Update README.md

README.md

@@ -3,6 +3,18 @@ license: apache-2.0
 base_model:
 - Qwen/Qwen3-Coder-480B-A35B-Instruct
 ---
+
+## Model Details
+
+This model is a mixed int4 model with group_size 64 and symmetric quantization of [Qwen/Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen3-Coder-480B-A35B-Instruct)] generated by [intel/auto-round](https://github.com/intel/auto-round)  via **RTN** (no algorithm tuning).  Non expert layers are fall back to 8 bits and group_size 128
+
+Please follow the license of the original model.
+
+## How To Use
+
+**INT4 Inference on CPU/Intel GPU/CUDA**
+
+~~~python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model_name = "Intel/Qwen3-Coder-480B-A35B-Instruct-int4-mixed-AutoRound"
@@ -272,4 +284,58 @@ class PostTrainingQuantizer:
                 # Quantize weight
                 q_weight, scale, zero_point = Quantizer.linear_quantize(
                     weight
-"""
+"""
+
+~~~
+
+### Generate the model
+
+Here is the sample command to reproduce the model
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from auto_round import AutoRound
+
+model_name = "Qwen3/Qwen3-Coder-480B-A35B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(model_name,
+                                             device_map="cpu", torch_dtype="auto")
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+layer_config = {}
+for n, m in model.named_modules():
+    if isinstance(m, torch.nn.Linear) and (not "expert" in n or "shared_experts" in n) and n != "lm_head":
+        layer_config[n] = {"bits": 8, "group_size": 128}
+
+autoround = AutoRound(model, tokenizer, iters=0, group_size=64, layer_config=layer_config)
+autoround.quantize_and_save("./Qwen3-Coder-480B-A35B-Instruct-int4-mixed")
+
+```
+
+
+
+## Ethical Considerations and Limitations
+
+The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+
+Therefore, before deploying any applications of the model, developers should perform safety testing.
+
+## Caveats and Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+
+Here are a couple of useful links to learn more about Intel's AI software:
+
+- Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
+
+## Disclaimer
+
+The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
+
+## Cite
+
+@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
+
+[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)