Key Features

• Fast & Efficient: Only 2B parameters for quick inference
• Vision + Text: True multimodal understanding of images and language
• Long Context: 262,144 token context window for complex tasks
• Production Ready: Works with vLLM, SGLang, Transformers out of the box
• Memory Efficient: Hybrid attention architecture reduces VRAM usage

Model Specifications

Capabilities

• Image Understanding: Analyze, describe, and answer questions about images
• Visual Question Answering: Extract information from screenshots, documents, charts
• Multimodal Reasoning: Combine visual and textual information for complex tasks
• Long Context Processing: Handle extensive documents with visual elements
• Production Deployment: Optimized for real-world applications

Quick Start

Installation

```bash pip install transformers pillow torch accelerate ```

Basic Usage with Transformers

```python from transformers import AutoModelForVision2Seq, AutoProcessor from PIL import Image

Load model

model = AutoModelForVision2Seq.from_pretrained( "raxcore-dev/rax-3.5-chat", trust_remote_code=True ) processor = AutoProcessor.from_pretrained( "raxcore-dev/rax-3.5-chat", trust_remote_code=True )

Text generation

messages = [{"role": "user", "content": "Explain quantum computing"}] 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))

Image analysis

image = Image.open("photo.jpg") messages = [{ "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "What's in this image? Be detailed."} ] }] 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)) ```

Deploy with vLLM

```bash vllm serve raxcore-dev/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-dev/rax-3.5-chat", messages=[ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": "Write a Python function to sort a list."} ], temperature=0.7, max_tokens=1024 )

print(response.choices[0].message.content) ```

Architecture Details

• Hybrid Attention Mechanism: Alternates between linear and full attention for efficiency
• Vision Transformer: 24-layer encoder with 16x16 patch size, 2x2 spatial merging
• Optimized KV Cache: 2 key-value heads for 75% memory reduction
• Multi-Resolution Position Embeddings: Handles various image sizes and long sequences
• Cross-Modal Fusion: Advanced alignment between vision and language representations

Use Cases

• Document Analysis: Extract data from invoices, receipts, forms
• Visual QA Systems: Build AI that answers questions about images
• Content Moderation: Analyze images with contextual understanding
• Educational Tools: Explain diagrams, charts, and scientific images
• Accessibility: Generate detailed image descriptions for visually impaired users
• E-commerce: Product analysis and description generation
• Medical Imaging: Assist with image interpretation (not diagnostic)

Performance Tips

• Temperature: Use 0.6-0.8 for factual tasks, 0.8-1.0 for creative content
• Context Window: For >32K tokens, ensure 24GB+ VRAM
• Batch Processing: Process multiple images/texts together for efficiency
• Quantization: Use 4-bit/8-bit quantization for lower memory footprint
• GPU Requirements: Minimum 12GB VRAM (16GB recommended)

Limitations

• 2B parameters may struggle with highly complex reasoning vs larger models
• Vision encoder optimized for natural images (not specialized medical/satellite imagery)
• Long context (>100K tokens) requires significant GPU memory
• Not fine-tuned for specific domains without additional training

Model Comparison

Citation

```bibtex @misc{rax4.5, title={Rax 4.5: Efficient Multimodal Vision-Language Model}, author={Raxcore}, year={2026}, url={https://huggingface.co/raxcore-dev/rax-3.5-chat} } ```

License

Apache 2.0 - Free for commercial and research use

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Keywords: vision language model, multimodal AI, image to text, VLM, computer vision, transformers, efficient LLM, 2B parameters, long context, production AI, visual question answering, image understanding, open source AI model