
||||
"llama_model_loader: - kv 4: llama.block_count u32 \n",
|
||||
"llama_model_loader: - kv 5: llama.feed_forward_length u32 \n",
|
||||
"llama_model_loader: - kv 6: llama.rope.dimension_count u32 \n",
|
||||
"llama_model_loader: - kv 7: llama.attention.head_count u32 \n",
|
||||
"llama_model_loader: - kv 8: llama.attention.head_count_kv u32 \n",
|
||||
"llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 \n",
|
||||
"llama_model_loader: - kv 10: llama.rope.freq_base f32 \n",
|
||||
"llama_model_loader: - kv 11: general.file_type u32 \n",
|
||||
"llama_model_loader: - kv 12: tokenizer.ggml.model str \n",
|
||||
"llama_model_loader: - kv 13: tokenizer.ggml.tokens arr \n",
|
||||
"llama_model_loader: - kv 14: tokenizer.ggml.scores arr \n",
|
||||
"llama_model_loader: - kv 15: tokenizer.ggml.token_type arr \n",
|
||||
"llama_model_loader: - kv 16: general.quantization_version u32 \n",
|
||||
"llama_model_loader: - type f32: 81 tensors\n",
|
||||
"llama_model_loader: - type f16: 1 tensors\n",
|
||||
"llama_model_loader: - type q4_0: 1 tensors\n",
|
||||
"llama_model_loader: - type q4_K: 240 tensors\n",
|
||||
"llama_model_loader: - type q6_K: 40 tensors\n",
|
||||
"llm_load_print_meta: format = GGUF V1 (latest)\n",
|
||||
"llm_load_print_meta: arch = llama\n",
|
||||
"llm_load_print_meta: vocab type = SPM\n",
|
||||
"llm_load_print_meta: n_vocab = 32016\n",
|
||||
"llm_load_print_meta: n_merges = 0\n",
|
||||
"llm_load_print_meta: n_ctx_train = 16384\n",
|
||||
"llm_load_print_meta: n_ctx = 5000\n",
|
||||
"llm_load_print_meta: n_embd = 5120\n",
|
||||
"llm_load_print_meta: n_head = 40\n",
|
||||
"llm_load_print_meta: n_head_kv = 40\n",
|
||||
"llm_load_print_meta: n_layer = 40\n",
|
||||
"llm_load_print_meta: n_rot = 128\n",
|
||||
"llm_load_print_meta: n_gqa = 1\n",
|
||||
"llm_load_print_meta: f_norm_eps = 1.0e-05\n",
|
||||
"llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n",
|
||||
"llm_load_print_meta: n_ff = 13824\n",
|
||||
"llm_load_print_meta: freq_base = 1000000.0\n",
|
||||
"llm_load_print_meta: freq_scale = 1\n",
|
||||
"llm_load_print_meta: model type = 13B\n",
|
||||
"llm_load_print_meta: model ftype = mostly Q4_K - Medium\n",
|
||||
"llm_load_print_meta: model size = 13.02 B\n",
|
||||
"llm_load_print_meta: general.name = LLaMA\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 MB\n",
|
||||
"llm_load_tensors: mem required = 7685.49 MB (+ 3906.25 MB per state)\n",
|
||||
".................................................................................................\n",
|
||||
"llama_new_context_with_model: kv self size = 3906.25 MB\n",
|
||||
"ggml_metal_init: allocating\n",
|
||||
"ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
|
||||
"ggml_metal_init: loaded kernel_add 0x12126dd00 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_add_row 0x12126d610 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul 0x12126f2a0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_row 0x12126f500 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_scale 0x12126f760 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_silu 0x12126fe40 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_relu 0x1212700a0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_gelu 0x121270300 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_soft_max 0x121270560 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_diag_mask_inf 0x1212707c0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_f16 0x121270a20 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q4_0 0x121270c80 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q4_1 0x121270ee0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q8_0 0x121271140 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q2_K 0x1212713a0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q3_K 0x121271600 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q4_K 0x121271860 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q5_K 0x121271ac0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_get_rows_q6_K 0x121271d20 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_rms_norm 0x121271f80 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_norm 0x1212721e0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x121272440 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x1212726a0 | th_max = 896 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x121272900 | th_max = 896 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q8_0_f32 0x121272b60 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x121272dc0 | th_max = 640 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x121273020 | th_max = 704 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x121273280 | th_max = 576 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x1212734e0 | th_max = 576 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x121273740 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_f16_f32 0x1212739a0 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q4_0_f32 0x121273c00 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q8_0_f32 0x121273e60 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q4_1_f32 0x1212740c0 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q2_K_f32 0x121274320 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q3_K_f32 0x121274580 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q4_K_f32 0x1212747e0 | th_max = 768 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q5_K_f32 0x121274a40 | th_max = 704 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_mul_mm_q6_K_f32 0x121274ca0 | th_max = 704 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_rope 0x121274f00 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_alibi_f32 0x121275160 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_cpy_f32_f16 0x1212753c0 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_cpy_f32_f32 0x121275620 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: loaded kernel_cpy_f16_f16 0x121275880 | th_max = 1024 | th_width = 32\n",
|
||||
"ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
|
||||
"ggml_metal_init: hasUnifiedMemory = true\n",
|
||||
"ggml_metal_init: maxTransferRate = built-in GPU\n",
|
||||
"llama_new_context_with_model: compute buffer total size = 442.03 MB\n",
|
||||
"llama_new_context_with_model: max tensor size = 312.66 MB\n",
|
||||
"ggml_metal_add_buffer: allocated 'data ' buffer, size = 7686.00 MB, (20243.77 / 21845.34)\n",
|
||||
"ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1.42 MB, (20245.19 / 21845.34)\n",
|
||||
"ggml_metal_add_buffer: allocated 'kv ' buffer, size = 3908.25 MB, (24153.44 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n",
|
||||
"AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n",
|
||||
"ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 440.64 MB, (24594.08 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
|
||||
"llm = LlamaCpp(\n",
|
||||
@ -1063,7 +582,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
"version": "3.11.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user