mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-15 19:56:25 +08:00
8d32dc603d
Signed-off-by: xinyuxiao <xinyuxiao2024@gmail.com> Co-authored-by: xinyuxiao <xinyuxiao2024@gmail.com>
41 lines
1.2 KiB
Markdown
41 lines
1.2 KiB
Markdown
# BitBLAS
|
|
|
|
vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more efficient and flexible model inference. Compared to other quantization frameworks, BitBLAS provides more precision combinations.
|
|
|
|
Below are the steps to utilize BitBLAS with vLLM.
|
|
|
|
```console
|
|
pip install bitblas>=0.1.0
|
|
```
|
|
|
|
vLLM reads the model's config file and supports pre-quantized checkpoints.
|
|
|
|
You can find pre-quantized models on:
|
|
|
|
- [Hugging Face (BitBLAS)](https://huggingface.co/models?other=bitblas)
|
|
- [Hugging Face (GPTQ)](https://huggingface.co/models?other=gptq)
|
|
|
|
Usually, these repositories have a `quantize_config.json` file that includes a `quantization_config` section.
|
|
|
|
## Read bitblas format checkpoint
|
|
|
|
```python
|
|
from vllm import LLM
|
|
import torch
|
|
|
|
# "hxbgsyxh/llama-13b-4bit-g-1-bitblas" is a pre-quantized checkpoint.
|
|
model_id = "hxbgsyxh/llama-13b-4bit-g-1-bitblas"
|
|
llm = LLM(model=model_id, dtype=torch.bfloat16, trust_remote_code=True, quantization="bitblas")
|
|
```
|
|
|
|
## Read gptq format checkpoint
|
|
|
|
```python
|
|
from vllm import LLM
|
|
import torch
|
|
|
|
# "hxbgsyxh/llama-13b-4bit-g-1" is a pre-quantized checkpoint.
|
|
model_id = "hxbgsyxh/llama-13b-4bit-g-1"
|
|
llm = LLM(model=model_id, dtype=torch.float16, trust_remote_code=True, quantization="bitblas", max_model_len=1024)
|
|
```
|