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* Add arc demo in main readme * Small style fix * Realize using table * Update based on comments * Small update * Try to solve with height problem * Small fix * Update demo for inner llm readme * Update demo video for readthedocs * Small fix * Update based on comments
299 lines
13 KiB
Markdown
299 lines
13 KiB
Markdown
## BigDL-LLM
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**`bigdl-llm`** is a library for running ***LLM*** (large language model) on your Intel ***laptop*** or ***GPU*** using INT4 with very low latency[^1] (for any Hugging Face *Transformers* model).
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> *It is built on top of the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [gptq](https://github.com/IST-DASLab/gptq), [ggml](https://github.com/ggerganov/ggml), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq_for_llama](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [chatglm.cpp](https://github.com/li-plus/chatglm.cpp), [redpajama.cpp](https://github.com/togethercomputer/redpajama.cpp), [gptneox.cpp](https://github.com/byroneverson/gptneox.cpp), [bloomz.cpp](https://github.com/NouamaneTazi/bloomz.cpp/), etc.*
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### Latest update
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- `bigdl-llm` now supports Intel Arc or Flex GPU; see the the latest GPU examples [here](example/gpu).
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### Demos
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See the ***optimized performance*** of `chatglm2-6b` and `llama-2-13b-chat` models on 12th Gen Intel Core CPU and Intel Arc GPU below.
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<table width="100%">
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<tr>
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<td align="center" colspan="2">12th Gen Intel Core CPU</td>
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<td align="center" colspan="2">Intel Arc GPU</td>
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</tr>
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<tr>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif" ></a>
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</td>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"></a>
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</td>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"></a>
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</td>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"></a>
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</td>
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</tr>
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<tr>
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<td align="center" width="25%"><code>chatglm2-6b</code></td>
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<td align="center" width="25%"><code>llama-2-13b-chat</code></td>
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<td align="center" width="25%"><code>chatglm2-6b</code></td>
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<td align="center" width="25%"><code>llama-2-13b-chat</code></td>
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</tr>
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</table>
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### Verified models
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We may use any Hugging Face Transfomer models on `bigdl-llm`, and the following models have been verified on Intel laptops.
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| Model | Example |
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|-----------|----------------------------------------------------------|
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| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* | [link1](example/transformers/native_int4), [link2](example/transformers/transformers_int4/vicuna) |
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| LLaMA 2 | [link](example/transformers/transformers_int4/llama2) |
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| MPT | [link](example/transformers/transformers_int4/mpt) |
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| Falcon | [link](example/transformers/transformers_int4/falcon) |
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| ChatGLM | [link](example/transformers/transformers_int4/chatglm) |
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| ChatGLM2 | [link](example/transformers/transformers_int4/chatglm2) |
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| Qwen | [link](example/transformers/transformers_int4/qwen) |
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| MOSS | [link](example/transformers/transformers_int4/moss) |
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| Baichuan | [link](example/transformers/transformers_int4/baichuan) |
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| Dolly-v1 | [link](example/transformers/transformers_int4/dolly_v1) |
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| Dolly-v2 | [link](example/transformers/transformers_int4/dolly_v2) |
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| RedPajama | [link1](example/transformers/native_int4), [link2](example/transformers/transformers_int4/redpajama) |
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| Phoenix | [link1](example/transformers/native_int4), [link2](example/transformers/transformers_int4/phoenix) |
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| StarCoder | [link1](example/transformers/native_int4), [link2](example/transformers/transformers_int4/starcoder) |
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| InternLM | [link](example/transformers/transformers_int4/internlm) |
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| Whisper | [link](example/transformers/transformers_int4/whisper) |
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### Working with `bigdl-llm`
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<details><summary>Table of Contents</summary>
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- [Install](#install)
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- [Download Model](#download-model)
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- [Run Model](#run-model)
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- [Hugging Face `transformers` API](#hugging-face-transformers-api)
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- [LangChain API](#langchain-api)
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- [CLI Tool](#cli-tool)
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- [`bigdl-llm` API Doc](#bigdl-llm-api-doc)
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- [`bigdl-llm` Dependence](#bigdl-llm-dependence)
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</details>
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#### Install
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You may install **`bigdl-llm`** as follows:
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```bash
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pip install --pre --upgrade bigdl-llm[all]
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```
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#### Download Model
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You may download any PyTorch model in Hugging Face *Transformers* format (including *FP16* or *FP32* or *GPTQ-4bit*).
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#### Run Model
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You may run the models using **`bigdl-llm`** through one of the following APIs:
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1. [Hugging Face `transformers` API](#hugging-face-transformers-api)
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2. [LangChain API](#langchain-api)
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3. [CLI (command line interface) Tool](#cli-tool)
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#### Hugging Face `transformers` API
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You may run the models using `transformers`-style API in `bigdl-llm`.
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- ##### Using Hugging Face `transformers` INT4 format
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You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
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```python
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#load Hugging Face Transformers model with INT4 optimizations
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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```
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After loading the Hugging Face Transformers model, you may easily run the optimized model as follows.
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```python
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#run the optimized model
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...)
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids)
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```
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See the complete examples [here](example/transformers/transformers_int4/).
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>**Note**: You may apply more low bit optimizations (including INT8, INT5 and INT4) as follows:
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>```python
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>model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int5")
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>```
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>See the complete example [here](example/transformers/transformers_low_bit/).
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After the model is optimizaed using INT4 (or INT8/INT5), you may save and load the optimized model as follows:
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```python
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model.save_low_bit(model_path)
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new_model = AutoModelForCausalLM.load_low_bit(model_path)
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```
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See the example [here](example/transformers/transformers_low_bit/).
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- ##### Using native INT4 format
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You may also convert Hugging Face *Transformers* models into native INT4 format for maximum performance as follows.
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>**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).
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```python
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#convert the model
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from bigdl.llm import llm_convert
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bigdl_llm_path = llm_convert(model='/path/to/model/',
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outfile='/path/to/output/', outtype='int4', model_family="llama")
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#load the converted model
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#switch to ChatGLMForCausalLM/GptneoxForCausalLM/BloomForCausalLM/StarcoderForCausalLM to load other models
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from bigdl.llm.transformers import LlamaForCausalLM
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llm = LlamaForCausalLM.from_pretrained("/path/to/output/model.bin", native=True, ...)
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#run the converted model
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input_ids = llm.tokenize(prompt)
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output_ids = llm.generate(input_ids, ...)
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output = llm.batch_decode(output_ids)
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```
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See the complete example [here](example/transformers/native_int4/native_int4_pipeline.py).
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#### LangChain API
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You may run the models using the LangChain API in `bigdl-llm`.
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- **Using Hugging Face `transformers` INT4 format**
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You may run any Hugging Face *Transformers* model (with INT4 optimiztions applied) using the LangChain API as follows:
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```python
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from bigdl.llm.langchain.llms import TransformersLLM
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from bigdl.llm.langchain.embeddings import TransformersEmbeddings
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from langchain.chains.question_answering import load_qa_chain
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embeddings = TransformersEmbeddings.from_model_id(model_id=model_path)
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bigdl_llm = TransformersLLM.from_model_id(model_id=model_path, ...)
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doc_chain = load_qa_chain(bigdl_llm, ...)
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output = doc_chain.run(...)
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```
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See the examples [here](example/langchain/transformers_int4).
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- **Using native INT4 format**
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You may also convert Hugging Face *Transformers* models into *native INT4* format, and then run the converted models using the LangChain API as follows.
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>**Notes**:
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>* Currently only llama/bloom/gptneox/starcoder/chatglm model families are supported; for other models, you may use the Hugging Face `transformers` INT4 format as described above).
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>* You may choose the corresponding API developed for specific native models to load the converted model.
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```python
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from bigdl.llm.langchain.llms import LlamaLLM
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from bigdl.llm.langchain.embeddings import LlamaEmbeddings
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from langchain.chains.question_answering import load_qa_chain
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#switch to ChatGLMEmbeddings/GptneoxEmbeddings/BloomEmbeddings/StarcoderEmbeddings to load other models
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embeddings = LlamaEmbeddings(model_path='/path/to/converted/model.bin')
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#switch to ChatGLMLLM/GptneoxLLM/BloomLLM/StarcoderLLM to load other models
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bigdl_llm = LlamaLLM(model_path='/path/to/converted/model.bin')
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doc_chain = load_qa_chain(bigdl_llm, ...)
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doc_chain.run(...)
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```
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See the examples [here](example/langchain/native_int4).
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#### CLI Tool
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>**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.
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- ##### Convert model
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You may convert the downloaded model into native INT4 format using `llm-convert`.
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```bash
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#convert PyTorch (fp16 or fp32) model;
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#llama/bloom/gptneox/starcoder model family is currently supported
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llm-convert "/path/to/model/" --model-format pth --model-family "bloom" --outfile "/path/to/output/"
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#convert GPTQ-4bit model
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#only llama model family is currently supported
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llm-convert "/path/to/model/" --model-format gptq --model-family "llama" --outfile "/path/to/output/"
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```
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- ##### Run model
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You may run the converted model using `llm-cli` or `llm-chat` (*built on top of `main.cpp` in [llama.cpp](https://github.com/ggerganov/llama.cpp)*)
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```bash
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#help
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#llama/bloom/gptneox/starcoder model family is currently supported
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llm-cli -x gptneox -h
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#text completion
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#llama/bloom/gptneox/starcoder model family is currently supported
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llm-cli -t 16 -x gptneox -m "/path/to/output/model.bin" -p 'Once upon a time,'
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#chat mode
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#llama/gptneox model family is currently supported
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llm-chat -m "/path/to/output/model.bin" -x llama
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```
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#### CLI Tool
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>**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.
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- ##### Convert model
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You may convert the downloaded model into native INT4 format using `llm-convert`.
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```bash
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#convert PyTorch (fp16 or fp32) model;
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#llama/bloom/gptneox/starcoder model family is currently supported
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llm-convert "/path/to/model/" --model-format pth --model-family "bloom" --outfile "/path/to/output/"
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#convert GPTQ-4bit model
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#only llama model family is currently supported
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llm-convert "/path/to/model/" --model-format gptq --model-family "llama" --outfile "/path/to/output/"
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```
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- ##### Run model
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You may run the converted model using `llm-cli` or `llm-chat` (*built on top of `main.cpp` in [llama.cpp](https://github.com/ggerganov/llama.cpp)*)
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```bash
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#help
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#llama/bloom/gptneox/starcoder model family is currently supported
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llm-cli -x gptneox -h
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#text completion
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#llama/bloom/gptneox/starcoder model family is currently supported
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llm-cli -t 16 -x gptneox -m "/path/to/output/model.bin" -p 'Once upon a time,'
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#chat mode
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#llama/gptneox model family is currently supported
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llm-chat -m "/path/to/output/model.bin" -x llama
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```
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### `bigdl-llm` API Doc
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See the inital `bigdl-llm` API Doc [here](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).
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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.
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### `bigdl-llm` Dependencies
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The native code/lib in `bigdl-llm` has been built using the following tools.
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Note that lower `LIBC` version on your Linux system may be incompatible with `bigdl-llm`.
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| Model family | Platform | Compiler | GLIBC |
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| ------------ | -------- | ------------------ | ----- |
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| llama | Linux | GCC 11.2.1 | 2.17 |
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| llama | Windows | MSVC 19.36.32532.0 | |
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| llama | Windows | GCC 13.1.0 | |
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| gptneox | Linux | GCC 11.2.1 | 2.17 |
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| gptneox | Windows | MSVC 19.36.32532.0 | |
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| gptneox | Windows | GCC 13.1.0 | |
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| bloom | Linux | GCC 11.2.1 | 2.29 |
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| bloom | Windows | MSVC 19.36.32532.0 | |
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| bloom | Windows | GCC 13.1.0 | |
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| starcoder | Linux | GCC 11.2.1 | 2.29 |
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| starcoder | Windows | MSVC 19.36.32532.0 | |
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| starcoder | Windows | GCC 13.1.0 | |
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