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fix RAG with quantized embeddings notebook (#24422)

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1. Fix HuggingfacePipeline import error to newer partner package
 2. Switch to IPEXModelForCausalLM for performance

There are no dependency changes since optimum intel is also needed for
QuantizedBiEncoderEmbeddings

---------

Co-authored-by: ccurme <chester.curme@gmail.com>
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rbrugaro 2024-07-22 06:44:03 -07:00 committed by GitHub
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commit 37b89fb7fc
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11
cookbook/rag_with_quantized_embeddings.ipynb
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@ -370,13 +370,14 @@
], ],
"source": [ "source": [
"import torch\n", "import torch\n",
"from langchain.llms.huggingface_pipeline import HuggingFacePipeline\n", "from langchain_huggingface.llms import HuggingFacePipeline\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n", "from optimum.intel.ipex import IPEXModelForCausalLM\n",
"from transformers import AutoTokenizer, pipeline\n",
"\n", "\n",
"model_id = \"Intel/neural-chat-7b-v3-3\"\n", "model_id = \"Intel/neural-chat-7b-v3-3\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n", "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(\n", "model = IPEXModelForCausalLM.from_pretrained(\n",
" model_id, device_map=\"auto\", torch_dtype=torch.bfloat16\n", " model_id, torch_dtype=torch.bfloat16, export=True\n",
")\n", ")\n",
"\n", "\n",
"pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=100)\n", "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=100)\n",
@ -581,7 +582,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.18" "version": "3.10.14"
} }
}, },
"nbformat": 4, "nbformat": 4,