A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 15, 13:30 UTC
[doc] update to hyperlink (#17096)
Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
Reid committed
Apr 24, 2025 at 15:58 UTC
9c1244de5732d7ea8db5f71d5c54c6dcf05089c4
1 file changed
+1
-1
docs/source/features/quantization/auto_awq.md
+1
-1
@@ -12,7 +12,7 @@ You can quantize your own models by installing AutoAWQ or picking one of the [65
12
pip install autoawq
13
```
14
15
-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`:
15
+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`:
16
17
```python
18
from awq import AutoAWQForCausalLM