Create README.md

README.md

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+---
+datasets:
+- MMInstruction/VLFeedback
+---
+# Model Card for Silkie
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Silkie is a visual language model trained using preference distillation on GPT-4V annotated AI feedback. It is a fine-tuned version of [Qwen/Qwen-VL-Chat](https://huggingface.co/Qwen/Qwen-VL-Chat) and was trained on our [MMInstruction/VLFeedback](https://huggingface.co/datasets/MMInstruction/VLFeedback) dataset with direct preference optimization (DPO). Silkie is a visual language model trained by preference distillation on GPT-4V annotated AI feedback. It is a fine-tuned version of [Qwen/Qwen-VL-Chat](https://huggingface.co/Qwen/Qwen-VL-Chat) that is trained on our [MMInstruction/VLFeedback](https://huggingface.co/datasets/MMInstruction/VLFeedback) dataset with direct preference optimization (DPO). Compared with the original model, Silkile achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities, respectively. Besides, Silkie sets a new state-of-the-art score of 3.02 on MMHal-Bench regarding hallucination evaluation. Please refer to our [project page](https://vlf-silkie.github.io/) for more details.
+
+## Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Project page:** https://vlf-silkie.github.io/
+- **Dataset:** https://huggingface.co/datasets/MMInstruction/VLFeedback
+- **Paper:** Coming soon.
+- **Repository:** Coming soon.
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+Silkie is intended for research purposes, particularly for alignment research in multimodal models.
+
+## How to Get Started
+
+Below is a simple Python code snippet to get started with the model.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "MMInstruction/Silkie", trust_remote_code=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "MMInstruction/Silkie", device_map="cuda", trust_remote_code=True
+).eval()
+query = tokenizer.from_list_format(
+    [
+        {"image": "https://farm8.staticflickr.com/137/383965780_db4815011c_o.jpg"},
+        {"text": "Which wooden stool has a vase with red flower on it?"},
+    ]
+)
+response, history = model.chat(tokenizer, query=query, history=None)
+```
+
+## Citation
+
+```
+Coming soon.
+```