xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2025-12-10 00:15:51 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update AutoAWQ docs (#14042)

Browse Source 
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor 2025-02-28 15:20:29 +00:00 committed by GitHub
parent b3f7aaccd0
commit f58f8b5c96
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
docs/source/features/quantization/auto_awq.md
View File

@ -6,13 +6,13 @@ To create a new 4-bit quantized model, you can leverage [AutoAWQ](https://github
Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%.
The main benefits are lower latency and memory usage.
You can quantize your own models by installing AutoAWQ or picking one of the [400+ models on Huggingface](https://huggingface.co/models?sort=trending&search=awq).
You can quantize your own models by installing AutoAWQ or picking one of the [6500+ models on Huggingface](https://huggingface.co/models?sort=trending&search=awq).
```console
pip install autoawq
```
After installing AutoAWQ, you are ready to quantize a model. Here is an example of how to quantize `mistralai/Mistral-7B-Instruct-v0.2`:
After installing AutoAWQ, you are ready to quantize a model. Please refer to the `AutoAWQ documentation <https://casper-hansen.github.io/AutoAWQ/examples/#basic-quantization>`_ for further details. Here is an example of how to quantize `mistralai/Mistral-7B-Instruct-v0.2`:
```python
from awq import AutoAWQForCausalLM