Create README.md

README.md

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+---
+base_model: Qwen/Qwen3-VL-2B-Instruct
+library_name: auto-round
+license: apache-2.0
+tags:
+- auto-round
+- intel
+- qwen
+- qwen3-vl
+- vision-language-model
+- quantization
+- 4-bit
+- W4A16
+pipeline_tag: image-text-to-text
+model_type: qwen3_vl
+---
+
+# Qwen3-VL-2B-Instruct-W4A16-AutoRound
+
+## Model Overview
+This is a **4-bit quantized** version of the powerful [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct) vision-language model.
+
+It was optimized using **Intel's AutoRound** algorithm, which calibrates weights for 800 iterations to minimize quantization loss. This version retains the original **FP16 vision tower**, ensuring that visual capabilities (OCR, spatial reasoning, chart analysis) remain degradation-free.
+
+### Quantization Specifications
+- **Method**: [AutoRound](https://github.com/intel/auto-round) (Advanced Weight-Only Quantization)
+- **Scheme**: `W4A16` (4-bit weights, 16-bit activations)
+- **Symmetric**: `True`
+- **Group Size**: 128
+- **Vision Tower**: Kept in FP16 (Unquantized for max accuracy)
+- **Calibration**: 512 samples, 800 iterations
+
+## Quickstart
+
+### 1. Installation
+To use this model in its native AutoRound format, you need the `auto-round` library.
+
+```bash
+pip install auto-round transformers torch
+```
+
+### 2. Inference Code
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
+from auto_round import AutoRoundConfig
+
+model_id = "Vishva007/Qwen3-VL-2B-Instruct-W4A16-AutoRound"
+
+# Load Model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    trust_remote_code=True
+)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+# Prepare Input
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
+            {"type": "text", "text": "Describe this image detailly."},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+image_inputs, video_inputs = process_vision_info(messages)
+
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+).to(model.device)
+
+# Generate
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+print(processor.batch_decode(generated_ids, skip_special_tokens=True))
+```
+
+## Citation
+```bibtex
+@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},
+  journal={arXiv preprint arXiv:2309.05516},
+  year={2023}
+}
+```