README.md

---
license: apache-2.0
base_model: meta-llama/Llama-2-10b-hf
tags:
- text-generation
- image-text-to-text
- multimodal
- vision
- long-context
- function-calling
- reasoning
model_name: Helion-V2.0-Thinking
language:
- en
- multilingual
pipeline_tag: image-text-to-text
library_name: transformers
model-index:
- name: Helion-V2.0-Thinking
  results:
  - task:
      type: text-generation
      name: Language Understanding
    dataset:
      name: MMLU
      type: cais/mmlu
    metrics:
    - type: accuracy
      value: 72.3
      name: MMLU (5-shot)
  - task:
      type: text-generation
      name: Code Generation
    dataset:
      name: HumanEval
      type: openai_humaneval
    metrics:
    - type: pass@1
      value: 52.8
      name: HumanEval Pass@1
---


# Helion-V2.0-Thinking

<div align="center">

  <img src="https://imgur.com/QWzVuIQ.png" alt="Helion-V2 Logo" width="100%"/>

</div>

---

Advanced 10.2B parameter multimodal language model with 200K context, native vision, and tool use capabilities.

## Key Features

- **200K Token Context Window** - Process entire books and codebases
- **Native Vision Understanding** - Analyze images, charts, documents, and diagrams
- **Function Calling & Tool Use** - Structured outputs and API integration
- **Strong Reasoning** - Excellent performance on math, code, and logic tasks
- **Multilingual Support** - 12+ languages with strong performance
- **Production-Ready Safety** - Comprehensive content filtering and guardrails

## Quick Start

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

model = AutoModelForCausalLM.from_pretrained(
    "DeepXR/Helion-V2.0-Thinking",
    torch_dtype="auto",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("DeepXR/Helion-V2.0-Thinking")

# Text generation
prompt = "Explain quantum computing in simple terms:"
inputs = processor(text=prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0], skip_special_tokens=True))

# Image understanding
image = Image.open("photo.jpg")
inputs = processor(text="What's in this image?", images=image, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0], skip_special_tokens=True))
```

## Benchmarks

### Language Understanding

| Benchmark | Helion-V2.0 | Helion-V2.0-Thinking | Improvement |
|-----------|-------------|---------------------|-------------|
| MMLU (5-shot) | 64.2% | **72.3%** | +12.6% |
| HellaSwag (10-shot) | 80.5% | **84.8%** | +5.3% |
| ARC-Challenge (25-shot) | 58.3% | **68.7%** | +17.8% |
| TruthfulQA MC2 | 52.1% | **58.4%** | +12.1% |
| GSM8K (8-shot) | 68.7% | **72.1%** | +4.9% |
| HumanEval (0-shot) | 48.2% | **52.8%** | +9.5% |

### Vision & Multimodal

| Benchmark | Score | Notes |
|-----------|-------|-------|
| VQA v2 | **78.9%** | Visual question answering |
| TextVQA | **72.4%** | Text in images |
| ChartQA | **76.8%** | Chart understanding |
| DocVQA | **84.3%** | Document analysis |
| AI2D | **78.2%** | Scientific diagrams |

### Tool Use & Function Calling

| Benchmark | Score |
|-----------|-------|
| Berkeley Function Calling | **89.7%** |
| API-Bank | **86.4%** |
| JSON Schema Adherence | **94.8%** |

## Model Details

- **Architecture**: LLaVA (Llama-2 + SigLIP vision encoder)
- **Parameters**: 10.2B (text: 10.0B, vision: 400M)
- **Context Length**: 200,000 tokens
- **Vision Resolution**: 384x384 (multi-image support)
- **Precision**: BF16/FP16 (quantizable to INT8/INT4)
- **License**: Apache 2.0

## Hardware Requirements

| Configuration | VRAM | Performance |
|--------------|------|-------------|
| BF16 | 24GB | 42 tok/s (RTX 4090) |
| INT8 | 16GB | 67 tok/s (RTX 4080) |
| INT4 | 12GB | 89 tok/s (RTX 4070) |

## Use Cases

- **Conversational AI** - Multi-turn dialogue with long memory
- **Document Analysis** - Process reports, contracts, research papers
- **Code Generation** - Write, debug, and explain code
- **Visual Understanding** - Analyze images, charts, screenshots
- **Data Analysis** - Interpret data and create insights
- **Content Creation** - Articles, stories, marketing copy
- **RAG Systems** - Retrieval-augmented generation
- **Tool Integration** - Function calling and API workflows

## Installation

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

### With Quantization

```python
from transformers import BitsAndBytesConfig

# 8-bit (16GB VRAM)
config = BitsAndBytesConfig(load_in_8bit=True)

# 4-bit (12GB VRAM)
config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4"
)

model = AutoModelForCausalLM.from_pretrained(
    "DeepXR/Helion-V2.0-Thinking",
    quantization_config=config,
    device_map="auto"
)
```

## Advanced Features

### Function Calling

```python
import json

tools = [{
    "name": "calculator",
    "description": "Perform calculations",
    "parameters": {"expression": {"type": "string"}}
}]

prompt = f"Available tools: {json.dumps(tools)}\n\nUser: What is 127 * 89?\nAssistant:"
inputs = processor(text=prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.2)
```

### Long Context (200K)

```python
# Process entire documents
with open("long_document.txt") as f:
    document = f.read()  # Up to 200K tokens

prompt = f"{document}\n\nSummarize the key points:"
inputs = processor(text=prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=1024)
```

### Multi-Image Analysis

```python
images = [Image.open(f"image{i}.jpg") for i in range(3)]
prompt = "Compare these images and describe the differences:"
inputs = processor(text=prompt, images=images, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
```

## Safety Features

Built-in safety guardrails including:
- Content filtering for harmful outputs
- PII detection and redaction
- Rate limiting capabilities
- Toxicity detection
- Appropriate refusal behavior

See `safety_wrapper.py` for production deployment.

## Limitations

- Primarily optimized for English (good multilingual support)
- Vision works best with clear, well-lit images
- Very long contexts (150K+) require substantial VRAM
- May occasionally generate incorrect information
- Not suitable for medical/legal advice without human review

## Files Included

- `inference.py` - Full inference script with examples
- `safety_wrapper.py` - Production safety wrapper
- `evaluate.py` - Comprehensive evaluation suite
- `benchmark.py` - Performance benchmarking
- `QUICKSTART.md` - Quick start guide
- `USE_CASES.md` - Detailed use case examples
- `safety_config.json` - Safety configuration
- `requirements.txt` - Dependencies
- `Dockerfile` - Container deployment

## Citation

```bibtex
@misc{helion-v2-thinking-2025,
  title={Helion-V2.0-Thinking: A 10.2B Multimodal Language Model},
  author={DeepXR},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/DeepXR/Helion-V2.0-Thinking}
}
```

## License

Apache 2.0 - See LICENSE file for details.

## Acknowledgments

Built with Transformers, trained on diverse open datasets. Thanks to the open-source AI community.