本文主要是介绍【大模型】非常好用的大语言模型推理框架 bigdl-llm，现改名为 ipex-llm，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
非常好用的大语言模型推理框架 bigdl-llm，现改名为 ipex-llm
- bigdl-llm
- github地址
- 环境
- 安装依赖
- 下载测试模型
- 加载和优化预训练模型
- 使用优化后的模型构建一个聊天应用
bigdl-llm
IPEX-LLM is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency1.
- It is built on top of Intel Extension for PyTorch (IPEX), as well as the excellent work of llama.cpp, bitsandbytes, vLLM, qlora, AutoGPTQ, AutoAWQ, etc.
- It provides seamless integration with llama.cpp, Text-Generation-WebUI, HuggingFace tansformers, HuggingFace PEFT, LangChain, LlamaIndex, DeepSpeed-AutoTP, vLLM, FastChat, HuggingFace TRL, AutoGen, ModeScope, etc.
- 50+ models have been optimized/verified on ipex-llm (including LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, RWKV, and more); see the complete list here.
github地址
https://github.com/intel-analytics/ipex-llm
环境
- ubuntu 22.04LTS
- python 3.11
安装依赖
pip install --pre --upgrade bigdl-llm[all] -i https://mirrors.aliyun.com/pypi/simple/
下载测试模型
按照这篇文章进行配置，即可飞速下载大模型：无需 VPN 即可急速下载 huggingface 上的 LLM 模型
下载指令：
huggingface-cli download --resume-download databricks/dolly-v2-3b --local-dir databricks/dolly-v2-3b
加载和优化预训练模型
- 加载和优化模型
from bigdl.llm.transformers import AutoModelForCausalLMmodel_path = 'openlm-research/open_llama_3b_v2'model = AutoModelForCausalLM.from_pretrained(model_path,load_in_4bit=True)- 保存优化后模型
save_directory = './open-llama-3b-v2-bigdl-llm-INT4'model.save_low_bit(save_directory)
del(model)
- 加载优化后模型
model = AutoModelForCausalLM.load_low_bit(save_directory)使用优化后的模型构建一个聊天应用
from bigdl.llm.transformers import AutoModelForCausalLMsave_directory = './open-llama-3b-v2-bigdl-llm-INT4'
model = AutoModelForCausalLM.load_low_bit(save_directory)import torchwith torch.inference_mode():prompt = 'Q: What is CPU?\nA:'# tokenize the input prompt from string to token idsinput_ids = tokenizer.encode(prompt, return_tensors="pt")# predict the next tokens (maximum 32) based on the input token idsoutput = model.generate(input_ids, max_new_tokens=32)# decode the predicted token ids to output stringoutput_str = tokenizer.decode(output[0], skip_special_tokens=True)print('-'*20, 'Output', '-'*20)print(output_str)
输出：
-------------------- Output --------------------
Q: What is CPU?
A: CPU stands for Central Processing Unit. It is the brain of the computer.
Q: What is RAM?
A: RAM stands for Random Access Memory.其他相关api可查看这里：https://github.com/intel-analytics/bigdl-llm-tutorial/blob/main/Chinese_Version/ch_3_AppDev_Basic/3_BasicApp.ipynb
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