diff --git a/rag_fr_3.ipynb b/rag_fr_3.ipynb index 7b15b5d..0969f93 100644 --- a/rag_fr_3.ipynb +++ b/rag_fr_3.ipynb @@ -21,12 +21,20 @@ }, { "cell_type": "markdown", - "id": "3c31df71-9eb1-499c-bbab-c92d4c870e6c", + "id": "54a9d312-b39b-45f8-9529-57a142b6f6fc", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ - "# Embedding model and tokenizer" + "# Embed a folder of CERA webpages in txt format" + ] + }, + { + "cell_type": "markdown", + "id": "3c31df71-9eb1-499c-bbab-c92d4c870e6c", + "metadata": {}, + "source": [ + "## Embedding model and tokenizer" ] }, { @@ -53,502 +61,10 @@ "embed_model = SentenceTransformer(embed_model_name)" ] }, - { - "cell_type": "markdown", - "id": "631ab89d-55f7-4d89-9e82-0d1a09359c79", - "metadata": {}, - "source": [ - "# LLM model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "bc970979-82f3-46c4-ab86-4d9bf65acdd6", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - 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"llama_model_loader: - tensor 256: blk.28.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 257: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 258: blk.28.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 259: blk.28.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 260: blk.28.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 261: blk.28.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 262: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 263: blk.29.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 264: blk.29.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 265: blk.29.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 266: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 267: blk.29.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 268: blk.29.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 269: blk.29.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 270: blk.29.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 271: blk.30.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 272: blk.30.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 273: blk.30.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 274: blk.30.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 275: blk.30.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 276: blk.30.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 277: output.weight q6_K [ 4096, 32000, 1, 1 ]\n", - "llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 279: blk.30.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 280: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 281: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 282: blk.31.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 283: blk.31.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 284: blk.31.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", - "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 q5_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 287: blk.31.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 288: blk.31.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 289: blk.31.attn_v.weight q6_K [ 4096, 1024, 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 = llama\n", - "llama_model_loader: - kv 1: general.name str = huggingfaceh4_zephyr-7b-beta\n", - "llama_model_loader: - kv 2: llama.context_length u32 = 32768\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 = 14336\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 = 8\n", - "llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010\n", - "llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000\n", - "llama_model_loader: - kv 11: general.file_type u32 = 17\n", - "llama_model_loader: - kv 12: tokenizer.ggml.model str = llama\n", - "llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = [\"\", \"\", \"\", \"<0x00>\", \"<...\n", - "llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...\n", - "llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n", - "llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1\n", - "llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2\n", - "llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0\n", - "llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 2\n", - "llama_model_loader: - kv 20: 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", - "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n", - "llm_load_print_meta: format = GGUF V3 (latest)\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 = 32768\n", - "llm_load_print_meta: n_embd = 4096\n", - "llm_load_print_meta: n_head = 32\n", - "llm_load_print_meta: n_head_kv = 8\n", - "llm_load_print_meta: n_layer = 32\n", - "llm_load_print_meta: n_rot = 128\n", - "llm_load_print_meta: n_gqa = 4\n", - "llm_load_print_meta: f_norm_eps = 0.0e+00\n", - "llm_load_print_meta: f_norm_rms_eps = 1.0e-05\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 = 14336\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 = 32768\n", - "llm_load_print_meta: rope_finetuned = unknown\n", - "llm_load_print_meta: model type = 7B\n", - "llm_load_print_meta: model ftype = mostly Q5_K - Medium\n", - "llm_load_print_meta: model params = 7.24 B\n", - "llm_load_print_meta: model size = 4.78 GiB (5.67 BPW) \n", - "llm_load_print_meta: general.name = huggingfaceh4_zephyr-7b-beta\n", - "llm_load_print_meta: BOS token = 1 ''\n", - "llm_load_print_meta: EOS token = 2 ''\n", - "llm_load_print_meta: UNK token = 0 ''\n", - "llm_load_print_meta: PAD token = 2 ''\n", - "llm_load_print_meta: LF token = 13 '<0x0A>'\n", - "llm_load_tensors: ggml ctx size = 0.11 MiB\n", - "llm_load_tensors: mem required = 4893.10 MiB\n", - "...................................................................................................\n", - "llama_new_context_with_model: n_ctx = 4096\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 = 512.00 MiB\n", - "llama_build_graph: non-view tensors processed: 740/740\n", - "ggml_metal_init: allocating\n", - "ggml_metal_init: found device: Apple M2 Max\n", - "ggml_metal_init: picking default device: Apple M2 Max\n", - "ggml_metal_init: default.metallib not found, loading from source\n", - "ggml_metal_init: loading '/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n", - "ggml_metal_init: GPU name: Apple M2 Max\n", - "ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)\n", - "ggml_metal_init: hasUnifiedMemory = true\n", - "ggml_metal_init: recommendedMaxWorkingSetSize = 49152.00 MiB\n", - "ggml_metal_init: maxTransferRate = built-in GPU\n", - "llama_new_context_with_model: compute buffer total size = 291.07 MiB\n", - "llama_new_context_with_model: max tensor size = 102.54 MiB\n", - "ggml_metal_add_buffer: allocated 'data ' buffer, size = 4893.70 MiB, (10588.06 / 49152.00)\n", - "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 512.02 MiB, (11100.08 / 49152.00)\n", - "ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 288.02 MiB, (11388.09 / 49152.00)\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 | SSSE3 = 0 | VSX = 0 | \n", - "ggml_metal_free: deallocating\n" - ] - } - ], - "source": [ - "llm = Llama(model_path='/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf', \n", - " n_gpu_layers=1, use_mlock=True, n_ctx=4096)\n", - "\n", - "system_prompt = \"\"\"\\\n", - "Vous fournissez avec soin des réponses précises, factuelles, réfléchies et nuancées, et vous êtes doué pour le raisonnement. \\\n", - "Si vous pensez qu'il n'y a peut-être pas de bonne réponse, vous le dites. \\\n", - "Ne soyez pas verbeux dans vos réponses, mais donnez des détails et des exemples lorsque cela peut aider à l'explication. \\\n", - "Vous rédigez vos réponses en français. \\\n", - "\"\"\"\n", - "\n", - "def format_prompt(question):\n", - " prompt = \"\"\n", - " prompt = f\"<|system|>\\n {system_prompt.strip()} \\n\"\n", - " prompt += f\"<|user|>\\n {question} \\n\"\n", - " prompt += f\"<|assistant|>\\n\"\n", - " return prompt\n", - "\n", - "def answer(question):\n", - " response = llm(prompt = question,\n", - " temperature = 0.1,\n", - " mirostat_mode = 2,\n", - " max_tokens = -1,\n", - " stop = [''])\n", - " return response[\"choices\"][0][\"text\"]" - ] - }, - { - "cell_type": "markdown", - "id": "4b9b8dcd-a371-4f03-b0c6-eb27d56002fe", - "metadata": {}, - "source": [ - "## Test LLM model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "460b87be-7778-430a-a167-3c3fd8deaf48", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Le philosophe allemand Georg Wilhelm Friedrich Hegel (1770-1831) a exercé une influence considérable sur la philosophie moderne, particulièrement dans le domaine de l'idéalisme et du marxisme. Voici quelques philosophes qui ont été fortement influencés par Hegel :\n", - "\n", - "1. Karl Marx (1818-1883) : Le fondateur du marxisme a été profondément inspiré par la philosophie de Hegel, en particulier son concept d'histoire comme processus dialectique. Marx a critiqué et développé l'idée hégélienne de la dialectique pour expliquer le fonctionnement de la société et les lois de l'évolution historique.\n", - "\n", - "2. Friedrich Engels (1820-1895) : Le collaborateur de Marx a également été influencé par Hegel, en particulier son concept d'histoire comme processus dialectique. Engels a développé cette idée dans son ouvrage \"L'origine de la famille, de la propriété privée et de l'État\" (1884), où il explique comment les relations sociales ont évolué en fonction des conditions historiques.\n", - "\n", - "3. G.W.F. Hegel lui-même : Certains philosophes ont été influencés par Hegel à tel point qu'ils ont développé leur propre philosophie dans le cadre de l'hégélianisme, une école de pensée qui a été dominante en Allemagne au XIXe siècle. Parmi ces philosophes, on peut citer Arthur Schopenhauer (1788-1860), qui a critiqué et modifié les idées hégéliennes pour développer sa propre philosophie de l'art et du pessimisme, ainsi que Ludwig Feuerbach (1804-1872), qui a développé une critique de la religion et de la philosophie hégélienne.\n", - "\n", - "4. Jean-Paul Sartre (1905-1980) : Le philosophe existentialiste français a été influencé par Hegel dans son travail sur l'histoire et la dialectique, en particulier dans son ouvrage \"L'existentialisme est un humanisme\" (1946), où il développe une vision de l'histoire comme processus dialectique. Sartre a également critiqué les idées hégéliennes sur le sujet et la conscience, en particulier dans son ouvrage \"L'être et le néant\" (1943).\n", - "\n", - "5. Slavoj Žižek (né en 1949) : Le philosophe slovène a été influencé par Hegel dans son travail sur la psychanalyse, la politique et la culture populaire, en particulier dans son ouvrage \"Le Sublime Object de l'Idée\" (1981), où il développe une vision dialectique de la culture populaire. Žižek a également critiqué les idées hégéliennes sur le sujet et la conscience, en particulier dans son ouvrage \"Le Pouvoir politique et la Formation du Sujet\" (1976).\n", - "\n", - "Ces philosophes ont été influencés par Hegel dans des domaines variés de la philosophie moderne, mais ils ont tous reconnu l'importance de ses idées sur l'histoire, la dialectique et le sujet.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "llama_print_timings: load time = 575.42 ms\n", - "llama_print_timings: sample time = 2695.33 ms / 804 runs ( 3.35 ms per token, 298.29 tokens per second)\n", - "llama_print_timings: prompt eval time = 574.33 ms / 165 tokens ( 3.48 ms per token, 287.29 tokens per second)\n", - "llama_print_timings: eval time = 22009.23 ms / 803 runs ( 27.41 ms per token, 36.48 tokens per second)\n", - "llama_print_timings: total time = 24988.34 ms\n" - ] - } - ], - "source": [ - "print(answer(format_prompt(\"Quels sont les philosophes les plus influencés par Hegel ?\")))" - ] - }, - { - "cell_type": "markdown", - "id": "516058c3-c764-4678-8a95-1d234e4c7f6a", - "metadata": {}, - "source": [ - "# Embed a folder to a ChromaDB collection" - ] - }, { "cell_type": "markdown", "id": "71b71ca4-3e59-4cf9-a43a-2877eccfcf07", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "## Initialize a ChromaDB persistent collection" ] @@ -568,9 +84,7 @@ { "cell_type": "markdown", "id": "0adb9e64-bc3a-40c7-ab8f-c3b6bf39a15c", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "## Embed the text of a particular web page" ] @@ -685,9 +199,7 @@ { "cell_type": "markdown", "id": "cb3fc271-be0b-4532-978e-8215227fa8fd", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "## Embed all the webpages in a folder" ] @@ -1596,6 +1108,490 @@ "query_results = query_collection(query)" ] }, + { + "cell_type": "markdown", + "id": "631ab89d-55f7-4d89-9e82-0d1a09359c79", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# LLM model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc970979-82f3-46c4-ab86-4d9bf65acdd6", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from /Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf (version GGUF V3 (latest))\n", + "llama_model_loader: - tensor 0: token_embd.weight q5_K [ 4096, 32000, 1, 1 ]\n", + "llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 2: blk.0.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 4: blk.0.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 5: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 6: blk.0.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 7: blk.0.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 8: blk.0.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 9: blk.0.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 11: blk.1.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 12: blk.1.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 13: blk.1.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 14: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 15: blk.1.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 16: blk.1.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 17: blk.1.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 18: blk.1.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 19: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 20: blk.2.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 21: blk.2.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 22: blk.2.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 23: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - 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tensor 264: blk.29.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 265: blk.29.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 266: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 267: blk.29.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 268: blk.29.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 269: blk.29.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 270: blk.29.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 271: blk.30.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 272: blk.30.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 273: blk.30.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 274: blk.30.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 275: blk.30.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 276: blk.30.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 277: output.weight q6_K [ 4096, 32000, 1, 1 ]\n", + "llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 279: blk.30.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 280: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 281: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 282: blk.31.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 283: blk.31.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 284: blk.31.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n", + "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 q5_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 287: blk.31.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 288: blk.31.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 289: blk.31.attn_v.weight q6_K [ 4096, 1024, 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 = llama\n", + "llama_model_loader: - kv 1: general.name str = huggingfaceh4_zephyr-7b-beta\n", + "llama_model_loader: - kv 2: llama.context_length u32 = 32768\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 = 14336\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 = 8\n", + "llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010\n", + "llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000\n", + "llama_model_loader: - kv 11: general.file_type u32 = 17\n", + "llama_model_loader: - kv 12: tokenizer.ggml.model str = llama\n", + "llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = [\"\", \"\", \"\", \"<0x00>\", \"<...\n", + "llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...\n", + "llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n", + "llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1\n", + "llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2\n", + "llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0\n", + "llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 2\n", + "llama_model_loader: - kv 20: 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", + "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n", + "llm_load_print_meta: format = GGUF V3 (latest)\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 = 32768\n", + "llm_load_print_meta: n_embd = 4096\n", + "llm_load_print_meta: n_head = 32\n", + "llm_load_print_meta: n_head_kv = 8\n", + "llm_load_print_meta: n_layer = 32\n", + "llm_load_print_meta: n_rot = 128\n", + "llm_load_print_meta: n_gqa = 4\n", + "llm_load_print_meta: f_norm_eps = 0.0e+00\n", + "llm_load_print_meta: f_norm_rms_eps = 1.0e-05\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 = 14336\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 = 32768\n", + "llm_load_print_meta: rope_finetuned = unknown\n", + "llm_load_print_meta: model type = 7B\n", + "llm_load_print_meta: model ftype = mostly Q5_K - Medium\n", + "llm_load_print_meta: model params = 7.24 B\n", + "llm_load_print_meta: model size = 4.78 GiB (5.67 BPW) \n", + "llm_load_print_meta: general.name = huggingfaceh4_zephyr-7b-beta\n", + "llm_load_print_meta: BOS token = 1 ''\n", + "llm_load_print_meta: EOS token = 2 ''\n", + "llm_load_print_meta: UNK token = 0 ''\n", + "llm_load_print_meta: PAD token = 2 ''\n", + "llm_load_print_meta: LF token = 13 '<0x0A>'\n", + "llm_load_tensors: ggml ctx size = 0.11 MiB\n", + "llm_load_tensors: mem required = 4893.10 MiB\n", + "...................................................................................................\n", + "llama_new_context_with_model: n_ctx = 4096\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 = 512.00 MiB\n", + "llama_build_graph: non-view tensors processed: 740/740\n", + "ggml_metal_init: allocating\n", + "ggml_metal_init: found device: Apple M2 Max\n", + "ggml_metal_init: picking default device: Apple M2 Max\n", + "ggml_metal_init: default.metallib not found, loading from source\n", + "ggml_metal_init: loading '/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n", + "ggml_metal_init: GPU name: Apple M2 Max\n", + "ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)\n", + "ggml_metal_init: hasUnifiedMemory = true\n", + "ggml_metal_init: recommendedMaxWorkingSetSize = 49152.00 MiB\n", + "ggml_metal_init: maxTransferRate = built-in GPU\n", + "llama_new_context_with_model: compute buffer total size = 291.07 MiB\n", + "llama_new_context_with_model: max tensor size = 102.54 MiB\n", + "ggml_metal_add_buffer: allocated 'data ' buffer, size = 4893.70 MiB, (10588.06 / 49152.00)\n", + "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 512.02 MiB, (11100.08 / 49152.00)\n", + "ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 288.02 MiB, (11388.09 / 49152.00)\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 | SSSE3 = 0 | VSX = 0 | \n", + "ggml_metal_free: deallocating\n" + ] + } + ], + "source": [ + "llm = Llama(model_path='/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf', \n", + " n_gpu_layers=1, use_mlock=True, n_ctx=4096)\n", + "\n", + "system_prompt = \"\"\"\\\n", + "Vous fournissez avec soin des réponses précises, factuelles, réfléchies et nuancées, et vous êtes doué pour le raisonnement. \\\n", + "Si vous pensez qu'il n'y a peut-être pas de bonne réponse, vous le dites. \\\n", + "Ne soyez pas verbeux dans vos réponses, mais donnez des détails et des exemples lorsque cela peut aider à l'explication. \\\n", + "Vous rédigez vos réponses en français. \\\n", + "\"\"\"\n", + "\n", + "def format_prompt(question):\n", + " prompt = \"\"\n", + " prompt = f\"<|system|>\\n {system_prompt.strip()} \\n\"\n", + " prompt += f\"<|user|>\\n {question} \\n\"\n", + " prompt += f\"<|assistant|>\\n\"\n", + " return prompt\n", + "\n", + "def answer(question):\n", + " response = llm(prompt = question,\n", + " temperature = 0.1,\n", + " mirostat_mode = 2,\n", + " max_tokens = -1,\n", + " stop = [''])\n", + " return response[\"choices\"][0][\"text\"]" + ] + }, + { + "cell_type": "markdown", + "id": "4b9b8dcd-a371-4f03-b0c6-eb27d56002fe", + "metadata": {}, + "source": [ + "## Test LLM model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "460b87be-7778-430a-a167-3c3fd8deaf48", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Le philosophe allemand Georg Wilhelm Friedrich Hegel (1770-1831) a exercé une influence considérable sur la philosophie moderne, particulièrement dans le domaine de l'idéalisme et du marxisme. Voici quelques philosophes qui ont été fortement influencés par Hegel :\n", + "\n", + "1. Karl Marx (1818-1883) : Le fondateur du marxisme a été profondément inspiré par la philosophie de Hegel, en particulier son concept d'histoire comme processus dialectique. Marx a critiqué et développé l'idée hégélienne de la dialectique pour expliquer le fonctionnement de la société et les lois de l'évolution historique.\n", + "\n", + "2. Friedrich Engels (1820-1895) : Le collaborateur de Marx a également été influencé par Hegel, en particulier son concept d'histoire comme processus dialectique. Engels a développé cette idée dans son ouvrage \"L'origine de la famille, de la propriété privée et de l'État\" (1884), où il explique comment les relations sociales ont évolué en fonction des conditions historiques.\n", + "\n", + "3. G.W.F. Hegel lui-même : Certains philosophes ont été influencés par Hegel à tel point qu'ils ont développé leur propre philosophie dans le cadre de l'hégélianisme, une école de pensée qui a été dominante en Allemagne au XIXe siècle. Parmi ces philosophes, on peut citer Arthur Schopenhauer (1788-1860), qui a critiqué et modifié les idées hégéliennes pour développer sa propre philosophie de l'art et du pessimisme, ainsi que Ludwig Feuerbach (1804-1872), qui a développé une critique de la religion et de la philosophie hégélienne.\n", + "\n", + "4. Jean-Paul Sartre (1905-1980) : Le philosophe existentialiste français a été influencé par Hegel dans son travail sur l'histoire et la dialectique, en particulier dans son ouvrage \"L'existentialisme est un humanisme\" (1946), où il développe une vision de l'histoire comme processus dialectique. Sartre a également critiqué les idées hégéliennes sur le sujet et la conscience, en particulier dans son ouvrage \"L'être et le néant\" (1943).\n", + "\n", + "5. Slavoj Žižek (né en 1949) : Le philosophe slovène a été influencé par Hegel dans son travail sur la psychanalyse, la politique et la culture populaire, en particulier dans son ouvrage \"Le Sublime Object de l'Idée\" (1981), où il développe une vision dialectique de la culture populaire. Žižek a également critiqué les idées hégéliennes sur le sujet et la conscience, en particulier dans son ouvrage \"Le Pouvoir politique et la Formation du Sujet\" (1976).\n", + "\n", + "Ces philosophes ont été influencés par Hegel dans des domaines variés de la philosophie moderne, mais ils ont tous reconnu l'importance de ses idées sur l'histoire, la dialectique et le sujet.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "llama_print_timings: load time = 575.42 ms\n", + "llama_print_timings: sample time = 2695.33 ms / 804 runs ( 3.35 ms per token, 298.29 tokens per second)\n", + "llama_print_timings: prompt eval time = 574.33 ms / 165 tokens ( 3.48 ms per token, 287.29 tokens per second)\n", + "llama_print_timings: eval time = 22009.23 ms / 803 runs ( 27.41 ms per token, 36.48 tokens per second)\n", + "llama_print_timings: total time = 24988.34 ms\n" + ] + } + ], + "source": [ + "print(answer(format_prompt(\"Quels sont les philosophes les plus influencés par Hegel ?\")))" + ] + }, { "cell_type": "markdown", "id": "13153d19-b9d5-482c-82e7-8eca1fad5bcd", @@ -4080,9 +4076,7 @@ { "cell_type": "markdown", "id": "14c72389-df64-42f3-89fd-1378fe555438", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "# Chat interface" ]