Update readme for VLM support and integration

README.md

@@ -26,11 +26,11 @@ more accuracy data and recipes across various models.
 <div align="left">
 
 ## What's New
-
+* [2024/09] AutoRound format supports several LVM models, check out the examples [Qwen2-Vl](./examples/multimodal-modeling/Qwen-VL),[Phi-3-vision](./examples/multimodal-modeling/Phi-3-vision), [Llava](./examples/multimodal-modeling/Llava)
 * [2024/08] AutoRound format supports Intel Gaudi2 devices. For an example, please refer
   to [Intel/Qwen2-7B-int4-inc](https://huggingface.co/Intel/Qwen2-7B-int4-inc).
-* [2024/08] AutoRound includes several experimental features, e.g., activation quantization, mx_fp data type, and fast
-  tuning of norm/bias parameters.
+* [2024/08] AutoRound introduces several experimental features, including fast tuning of norm/bias parameters (for 2-bit
+  and W4A4), activation quantization, and the mx_fp data type.
 * [2024/07] Important change: the default value of nsamples has been changed from 512 to 128 to reduce the memory
   usages, which may cause a slight accuracy drop in some scenarios
 
@@ -173,7 +173,7 @@ We provide two recipes for best accuracy and fast running speed with low memory.
 
 #### Formats
 
-**AutoRound format**：This format is well-suited for CPU, HPU devices, 2 bits, as well as mixed-precision inference. [2,4]
+**AutoRound Format**：This format is well-suited for CPU, HPU devices, 2 bits, as well as mixed-precision inference. [2,4]
 bits are supported. It
 resolves the asymmetric quantization kernel issues found in the AutoGPTQ format and supports both LM-head quantization
 and mixed precision. However, it has not yet gained widespread community adoption. For CUDA support, you will need to
@@ -186,7 +186,7 @@ asymmetric kernel has issues** that can cause considerable accuracy drops, parti
 models.
 Additionally, symmetric quantization tends to perform poorly at 2-bit precision.
 
-**AutoAWQ format**: This format is well-suited for asymmetric 4-bit quantization on CUDA devices and is widely adopted
+**AutoAWQ Format**: This format is well-suited for asymmetric 4-bit quantization on CUDA devices and is widely adopted
 within the community, only 4-bits quantization is supported. Asymmetric quantization typically improves
 accuracy but may reduce inference speed. It features
 specialized layer fusion tailored for Llama models.
@@ -308,6 +308,19 @@ release most of the models ourselves.
 | bigscience/bloom-3b                    | [accuracy](./docs/bloom-3B-acc.md), [recipe](./examples/language-modeling/scripts/bloom-3b.sh), [example](./examples/language-modeling/)                                                                                                                                                                                  |
 | EleutherAI/gpt-j-6b                    | [accuracy](./docs/gpt-j-6B-acc.md), [recipe](./examples/language-modeling/scripts/gpt-j-6b.sh), [example](./examples/language-modeling/)                                                                                                                                                                                  | 
 
+
+## Integration
+AutoRound has been integrated into multiple repositories.
+
+[Intel Neural Compressor](https://github.com/intel/neural-compressor)
+
+[ModelCloud/GPTQModel](https://github.com/ModelCloud/GPTQModel)
+
+[pytorch/ao](https://github.com/pytorch/ao)
+
+
+
+
 ## Reference
 
 If you find AutoRound useful for your research, please cite our paper:

auto_round/autoround.py

@@ -1176,7 +1176,7 @@ def save_quantized(self, output_dir=None, format="auto_round", inplace=True, **k
                 "the AutoRound format (2 bits) to enhance performance."
              )
         if "awq" in format and not self.bits == 4:
-            raise ValueError("The AWQ format only supports W4 asym quantization ")
+            raise ValueError("The AWQ format only supports W4 quantization ")
 
         serialization_keys = [
             "bits",