ipex-llm/python/llm

BigDL-LLM

bigdl-llm is a library for running LLM (large language model) on your Intel laptop using INT4 with very low latency¹ (for any Hugging Face Transformers model).

(It is built on top of the excellent work of llama.cpp, gptq, ggml, llama-cpp-python, gptq_for_llama, bitsandbytes, chatglm.cpp, redpajama.cpp, gptneox.cpp, bloomz.cpp, etc.)

Demos

See the optimized performance of chatglm2-6b, llama-2-13b-chat, and starcoder-15b models on a 12th Gen Intel Core CPU below.

Verified models

We may use any Hugging Face Transfomer models on bigdl-llm, and the following models have been verified on Intel laptops.

Model	Example
LLaMA (such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)	link1, link2
LLaMA 2	link
MPT	link
Falcon	link
ChatGLM	link
ChatGLM2	link
Qwen	link
MOSS	link
Baichuan	link
Dolly-v1	link
Dolly-v2	link
RedPajama	link1, link2
Phoenix	link1, link2
StarCoder	link1, link2
InternLM	link
Whisper	link

Working with bigdl-llm

Table of Contents

• Install
• Download Model
• Run Model
  • Hugging Face transformers API
  • LangChain API
  • CLI Tool
• bigdl-llm API Doc
• bigdl-llm Dependence

Install

You may install bigdl-llm as follows:

pip install --pre --upgrade bigdl-llm[all]

Download Model

You may download any PyTorch model in Hugging Face Transformers format (including FP16 or FP32 or GPTQ-4bit).

Run Model

You may run the models using bigdl-llm through one of the following APIs:

1. Hugging Face transformers API
2. LangChain API
3. CLI (command line interface) Tool

Hugging Face transformers API

You may run the models using transformers-style API in bigdl-llm.

• Using Hugging Face transformers INT4 format

  You may apply INT4 optimizations to any Hugging Face Transformers models as follows.

  #load Hugging Face Transformers model with INT4 optimizations
  from bigdl.llm.transformers import AutoModelForCausalLM
  model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
  

  After loading the Hugging Face Transformers model, you may easily run the optimized model as follows.

  #run the optimized model
  from transformers import AutoTokenizer
  tokenizer = AutoTokenizer.from_pretrained(model_path)
  input_ids = tokenizer.encode(input_str, ...)
  output_ids = model.generate(input_ids, ...)
  output = tokenizer.batch_decode(output_ids)
  

  See the complete examples here.

  Note

  : You may apply more low bit optimizations (including INT8, INT5 and INT4) as follows:

  model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int5")
  

  See the complete example here.

  After the model is optimizaed using INT4 (or INT8/INT5), you may save and load the optimized model as follows:

  model.save_low_bit(model_path)
  
  new_model = AutoModelForCausalLM.load_low_bit(model_path)
  

  See the example here.

• Using native INT4 format

  You may also convert Hugging Face Transformers models into native INT4 format for maximum performance as follows.

  Notes: Currently only llama/bloom/gptneox/starcoder/chatglm model families are supported; you may use the corresponding API to load the converted model. (For other models, you can use the Transformers INT4 format as described above).

  #convert the model
  from bigdl.llm import llm_convert
  bigdl_llm_path = llm_convert(model='/path/to/model/',
         outfile='/path/to/output/', outtype='int4', model_family="llama")
  
  #load the converted model
  #switch to ChatGLMForCausalLM/GptneoxForCausalLM/BloomForCausalLM/StarcoderForCausalLM to load other models
  from bigdl.llm.transformers import LlamaForCausalLM
  llm = LlamaForCausalLM.from_pretrained("/path/to/output/model.bin", native=True, ...)
  
  #run the converted model
  input_ids = llm.tokenize(prompt)
  output_ids = llm.generate(input_ids, ...)
  output = llm.batch_decode(output_ids)
  

  See the complete example here.

LangChain API

You may run the models using the LangChain API in bigdl-llm.

• Using Hugging Face transformers INT4 format

  You may run any Hugging Face Transformers model (with INT4 optimiztions applied) using the LangChain API as follows:

  from bigdl.llm.langchain.llms import TransformersLLM
  from bigdl.llm.langchain.embeddings import TransformersEmbeddings
  from langchain.chains.question_answering import load_qa_chain
  
  embeddings = TransformersEmbeddings.from_model_id(model_id=model_path)
  bigdl_llm = TransformersLLM.from_model_id(model_id=model_path, ...)
  
  doc_chain = load_qa_chain(bigdl_llm, ...)
  output = doc_chain.run(...)
  

  See the examples here.

• Using native INT4 format

  You may also convert Hugging Face Transformers models into native INT4 format (currently only llama/bloom/gptneox/starcoder model family is supported), and then run the converted models using the LangChain API as follows.

  Note

  : Currently only llama/bloom/gptneox/starcoder model family is supported; for other models, you may use the Transformers INT4 format as described above).

  from bigdl.llm.langchain.llms import BigdlNativeLLM
  from bigdl.llm.langchain.embeddings import BigdlNativeEmbeddings
  from langchain.chains.question_answering import load_qa_chain
  
  embeddings = BigdlNativeEmbeddings(model_path='/path/to/converted/model.bin',
                            model_family="llama",...)
  bigdl_llm = BigdlNativeLLM(model_path='/path/to/converted/model.bin',
                       model_family="llama",...)
  
  doc_chain = load_qa_chain(bigdl_llm, ...)
  doc_chain.run(...)
  

  See the examples here.

CLI Tool

Note

: Currently bigdl-llm CLI supports LLaMA (e.g., vicuna), GPT-NeoX (e.g., redpajama), BLOOM (e.g., pheonix) and GPT2 (e.g., starcoder) model architecture; for other models, you may use the transformers-style or LangChain APIs.

• Convert model

  You may convert the downloaded model into native INT4 format using llm-convert.

  #convert PyTorch (fp16 or fp32) model; 
  #llama/bloom/gptneox/starcoder model family is currently supported
  llm-convert "/path/to/model/" --model-format pth --model-family "bloom" --outfile "/path/to/output/"
  
  #convert GPTQ-4bit model
  #only llama model family is currently supported
  llm-convert "/path/to/model/" --model-format gptq --model-family "llama" --outfile "/path/to/output/"
  

• Run model

  You may run the converted model using llm-cli or llm-chat (built on top of main.cpp in llama.cpp)

  #help
  #llama/bloom/gptneox/starcoder model family is currently supported
  llm-cli -x gptneox -h
  
  #text completion
  #llama/bloom/gptneox/starcoder model family is currently supported
  llm-cli -t 16 -x gptneox -m "/path/to/output/model.bin" -p 'Once upon a time,'
  
  #chat mode
  #llama/gptneox model family is currently supported
  llm-chat -m "/path/to/output/model.bin" -x llama
  

CLI Tool

Note

: Currently bigdl-llm CLI supports LLaMA (e.g., vicuna), GPT-NeoX (e.g., redpajama), BLOOM (e.g., pheonix) and GPT2 (e.g., starcoder) model architecture; for other models, you may use the transformers-style or LangChain APIs.

• Convert model

  You may convert the downloaded model into native INT4 format using llm-convert.

  #convert PyTorch (fp16 or fp32) model; 
  #llama/bloom/gptneox/starcoder model family is currently supported
  llm-convert "/path/to/model/" --model-format pth --model-family "bloom" --outfile "/path/to/output/"
  
  #convert GPTQ-4bit model
  #only llama model family is currently supported
  llm-convert "/path/to/model/" --model-format gptq --model-family "llama" --outfile "/path/to/output/"
  

• Run model

  You may run the converted model using llm-cli or llm-chat (built on top of main.cpp in llama.cpp)

  #help
  #llama/bloom/gptneox/starcoder model family is currently supported
  llm-cli -x gptneox -h
  
  #text completion
  #llama/bloom/gptneox/starcoder model family is currently supported
  llm-cli -t 16 -x gptneox -m "/path/to/output/model.bin" -p 'Once upon a time,'
  
  #chat mode
  #llama/gptneox model family is currently supported
  llm-chat -m "/path/to/output/model.bin" -x llama
  

bigdl-llm API Doc

See the inital bigdl-llm API Doc here.

bigdl-llm Dependencies

The native code/lib in bigdl-llm has been built using the following tools. Note that lower LIBC version on your Linux system may be incompatible with bigdl-llm.

Model family	Platform	Compiler	GLIBC
llama	Linux	GCC 11.2.1	2.17
llama	Windows	MSVC 19.36.32532.0
llama	Windows	GCC 13.1.0
gptneox	Linux	GCC 11.2.1	2.17
gptneox	Windows	MSVC 19.36.32532.0
gptneox	Windows	GCC 13.1.0
bloom	Linux	GCC 11.2.1	2.29
bloom	Windows	MSVC 19.36.32532.0
bloom	Windows	GCC 13.1.0
starcoder	Linux	GCC 11.2.1	2.29
starcoder	Windows	MSVC 19.36.32532.0
starcoder	Windows	GCC 13.1.0

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1. Performance varies by use, configuration and other factors. bigdl-llm may not optimize to the same degree for non-Intel products. Learn more at www.Intel.com/PerformanceIndex. ↩︎