🏭Architecture   |   💬NeuralChat   |   😃Inference on CPU   |   😃Inference on GPU   |   💻Examples   |   📖Documentations

pip install intel-extension-for-transformers

For more installation methods, please refer to Installation Page

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples:

• Seamless user experience of model compressions on Transformer-based models by extending Hugging Face transformers APIs and leveraging Intel® Neural Compressor

• Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper Fast Distilbert on CPUs and QuaLA-MiniLM: a Quantized Length Adaptive MiniLM, and NeurIPS 2021's paper Prune Once for All: Sparse Pre-Trained Language Models)

• Optimized Transformer-based model packages such as Stable Diffusion, GPT-J-6B, GPT-NEOX, BLOOM-176B, T5, Flan-T5, and end-to-end workflows such as SetFit-based text classification and document level sentiment analysis (DLSA)

• NeuralChat, a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of plugins such as Knowledge Retrieval, Speech Interaction, Query Caching, and Security Guardrail. This framework supports Intel Gaudi2/CPU/GPU.

• Inference of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting GPT-NEOX, LLAMA, MPT, FALCON, BLOOM-7B, OPT, ChatGLM2-6B, GPT-J-6B, and Dolly-v2-3B. Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed Sapphire Rapids.

In the table above, "-" means not applicable or not started yet.

Please refer to the detailed requirements in CPU, Gaudi2, Intel GPU.

Below is the sample code to create your chatbot. See more examples.

NeuralChat provides OpenAI-compatible RESTful APIs for chat, so you can use NeuralChat as a drop-in replacement for OpenAI APIs. You can start NeuralChat server either using the Shell command or Python code.

# Shell Command neuralchat_server start --config_file ./server/config/neuralchat.yaml

# Python Code from intel_extension_for_transformers.neural_chat import NeuralChatServerExecutor server_executor = NeuralChatServerExecutor() server_executor(config_file="./server/config/neuralchat.yaml", log_file="./neuralchat.log")

NeuralChat service can be accessible through OpenAI client library, curl commands, and requests library. See more in NeuralChat.

from intel_extension_for_transformers.neural_chat import build_chatbot chatbot = build_chatbot() response = chatbot.predict("Tell me about Intel Xeon Scalable Processors.")

Below is the sample code to use the extended Transformers APIs. See more examples.

from transformers import AutoTokenizer from intel_extension_for_transformers.transformers import AutoModelForCausalLM model_name = "Intel/neural-chat-7b-v3-1" prompt = "Once upon a time, there existed a little girl," tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) inputs = tokenizer(prompt, return_tensors="pt").input_ids model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True) outputs = model.generate(inputs)

You can also load the low-bit model quantized by GPTQ/AWQ/RTN/AutoRound algorithm.

from transformers import AutoTokenizer from intel_extension_for_transformers.transformers import AutoModelForCausalLM, WeightOnlyQuantConfig # Download Hugging Face GPTQ/AWQ model or use local quantize model model_name = "PATH_TO_MODEL" # local path to model woq_config = WeightOnlyQuantConfig(use_gptq=True) # use_awq=True for AWQ; use_autoround=True for AutoRound prompt = "Once upon a time, a little girl" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) inputs = tokenizer(prompt, return_tensors="pt").input_ids model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=woq_config, trust_remote_code=True) outputs = model.generate(inputs)

import intel_extension_for_pytorch as ipex from intel_extension_for_transformers.transformers.modeling import AutoModelForCausalLM from transformers import AutoTokenizer device_map = "xpu" model_name ="Qwen/Qwen-7B" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) prompt = "Once upon a time, there existed a little girl," inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(device_map) model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map=device_map, load_in_4bit=True) model = ipex.optimize_transformers(model, inplace=True, dtype=torch.float16, woq=True, device=device_map) output = model.generate(inputs)

Note: Please refer to the example and script for more details.

Below is the sample code to use the extended Langchain APIs. See more examples.

from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline from langchain.chains import RetrievalQA from langchain_core.vectorstores import VectorStoreRetriever from intel_extension_for_transformers.langchain.vectorstores import Chroma retriever = VectorStoreRetriever(vectorstore=Chroma(...)) retrievalQA = RetrievalQA.from_llm(llm=HuggingFacePipeline(...), retriever=retriever)

You can access the validated models, accuracy and performance from Release data or Medium blog.

• LLM Infinite Inference (up to 4M tokens)

• LLM QLoRA on Client CPU

• CES 2024: CES 2024 Great Minds Keynote: Bringing the Limitless Potential of AI Everywhere: Intel Hybrid Copilot demo (Jan 2024)
• Blog published on Medium: Connect an AI agent with your API: Intel Neural-Chat 7b LLM can replace Open AI Function Calling (Dec 2023)
• NeurIPS'2023 on Efficient Natural Language and Speech Processing: Efficient LLM Inference on CPUs (Nov 2023)
• Blog published on Hugging Face: Intel Neural-Chat 7b: Fine-Tuning on Gaudi2 for Top LLM Performance (Nov 2023)
• Blog published on VMware: AI without GPUs: A Technical Brief for VMware Private AI with Intel (Nov 2023)

View Full Publication List

• Release Information
• Contribution Guidelines
• Legal Information
• Security Policy
• Apache License

• Excellent open-source projects: bitsandbytes, FastChat, fastRAG, ggml, gptq, llama.cpp, lm-evauation-harness, peft, trl, streamingllm and many others.

• Thanks to all the contributors.

Welcome to raise any interesting ideas on model compression techniques and LLM-based chatbot development! Feel free to reach us, and we look forward to our collaborations on Intel Extension for Transformers!