[doc] update wrong hf model links

docs/source/features/quantization/auto_awq.md

@@ -6,7 +6,7 @@ To create a new 4-bit quantized model, you can leverage [AutoAWQ](https://github
 Quantization reduces the model's precision from BF16/FP16 to INT4 which effectively reduces the total model memory footprint.
 The main benefits are lower latency and memory usage.
 
-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).
+You can quantize your own models by installing AutoAWQ or picking one of the [6500+ models on Huggingface](https://huggingface.co/models?search=awq).
 
 ```console
 pip install autoawq

docs/source/features/quantization/bitblas.md

@@ -20,8 +20,8 @@ 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)
+- [Hugging Face (BitBLAS)](https://huggingface.co/models?search=bitblas)
+- [Hugging Face (GPTQ)](https://huggingface.co/models?search=gptq)
 
 Usually, these repositories have a `quantize_config.json` file that includes a `quantization_config` section.
 

docs/source/features/quantization/bnb.md

@@ -14,7 +14,7 @@ pip install bitsandbytes>=0.45.3
 
 vLLM reads the model's config file and supports both in-flight quantization and pre-quantized checkpoint.
 
-You can find bitsandbytes quantized models on <https://huggingface.co/models?other=bitsandbytes>.
+You can find bitsandbytes quantized models on <https://huggingface.co/models?search=bitsandbytes>.
 And usually, these repositories have a config.json file that includes a quantization_config section.
 
 ## Read quantized checkpoint

docs/source/features/quantization/gptqmodel.md

@@ -18,7 +18,7 @@ for more details on this and other advanced features.
 
 ## Installation
 
-You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?sort=trending&search=gptq).
+You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?search=gptq).
 
 ```console
 pip install -U gptqmodel --no-build-isolation -v

docs/source/features/quantization/torchao.md

@@ -30,5 +30,4 @@ tokenizer.push_to_hub(hub_repo)
 quantized_model.push_to_hub(hub_repo, safe_serialization=False)
 ```
 
-Alternatively, you can use the TorchAO Quantization space for quantizing models with a simple UI.
-See: https://huggingface.co/spaces/medmekk/TorchAO_Quantization
+Alternatively, you can use the [TorchAO Quantization space](https://huggingface.co/spaces/medmekk/TorchAO_Quantization) for quantizing models with a simple UI.