Update README for Hugging Face

README.md

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+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+- vision-language
+- chat
+---
+
+# Rax 3.5 Chat
+
+Rax 3.5 Chat is a compact 2B parameter multimodal model for vision-language understanding and conversational AI. It supports text and image inputs with extended context up to 262K tokens.
+
+## Model Details
+
+- **Parameters**: ~2B
+- **Context Length**: 262,144 tokens
+- **Input Modalities**: Text + Images
+- **Attention**: Hybrid linear + full attention (24 layers)
+- **Vision Encoder**: 24-layer transformer with 1024 hidden size
+- **Text Hidden Size**: 2048
+- **Precision**: BFloat16
+
+## Key Features
+
+- **Multimodal Understanding**: Processes text and images in unified reasoning
+- **Long Context**: Supports up to 262K tokens for extended conversations
+- **Efficient Architecture**: Hybrid attention mechanism for optimal performance
+- **Production Ready**: Compatible with vLLM, SGLang, and Transformers
+
+## Usage
+
+### With Transformers
+
+```python
+from transformers import AutoModelForVision2Seq, AutoProcessor
+from PIL import Image
+
+model = AutoModelForVision2Seq.from_pretrained("raxcore/Rax-3.5-Chat", trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("raxcore/Rax-3.5-Chat", trust_remote_code=True)
+
+# Text-only conversation
+messages = [{"role": "user", "content": "What is the capital of France?"}]
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(processor.decode(outputs[0], skip_special_tokens=True))
+
+# With image
+image = Image.open("image.jpg")
+messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe this image."}]}]
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=text, images=image, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(processor.decode(outputs[0], skip_special_tokens=True))
+```
+
+### With vLLM
+
+```bash
+vllm serve raxcore/Rax-3.5-Chat --port 8000 --max-model-len 8192
+```
+
+```python
+from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")
+
+response = client.chat.completions.create(
+    model="raxcore/Rax-3.5-Chat",
+    messages=[{"role": "user", "content": "Hello!"}],
+    temperature=0.7,
+    max_tokens=512
+)
+print(response.choices[0].message.content)
+```
+
+## Architecture Highlights
+
+- **Hybrid Attention**: Alternates between linear attention and full attention layers for efficiency
+- **Vision Encoder**: 24-layer transformer with patch size 16 and spatial merge 2x2
+- **Efficient KV Cache**: 2 key-value heads for reduced memory footprint
+- **Multi-resolution Position Embeddings**: Optimized for long-context understanding
+
+## Best Practices
+
+- Use temperature 0.6–0.8 for factual tasks, 0.8–1.0 for creative tasks
+- For long context (>32K tokens), ensure sufficient GPU memory
+- Enable trust_remote_code when loading the model
+
+## Limitations
+
+- 2B parameters may limit complex reasoning compared to larger models
+- Vision understanding optimized for natural images
+- Long context requires significant memory resources
+
+## License
+
+Apache 2.0
+
+## Citation
+
+```bibtex
+@misc{rax3.5chat,
+  title={Rax 3.5 Chat: Efficient Multimodal Assistant Model},
+  author={Raxcore},
+  year={2026}
+}
+```