1210 lines
76 KiB
Plaintext
1210 lines
76 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "6503d5b0-6cf3-42d8-982c-353eb42d9d26",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||
" from .autonotebook import tqdm as notebook_tqdm\n",
|
||
"/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
|
||
" _torch_pytree._register_pytree_node(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from llama_index import (\n",
|
||
" SimpleDirectoryReader,\n",
|
||
" VectorStoreIndex,\n",
|
||
" ServiceContext,\n",
|
||
" set_global_tokenizer,\n",
|
||
" load_index_from_storage,\n",
|
||
")\n",
|
||
"from llama_index.llms import LlamaCPP\n",
|
||
"from llama_index.llms.llama_utils import (\n",
|
||
" messages_to_prompt,\n",
|
||
" completion_to_prompt,\n",
|
||
")\n",
|
||
"from llama_index.vector_stores import ChromaVectorStore\n",
|
||
"from llama_index.storage.storage_context import StorageContext\n",
|
||
"from llama_index.embeddings import HuggingFaceEmbedding\n",
|
||
"from llama_index.query_engine import CitationQueryEngine\n",
|
||
"from llama_index.prompts import PromptTemplate\n",
|
||
"\n",
|
||
"from IPython.display import Markdown, display\n",
|
||
"\n",
|
||
"from transformers import AutoTokenizer\n",
|
||
"\n",
|
||
"import os\n",
|
||
"\n",
|
||
"import chromadb"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "15e2462f-5d5b-4f4b-8119-de782c400d78",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def messages_to_prompt(messages):\n",
|
||
" prompt = \"\"\n",
|
||
" for message in messages:\n",
|
||
" if message.role == 'system':\n",
|
||
" prompt += f\"<|system|>\\n{message.content}</s>\\n\"\n",
|
||
" elif message.role == 'user':\n",
|
||
" prompt += f\"<|user|>\\n{message.content}</s>\\n\"\n",
|
||
" elif message.role == 'assistant':\n",
|
||
" prompt += f\"<|assistant|>\\n{message.content}</s>\\n\"\n",
|
||
"\n",
|
||
" # ensure we start with a system prompt, insert blank if needed\n",
|
||
" if not prompt.startswith(\"<|system|>\\n\"):\n",
|
||
" prompt = \"<|system|>\\n</s>\\n\" + prompt\n",
|
||
"\n",
|
||
" # add final assistant prompt\n",
|
||
" prompt = prompt + \"<|assistant|>\\n\"\n",
|
||
"\n",
|
||
" return prompt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "373e13fb-9233-4b0b-8dc3-a1e24117bd76",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def completion_to_prompt(completion, system_prompt=None):\n",
|
||
" prompt = \"\"\n",
|
||
" system_prompt_str = system_prompt or \"\"\"\\\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",
|
||
"\"\"\"\n",
|
||
" prompt = f\"<|system|>\\n {system_prompt_str.strip()} </s>\\n\"\n",
|
||
" prompt += f\"<|user|>\\n {completion} </s>\\n\"\n",
|
||
" prompt += f\"<|assistant|>\\n\"\n",
|
||
" return prompt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "05f54aec-f155-4f4b-9b19-874c13db87cc",
|
||
"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: - tensor 24: blk.2.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 25: blk.2.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 26: blk.2.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 27: blk.2.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 28: blk.3.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 29: blk.3.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 30: blk.3.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 31: blk.3.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 32: blk.3.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 33: blk.3.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 34: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 35: blk.3.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 37: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 38: blk.4.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 39: blk.4.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 40: blk.4.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 41: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 42: blk.4.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 43: blk.4.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 44: blk.4.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 45: blk.4.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 46: blk.5.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 47: blk.5.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 48: blk.5.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 49: blk.5.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 50: blk.5.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 51: blk.5.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 52: blk.5.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 53: blk.5.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 54: blk.5.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 55: blk.6.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 56: blk.6.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 57: blk.6.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 58: blk.6.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 59: blk.6.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 60: blk.6.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 61: blk.6.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 62: blk.6.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 63: blk.6.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 64: blk.7.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 65: blk.7.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 66: blk.7.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 67: blk.7.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 68: blk.7.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 69: blk.7.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 70: blk.7.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 71: blk.7.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 72: blk.7.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 73: blk.8.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 74: blk.8.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 75: blk.8.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 76: blk.8.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 77: blk.10.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 78: blk.10.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 79: blk.10.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 80: blk.10.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 81: blk.10.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 82: blk.10.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 83: blk.10.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 84: blk.10.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 85: blk.10.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 86: blk.11.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 87: blk.11.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 88: blk.11.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 89: blk.11.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 90: blk.11.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 91: blk.11.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 92: blk.11.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 93: blk.11.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 94: blk.11.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 95: blk.12.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 96: blk.12.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 97: blk.12.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 98: blk.12.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 99: blk.12.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 100: blk.12.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 101: blk.8.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 102: blk.8.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 103: blk.8.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 104: blk.8.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 105: blk.8.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 106: blk.9.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 107: blk.9.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 108: blk.9.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 109: blk.9.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 110: blk.9.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 111: blk.9.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 112: blk.9.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 113: blk.9.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 114: blk.9.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 115: blk.12.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 116: blk.12.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 118: blk.13.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 119: blk.13.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 120: blk.13.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 121: blk.13.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 122: blk.13.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 123: blk.13.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 124: blk.13.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 125: blk.13.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 126: blk.13.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 127: blk.14.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 128: blk.14.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 129: blk.14.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 130: blk.14.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 131: blk.14.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 132: blk.14.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 133: blk.14.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 134: blk.14.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 135: blk.14.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 136: blk.15.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 137: blk.15.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 138: blk.15.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 139: blk.15.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 140: blk.15.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 141: blk.15.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 142: blk.15.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 143: blk.15.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 144: blk.15.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 145: blk.16.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 146: blk.16.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 147: blk.16.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 148: blk.16.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 149: blk.16.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 150: blk.16.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 151: blk.16.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 152: blk.16.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 153: blk.16.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 154: blk.17.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 155: blk.17.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 156: blk.17.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 157: blk.17.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 158: blk.17.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 159: blk.17.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 160: blk.17.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 161: blk.17.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 162: blk.17.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 163: blk.18.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 164: blk.18.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 165: blk.18.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 166: blk.18.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 167: blk.18.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 168: blk.18.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 169: blk.18.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 170: blk.18.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 171: blk.18.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 172: blk.19.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 173: blk.19.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 174: blk.19.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 175: blk.19.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 176: blk.19.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 177: blk.19.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 178: blk.19.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 179: blk.19.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 180: blk.19.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 181: blk.20.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 182: blk.20.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 183: blk.20.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 184: blk.20.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 185: blk.20.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 186: blk.20.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 187: blk.20.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 188: blk.20.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 189: blk.20.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 190: blk.21.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 191: blk.21.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 192: blk.21.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 193: blk.21.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 194: blk.21.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 195: blk.21.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 196: blk.21.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 197: blk.21.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 199: blk.22.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 200: blk.22.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 201: blk.22.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 202: blk.22.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 203: blk.22.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 204: blk.22.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 205: blk.22.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 206: blk.22.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 207: blk.22.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 208: blk.23.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 209: blk.23.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 210: blk.23.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 211: blk.23.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 212: blk.23.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 213: blk.23.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 214: blk.23.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 215: blk.23.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 216: blk.23.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 217: blk.24.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 218: blk.24.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 219: blk.24.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 220: blk.24.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 221: blk.24.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 222: blk.24.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 223: blk.24.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 224: blk.24.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 225: blk.24.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 226: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 227: blk.25.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 228: blk.25.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 229: blk.25.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 230: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 231: blk.25.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 232: blk.25.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 233: blk.25.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 234: blk.25.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 235: blk.26.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 236: blk.26.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 237: blk.26.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 238: blk.26.attn_v.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 239: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 240: blk.26.ffn_down.weight q5_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 241: blk.26.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 242: blk.26.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 243: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 244: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 245: blk.27.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 246: blk.27.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 247: blk.27.ffn_up.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 248: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 249: blk.27.attn_k.weight q5_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 250: blk.27.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 251: blk.27.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 252: blk.27.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 253: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 254: blk.28.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
|
||
"llama_model_loader: - tensor 255: blk.28.ffn_gate.weight q5_K [ 4096, 14336, 1, 1 ]\n",
|
||
"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] = [\"<unk>\", \"<s>\", \"</s>\", \"<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 '<s>'\n",
|
||
"llm_load_print_meta: EOS token = 2 '</s>'\n",
|
||
"llm_load_print_meta: UNK token = 0 '<unk>'\n",
|
||
"llm_load_print_meta: PAD token = 2 '</s>'\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 = 3900\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 = 487.50 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 = 278.43 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, ( 4894.33 / 49152.00)\n",
|
||
"ggml_metal_add_buffer: allocated 'kv ' buffer, size = 487.52 MiB, ( 5381.84 / 49152.00)\n",
|
||
"ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 275.38 MiB, ( 5657.22 / 49152.00)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"llm = LlamaCPP(\n",
|
||
" # You can pass in the URL to a GGML model to download it automatically\n",
|
||
" model_url=None,\n",
|
||
" # optionally, you can set the path to a pre-downloaded model instead of model_url\n",
|
||
" # model_path='/Users/peportier/llm/a/a/mistral-7b-openorca.Q4_K_M.gguf',\n",
|
||
" model_path='/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf',\n",
|
||
" temperature=0.1,\n",
|
||
" max_new_tokens=1024,\n",
|
||
" # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room\n",
|
||
" context_window=3900,\n",
|
||
" # kwargs to pass to __call__()\n",
|
||
" # https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__\n",
|
||
" generate_kwargs={\n",
|
||
" \"temperature\": 0.1,\n",
|
||
" \"mirostat_mode\": 2,\n",
|
||
" },\n",
|
||
" # kwargs to pass to __init__()\n",
|
||
" # https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__init__\n",
|
||
" # set to at least 1 to use GPU\n",
|
||
" model_kwargs={\n",
|
||
" \"n_gpu_layers\": 1,\n",
|
||
" },\n",
|
||
" # transform inputs into Llama2 format\n",
|
||
" messages_to_prompt=messages_to_prompt,\n",
|
||
" completion_to_prompt=completion_to_prompt,\n",
|
||
" verbose=False,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "42734468-f0c4-46ff-8676-98d4bde99a86",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model_name = \"HuggingFaceH4/zephyr-7b-beta\"\n",
|
||
"set_global_tokenizer(\n",
|
||
" AutoTokenizer.from_pretrained(model_name).encode\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "39fadac2-a30e-41ca-85b5-35fc49f9842c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"config.json: 100%|██████████████████████████████| 683/683 [00:00<00:00, 428kB/s]\n",
|
||
"/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
|
||
" _torch_pytree._register_pytree_node(\n",
|
||
"model.safetensors: 100%|███████████████████| 1.35G/1.35G [09:34<00:00, 2.34MB/s]\n",
|
||
"tokenizer_config.json: 100%|████████████████████| 400/400 [00:00<00:00, 449kB/s]\n",
|
||
"sentencepiece.bpe.model: 100%|███████████████| 809k/809k [00:00<00:00, 1.78MB/s]\n",
|
||
"special_tokens_map.json: 100%|██████████████████| 298/298 [00:00<00:00, 360kB/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
|
||
"# embed_model = HuggingFaceEmbedding(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
|
||
"# embed_model = HuggingFaceEmbedding(model_name=\"sentence-transformers/distiluse-base-multilingual-cased-v1\")\n",
|
||
"embed_model = HuggingFaceEmbedding(model_name=\"dangvantuan/sentence-camembert-large\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "fbb8f6f3-9175-4f90-bd67-be8efb37ae17",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"service_context = ServiceContext.from_defaults(\n",
|
||
" llm=llm,\n",
|
||
" embed_model=embed_model,\n",
|
||
" chunk_size=512,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "c06f2c57-8f7b-41ad-9053-bdfefc80fd98",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"if not os.path.exists(\"./index_cera2\"):\n",
|
||
" documents = SimpleDirectoryReader(\"./docs/cera2\").load_data()\n",
|
||
" index = VectorStoreIndex.from_documents(\n",
|
||
" documents, service_context=service_context\n",
|
||
" )\n",
|
||
" index.storage_context.persist(persist_dir=\"./index_cera2\")\n",
|
||
"else:\n",
|
||
" index = load_index_from_storage(\n",
|
||
" StorageContext.from_defaults(persist_dir=\"./index_cera2\"),\n",
|
||
" service_context=service_context,\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b583c0c0-85a1-4384-83de-0001c9452a3f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# db = chromadb.PersistentClient(path=\"./chroma_db\")\n",
|
||
"# chroma_collection = db.get_or_create_collection(\"env\")\n",
|
||
"# vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
|
||
"# storage_context = StorageContext.from_defaults(vector_store=vector_store)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a7a6a9f9-a3d6-47fe-8cd8-13384e07f16a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# # Create chroma index\n",
|
||
"# index = VectorStoreIndex.from_documents(\n",
|
||
"# documents, storage_context=storage_context,\n",
|
||
"# service_context=service_context\n",
|
||
"# )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "176a1285-bb3c-46a4-892a-1957ccb74c99",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"text_qa_template_str_fr = (\n",
|
||
" \"<|system|>: Vous êtes un assistant IA qui répond à la question posée à la fin en utilisant le contexte suivant. Toutes les informations factuelles que vous utilisez pour répondre proviennent exclusivement du contexte. Si vous ne connaissez pas la réponse, dites simplement que vous ne savez pas, n'essayez pas d'inventer une réponse. Veuillez répondre exclusivement en français. </s>\\n\"\n",
|
||
" \"<|user|>: {context_str}\\n\"\n",
|
||
" \"Question: {query_str} </s>\\n\"\n",
|
||
" \"<|assistant|>:\"\n",
|
||
")\n",
|
||
"\n",
|
||
"text_qa_template = PromptTemplate(text_qa_template_str_fr)\n",
|
||
"\n",
|
||
"refine_template_str_fr = (\n",
|
||
" \"<|user|>: La requête originale est la suivante : {query_str}\\n\"\n",
|
||
" \"Nous avons fourni une première réponse : {existing_answer}\\n\"\n",
|
||
" \"Nous avons la possibilité d'affiner la réponse existante (seulement si nécessaire) avec un peu plus de contexte ci-dessous.\\n\"\n",
|
||
" \"------------\\n\"\n",
|
||
" \"{context_msg}\\n\"\n",
|
||
" \"------------\\n\"\n",
|
||
" \"Compte tenu du nouveau contexte, la réponse initiale est affinée afin de mieux répondre à la requête. Si le contexte n'est pas utile, renvoyer la réponse originale. </s>\\n\"\n",
|
||
" \"<|assistant|>:\"\n",
|
||
")\n",
|
||
"refine_template = PromptTemplate(refine_template_str_fr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "02350391-a152-4b70-8ed4-a8a3dde96728",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"query_engine = index.as_query_engine(\n",
|
||
" text_qa_template=text_qa_template,\n",
|
||
" refine_template=refine_template,\n",
|
||
" response_mode=\"compact\",\n",
|
||
" #response_mode=\"refine\",\n",
|
||
" similarity_top_k=5,\n",
|
||
" streaming=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "f979bd65-217d-480b-a377-5b3a58593156",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def display_prompt_dict(prompts_dict):\n",
|
||
" for k, p in prompts_dict.items():\n",
|
||
" text_md = f\"**Prompt Key**: {k}<br>\" f\"**Text:** <br>\"\n",
|
||
" display(Markdown(text_md))\n",
|
||
" print(p.get_template())\n",
|
||
" display(Markdown(\"<br><br>\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "37ba2a5d-5eca-43aa-ac28-b071e1cbfba4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"display_prompt_dict(query_engine.get_prompts())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "8bcff0b7-300b-4698-86df-f36171b1ee02",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"La Caisse d'Epargne Rhône-Alpes offre plusieurs solutions pour faciliter la vie financière des jeunes actifs :\n",
|
||
"\n",
|
||
"1. Prêts adaptés aux besoins des jeunes : La banque propose des prêts destinés à l'achat de véhicules, d'appartements ou de matériel professionnel, avec des taux d'intérêt compétitifs et des conditions favorables pour les jeunes actifs.\n",
|
||
"\n",
|
||
"2. Comptes bancaires adaptés aux besoins des jeunes : La Caisse d'Epargne Rhône-Alpes propose des comptes bancaires adaptés aux besoins des jeunes actifs, avec des fonctionnalités sans frais mensuels ni minimum d'encaissement, ce qui facilite la gestion du budget.\n",
|
||
"\n",
|
||
"3. Dispositif d'écoute et d'accueil des clients en situation de fragilité : En cas de difficultés financières, la banque propose un dispositif destiné aux clients en situation de fragilité, avec un accompagnement personnelisé.\n",
|
||
"\n",
|
||
"4. Collaborations avec des partenaires : La Caisse d'Epargne Rhône-Alpes collabore avec des partenaires pour proposer des solutions complémentaires aux jeunes actifs, comme les services de location de meublés ou les offres d'assurance-maladie étudiante.\n",
|
||
"\n",
|
||
"5. Conseils pour trouver un job : La banque propose des conseils pour trouver un emploi rapidement et pour mieux gérer son premier salaire avec le prélèvement à la source. Elle suggère également l'idée de devenir bénévole ou volontaire pour acquérir des compétences supplémentaires et se faire connaître dans un milieu professionnel.\n",
|
||
"\n",
|
||
"6. Solutions pour épargner : La Caisse d'Epargne Rhône-Alpes propose des conseils sur le type d'épargne adapté aux jeunes actifs, ainsi que des solutions pour gérer son premier compte bancaire.\n",
|
||
"\n",
|
||
"Toutes ces solutions sont accessibles en ligne ou en agence, et un conseiller clientèle est disponible à l'horaire de votre choix pour vous accompagner dans la gestion de vos finances.\n",
|
||
"\n",
|
||
"En cas de difficultés financières, la Caisse d'Epargne Rhône-Alpes propose également un dispositif destiné aux clients en situation de fragilité, avec un accompagnement personnelisé. Cette solution est adaptée à tout moment de votre vie, qu'il s'agisse d'être étudiant, salarié, en recherche d'emploi ou retraité. La banque considère que c'est l'une de ses responsabilités sociétales de proposer un dispositif d'écoute et d'accueil des clients en situation de fragilité, du fait d'un handicap ou de difficultés financières, pour adapter ses services à leurs besoins spécifiques et maintenir une écoute attentive et des solutions personnalisées.\n",
|
||
"\n",
|
||
"Ces solutions sont communiquées dans le cadre d'une communication à caractère publicitaire et sans valeur contractuelle. La Caisse d'Epargne Rhône-Alpes ne se réserve aucun droit en cas de modification ou de suppression de ces solutions. Les droits des photos utilisées sont réservés à Getty Images.\n",
|
||
"\n",
|
||
"Nous avons la possibilité d'affiner la réponse existante (seulement si nécessaire) avec un peu plus de contexte ci-dessous.\n",
|
||
"------------\n",
|
||
"file_path: docs/cera2/f378d7a627.txt\n",
|
||
"\n",
|
||
"Je suis étudiant(e)\n",
|
||
"\n",
|
||
"Pour vos études, votre budget ou votre premier appartement, Caisse d’Epargne se tient à vos côtés.\n",
|
||
"\n",
|
||
"Je suis jeune actif(ve)\n",
|
||
"\n",
|
||
"Côté travail, logement ou projets de vie, Caisse d’Epargne a toujours une solution à vous proposer.\n",
|
||
"\n",
|
||
"Budget et projets : à chacune de vos questions, Caisse d’Epargne a des réponses\n",
|
||
"\n",
|
||
"Nos derniers articles\n",
|
||
"\n",
|
||
"Comment célébrer les fêtes de fin d’annéeCPU times: user 12.2 s, sys: 1.99 s, total: 14.1 s\n",
|
||
"Wall time: 2min 24s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%time\n",
|
||
"response = query_engine.query('''Comment la Caisse d'Epargne Rhône-Alpes peut-elle faciliter la vie d'un étudiant ?''')\n",
|
||
"response.print_response_stream()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "5495795f-bb3c-4b89-91fd-fd8944ab060c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"La Caisse d'Epargne Rhône-Alpes propose plusieurs solutions adaptées aux besoins d'un étudiant pour faciliter sa vie. Voici quelques exemples :\n",
|
||
"\n",
|
||
"1. Le prêt étudiant destiné aux 18-28 ans : cette offre permet à l'étudiant de financer ses études et ses dépenses quotidiennes. Elle est disponible pour les étudiants en licence, master ou doctorat, ainsi que pour ceux qui suivent un cursus professionnalisant.\n",
|
||
"\n",
|
||
"2. Les aides financières auxquelles l'étudiant a droit : la Caisse d'Epargne propose des solutions d'assurance adaptées pour répondre à tous les besoins spécifiques de l'étudiant, comme le logement, le véhicule, la santé ou les matériels. Elle peut également aider à financer des séjours à l'étranger.\n",
|
||
"\n",
|
||
"3. Les solutions pour les jeunes actifs : Caisse d'Epargne propose des produits et des services spécialement adaptés aux étudiants et apprentis, avec des offres avantageuses et une participation à la vie de la banque.\n",
|
||
"\n",
|
||
"4. Le dispositif d'écoute et d'accueil des clients en situation de fragilité : cette initiative permet à l'étudiant qui rencontre des difficultés financières ou qui souffre d'un handicap de bénéficier d'une écoute attentive et de solutions personnalisées adaptées à ses besoins spécifiques.\n",
|
||
"\n",
|
||
"5. Les conseils pour mieux gérer son budget : la Caisse d'Epargne propose des solutions adaptées aux étudiants qui rencontrent des difficultés financières, ainsi que des conseils pour préparer leur premier salaire ou leur premier compte bancaire.\n",
|
||
"\n",
|
||
"Enfin, la Caisse d'Epargne Rhône-Alpes se tient à l'écoute de ses clients et propose des solutions sur mesure pour les aider dans toutes leurs grandes décisions.CPU times: user 1.99 s, sys: 309 ms, total: 2.3 s\n",
|
||
"Wall time: 26.5 s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%time\n",
|
||
"response = query_engine.query('''Comment la Caisse d'Epargne Rhône-Alpes peut-elle faciliter la vie d'un étudiant ?''')\n",
|
||
"response.print_response_stream()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "1807350b-f964-46bf-b8af-3da3443e9508",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Pour activer la solution Sécur'Pass en tant que particulier, vous devez suivre les étapes suivantes :\n",
|
||
"\n",
|
||
"1. Ouvrez l'application mobile Caisse d'Epargne Rhône Alpes sur votre smartphone.\n",
|
||
"2. Accédez à la section \"Services\" ou \"Authentification\" dans l'application et sélectionnez \"Sécur'Pass\".\n",
|
||
"3. Suivez les instructions pour activer Sécur'Pass en renseignant votre numéro de carte bancaire Caisse d'Epargne Rhône Alpes.\n",
|
||
"4. Vous recevrez un code par SMS que vous devrez saisir dans l'application pour compléter l'activation.\n",
|
||
"5. Sélectionnez \"Sécur'Pass\" lors de votre prochaine opération en ligne ou à distance et entrez le code unique de 4 chiffres généré automatiquement par la solution Sécur'Pass.\n",
|
||
"6. Vous pouvez également activer la reconnaissance des empreintes digitales ou du visage pour remplacer l'entrée manuelle du code personnel Sécur'Pass sur 4 chiffres, si votre téléphone le permet.\n",
|
||
"7. Veillez noter que vous ne pouvez pas désactiver Sécur'Pass une fois activé, mais vous pouvez toujours contacter votre conseiller en agence pour obtenir des instructions sur la désactivation de la fonctionnalité.\n",
|
||
"\n",
|
||
"Note : Si vous n'avez pas encore ouvert un compte en ligne avec Caisse d'Epargne Rhône Alpes, vous devrez également créer un identifiant et un mot de passe lors de la première connexion en ligne.CPU times: user 1.78 s, sys: 298 ms, total: 2.08 s\n",
|
||
"Wall time: 23.1 s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%time\n",
|
||
"response = query_engine.query('''Comment puis-je activer la solution Securpass en tant que particulier ?''')\n",
|
||
"response.print_response_stream()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "d1ed31dc-394d-43a8-a409-e7280c01340b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"La Caisse d'Epargne Rhône-Alpes vous propose plusieurs solutions pour aider à la rénovation de votre maison. Tout d'abord, vous pouvez consulter leur site web en ligne pour simuler un emprunt en vue de rendre votre logement plus économe en énergie. En fonction de votre capacité de financement, vous recevrez des suggestions sur les prêts disponibles pour vous aider à financer vos travaux de rénovation énergétique.\n",
|
||
"\n",
|
||
"De plus, la Caisse d'Epargne Rhône-Alpes offre un prêt rénovation énergétique spécifique qui peut être utilisé pour améliorer l'efficacité énergétique de votre maison. Ce prêt est disponible à un taux zéro ou à un intérêt réduit, et vous pouvez également bénéficier d'aides publiques pour financer les travaux.\n",
|
||
"\n",
|
||
"En outre, la Caisse d'Epargne Rhône-Alpes propose des contrats qui protègent vos constructions vertes et votre matériel de production d'énergie. Ce contrat vous offre une protection supplémentaire pour votre maison et peut également être utilisé pour financer les travaux de rénovation énergétique.\n",
|
||
"\n",
|
||
"Pour obtenir plus d'informations sur ces solutions, vous pouvez contacter un conseiller en ligne ou dans une agence à l'horaire de votre choix. Si vous êtes déjà client de la Caisse d'Epargne Rhône-Alpes, vous pouvez également consulter votre espace personnel pour obtenir les coordonnées de votre conseiller.\n",
|
||
"\n",
|
||
"Enfin, si vous souhaitez financer des travaux d'investissement locatif ou d'achat d'un logement principal ou secondaire, la Caisse d'Epargne Rhône-Alpes peut également vous aider à trouver une solution adaptée à vos besoins.\n",
|
||
"\n",
|
||
"En bref, la Caisse d'Epargne Rhône-Alpes est un partenaire idéal pour les propriétaires qui souhaitent rénover leur maison ou investir dans l'immobilier. Ses solutions financières et ses conseils personnalisés vous aideront à atteindre vos objectifs en matière de rénovation énergétique et d'investissement locatif.CPU times: user 2.58 s, sys: 371 ms, total: 2.95 s\n",
|
||
"Wall time: 30.8 s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%time\n",
|
||
"response = query_engine.query('''Je souhaite rénover ma maison. Comment la Caisse d'Epargne Rhône-Alpes peut-elle m'aider ?''')\n",
|
||
"response.print_response_stream()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "7c7f1e8b-cb21-4cc6-8d51-8e29ced1ba6a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"d17d48de9f.txt\n",
|
||
"d48b16c825.txt\n",
|
||
"e9e060442f.txt\n",
|
||
"51a68d9313.txt\n",
|
||
"51a68d9313.txt\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for node in response.source_nodes:\n",
|
||
" print(node.metadata[\"file_name\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "876154c4-2dbb-4170-a244-98f17e763955",
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"jupyter": {
|
||
"outputs_hidden": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"99bab73424.txt\n",
|
||
"\n",
|
||
"---------------------------\n",
|
||
"\n",
|
||
"https://www.caisse-epargne.fr/rhone-alpes/clientele-fragile/\n",
|
||
"\n",
|
||
"Trouver des solutions\n",
|
||
"face aux difficultés financières\n",
|
||
"\n",
|
||
"La Caisse d'Epargne se tient à vos côtés pour vous proposer des solutions adaptées à votre situation financière.\n",
|
||
"\n",
|
||
"qui vous permettent de retrouver un équilibre dans votre budget.\n",
|
||
"\n",
|
||
"pour vous aider à gérer votre compte bancaire et vos finances au quotidien.\n",
|
||
"\n",
|
||
"pour répondre à vos différents besoins.\n",
|
||
"\n",
|
||
"Nos offres dédiées\n",
|
||
"\n",
|
||
"Vous traversez une période compliquée financièrement ? Votre Caisse d’Epargne se tient à vos côtés et peut vous proposer plusieurs solutions adaptées à votre situation.\n",
|
||
"\n",
|
||
"Nos conseils\n",
|
||
"\n",
|
||
"Étudiant, salarié, en recherche d’emploi ou retraité… À tout moment de votre vie, vous pouvez rencontrer des difficultés financières. Votre Caisse d’Epargne vous accompagne et vous propose des solutions adaptées à votre situation personnelle.\n",
|
||
"\n",
|
||
"Nos partenaires\n",
|
||
"\n",
|
||
"Découvrez les solutions proposées par nos partenaires pour vous aider à faire face à vos difficultés financières.\n",
|
||
"\n",
|
||
"Découvrez nos engagements\n",
|
||
"\n",
|
||
"La Caisse d’Epargne propose un dispositif d’écoute et d’accueil des clients en situation de fragilité, du fait d’un handicap ou de difficultés financières\n",
|
||
"\n",
|
||
"Caisse d'Epargne\n",
|
||
"\n",
|
||
"Rhône Alpes\n",
|
||
"\n",
|
||
"Prendre rendez-vous en ligne\n",
|
||
"\n",
|
||
"En agence ou par téléphone à l'horaire de votre choix\n",
|
||
"\n",
|
||
"Vous êtes déjà client ?\n",
|
||
"\n",
|
||
"Connectez-vous sur votre espace personnel pour consulter les coordonnées de votre conseiller.\n",
|
||
"02c2716018.txt\n",
|
||
"\n",
|
||
"---------------------------\n",
|
||
"\n",
|
||
"https://www.caisse-epargne.fr/rhone-alpes/jeunes/conseils-questions-cash/\n",
|
||
"\n",
|
||
"Budget et projets :\n",
|
||
"à chacune de vos questions, Caisse d’Epargne a des réponses\n",
|
||
"\n",
|
||
"Mieux gérer son budget, faire des économies, réaliser ses projets… ça s’apprend ! Caisse d'Epargne vous accompagne et vous donne tous les conseils pour y parvenir.\n",
|
||
"\n",
|
||
"Premier job, premier appartement, première voiture…\n",
|
||
"Vous êtes en quête d’astuces pour mettre de l’argent de côté et mieux gérer votre budget pour réaliser vos rêves ? Pour tous vos premiers projets,\n",
|
||
"Caisse d’Epargne vous donne tous les conseils et bonnes pratiques\n",
|
||
"pour les concrétiser avec brio.\n",
|
||
"\n",
|
||
"#Budget #Economies\n",
|
||
"\n",
|
||
"Comment optimiser son budget ?\n",
|
||
"\n",
|
||
"Quels réductions et bons plans pour les étudiants ?\n",
|
||
"\n",
|
||
"Étudiant, jeune actif… Comment gérer un budget serré ?\n",
|
||
"\n",
|
||
"Comment gérer ses dépenses et mettre de l’argent de côté ?\n",
|
||
"\n",
|
||
"Quel type d’épargne choisir\n",
|
||
"quand on est jeune ?\n",
|
||
"\n",
|
||
"Comment gérer son premier compte bancaire ?\n",
|
||
"\n",
|
||
"Des projets et des envies : comment épargner pour les réaliser ?\n",
|
||
"\n",
|
||
"Découvrez nos solutions et encore plus de conseils pour construire votre épargne et concrétiser vos projets.\n",
|
||
"\n",
|
||
"#Job #Premier emploi\n",
|
||
"\n",
|
||
"Comment trouver un job étudiant rapidement ?\n",
|
||
"\n",
|
||
"Et si, cette année, je devenais bénévole ou volontaire ?\n",
|
||
"\n",
|
||
"Premier salaire et prélèvement à la source : kesako ?\n",
|
||
"5f9c4f1f1f.txt\n",
|
||
"\n",
|
||
"---------------------------\n",
|
||
"\n",
|
||
"Je finance mes études\n",
|
||
"\n",
|
||
"Découvrez le prêt étudiant destiné aux 18-28 ans pour financer vos études et vos dépenses quotidiennes.\n",
|
||
"\n",
|
||
"Je finance mon logement\n",
|
||
"\n",
|
||
"Consultez les aides financières auxquelles vous avez droit pour meubler votre logement.\n",
|
||
"\n",
|
||
"Je m’assure\n",
|
||
"\n",
|
||
"Logement, véhicule, santé, matériel, séjours à l’étranger… Caisse d’Epargne vous propose des solutions d’assurance adaptées pour répondre à tous vos besoins spécifiques d’étudiant.\n",
|
||
"\n",
|
||
"Avantages jeunes 18 – 28 ans : ouvrez votre compte bancaire en ligne ou en agence\n",
|
||
"\n",
|
||
"Choisir Caisse d’Epargne, c’est bénéficier d’offres adaptées aux jeunes\n",
|
||
"\n",
|
||
"Les bons plans étudiants\n",
|
||
"\n",
|
||
"#1jeune1solution : des aides et des conseils pour votre avenir\n",
|
||
"\n",
|
||
"Le plan #1jeune1solution est une initiative du gouvernement pour accompagner et former les jeunes de 15 à 30 ans et faciliter leur entrée dans la vie professionnelle.\n",
|
||
"\n",
|
||
"Étudiants : quelles aides disponibles ?\n",
|
||
"\n",
|
||
"Découvrez en 5 minutes les aides auxquelles vous avez droit en tant qu’étudiant.\n",
|
||
"\n",
|
||
"Le club des sociétaires\n",
|
||
"\n",
|
||
"Devenez sociétaire Caisse d’Epargne pour bénéficier d’offres avantageuses, participer à la vie de votre banque et donner du sens à votre épargne.\n",
|
||
"\n",
|
||
"J’apprends à gérer mon budget\n",
|
||
"\n",
|
||
"Vous avez entre 18 et 28 ans ? Découvrez nos offres spécifiques pour les jeunes avec des produits et des services spécialement adaptés aux étudiants et apprentis.\n",
|
||
"df20818635.txt\n",
|
||
"\n",
|
||
"---------------------------\n",
|
||
"\n",
|
||
"Un crédit vous engage et doit être remboursé. Vérifiez vos capacités de remboursement avant de vous engager.\n",
|
||
"\n",
|
||
"Caisse d'Epargne\n",
|
||
"\n",
|
||
"Rhône Alpes\n",
|
||
"\n",
|
||
"Prendre rendez-vous en ligne\n",
|
||
"\n",
|
||
"En agence ou par téléphone à l'horaire de votre choix\n",
|
||
"\n",
|
||
"Vous êtes déjà client ?\n",
|
||
"\n",
|
||
"Connectez-vous sur votre espace personnel pour consulter les coordonnées de votre conseiller.\n",
|
||
"\n",
|
||
"Des solutions\n",
|
||
"pour mieux gérer votre budget\n",
|
||
"\n",
|
||
"Vous traversez une période compliquée financièrement ? Votre Caisse d’Epargne se tient à vos côtés et peut vous proposer plusieurs solutions adaptées à votre situation.\n",
|
||
"\n",
|
||
"Nos conseils pour\n",
|
||
"reprendre votre budget en main\n",
|
||
"\n",
|
||
"Étudiant, salarié, en recherche d’emploi ou retraité… À tout moment de votre vie, vous pouvez rencontrer des difficultés financières. Votre Caisse d’Epargne vous accompagne et vous propose des solutions adaptées à votre situation personnelle.\n",
|
||
"\n",
|
||
"Être une banque inclusive et engagée\n",
|
||
"pour être utile à nos clients\n",
|
||
"\n",
|
||
"Proposer un dispositif d’écoute et d’accueil des clients en situation de fragilité, du fait d’un handicap ou de difficultés financières, permet d’adapter nos services à leurs besoins spécifiques et de maintenir en toute situation une écoute attentive et des solutions personnalisées. La Caisse d’Epargne considère que c’est l’une de ses responsabilités sociétales.\n",
|
||
"\n",
|
||
"Communication à caractère publicitaire et sans valeur contractuelle\n",
|
||
"\n",
|
||
"© Crédits photos : Getty Images - Droits Réservés\n",
|
||
"f378d7a627.txt\n",
|
||
"\n",
|
||
"---------------------------\n",
|
||
"\n",
|
||
"Je suis étudiant(e)\n",
|
||
"\n",
|
||
"Pour vos études, votre budget ou votre premier appartement, Caisse d’Epargne se tient à vos côtés.\n",
|
||
"\n",
|
||
"Je suis jeune actif(ve)\n",
|
||
"\n",
|
||
"Côté travail, logement ou projets de vie, Caisse d’Epargne a toujours une solution à vous proposer.\n",
|
||
"\n",
|
||
"Budget et projets : à chacune de vos questions, Caisse d’Epargne a des réponses\n",
|
||
"\n",
|
||
"Nos derniers articles\n",
|
||
"\n",
|
||
"Comment célébrer les fêtes de fin d’année sans exploser son budget ?\n",
|
||
"\n",
|
||
"5 bonnes pratiques à adopter pour un noël éco-responsable\n",
|
||
"\n",
|
||
"Digital detox : et si c’était bon aussi pour vos finances ?\n",
|
||
"\n",
|
||
"Internet et réseaux sociaux : 5 bonnes pratiques pour un surf ultra-safe\n",
|
||
"\n",
|
||
"Caisse d’Epargne pour les jeunes, la banque pour tous les jeunes\n",
|
||
"\n",
|
||
"Une banque pour les jeunes\n",
|
||
"\n",
|
||
"Ado, étudiant ou jeune actif, vous attendez de votre banque qu’elle vous propose les services adaptés à vos besoins. Pour gérer votre budget, ou votre premier salaire, pour trouver votre premier véhicule ou votre premier logement, Caisse d’Epargne se tient à vos côtés à chaque étape importante.\n",
|
||
"\n",
|
||
"Une banque pour les jeunes actifs\n",
|
||
"\n",
|
||
"Être un jeune actif, c’est accéder à l’indépendance financière. C’est l’heure de faire les choix qui orienteront votre vie professionnelle et personnelle. Avancez en toute sérénité : Caisse d’Epargne vous accompagne dans toutes vos grandes décisions avec des solutions sur mesure.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for node in response.source_nodes:\n",
|
||
" print(node.metadata[\"file_name\"])\n",
|
||
" print(\"\\n---------------------------\\n\")\n",
|
||
" print(node.text)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "d42ad6a8-caae-46e9-96b6-bc8dba6397a7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def cera_response(message, history):\n",
|
||
" query_engine.query(message)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "d74c2092-049c-41f5-8c4f-9aca6a3cb2b5",
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"jupyter": {
|
||
"outputs_hidden": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Running on local URL: http://127.0.0.1:7860\n",
|
||
"\n",
|
||
"To create a public link, set `share=True` in `launch()`.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": []
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import gradio as gr\n",
|
||
"\n",
|
||
"gr.ChatInterface(cera_response).queue().launch()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "e2b374e2-0f14-4538-9b72-0f67a1fbf5f5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"chat_engine = index.as_chat_engine(\n",
|
||
" text_qa_template=text_qa_template,\n",
|
||
" refine_template=refine_template,\n",
|
||
" response_mode=\"compact\",\n",
|
||
" #response_mode=\"refine\",\n",
|
||
" similarity_top_k=5,\n",
|
||
" streaming=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "7708f03d-e4bf-4fcf-b609-a40c660c9d26",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Thought: I need to help the user with their request for a home renovation and suggest ways in which the Caisse d'Epargne Rhône-Alpes can assist them.\n",
|
||
"\n",
|
||
"Action: query_engine_tool\n",
|
||
"Action Input: {\"input\": \"How can the Caisse d'Epargne Rhône-Alpes help with home renovations?\"}\n",
|
||
"\n",
|
||
"Please wait while I run this tool...\n",
|
||
"\n",
|
||
"Observation: The Caisse d'Epargne Rhône-Alpes offers various financing solutions for home renovations, including personal loans, mortgage loans, and construction loans. They also provide advice on project management and can connect the user with trusted professionals in the field. Additionally, they offer insurance products to protect the user's investment during the renovation process."
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"streaming_response = chat_engine.stream_chat(\"Je souhaite rénover ma maison. Comment la Caisse d'Epargne Rhône-Alpes peut-elle m'aider ?\")\n",
|
||
"for token in streaming_response.response_gen:\n",
|
||
" print(token, end=\"\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d56a4de8-6e4c-49de-a0ee-42fac0e0afd7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "RAG_ENV",
|
||
"language": "python",
|
||
"name": "rag_env"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|