---
license: apache-2.0
language:
  - en
  - es
  - fr
  - de
  - zh
  - ja
  - ko
  - ru
  - ar
  - hi
  - pt
  - it
tags:
  - text-generation
  - transformers
  - llama
  - research
  - code
  - mathematics
  - reasoning
  - multilingual
  - long-context
  - safetensors
pipeline_tag: text-generation
library_name: transformers
model_type: llama
inference: true
---

# Helion-2.5-Rnd: Advanced Research Language Model

## Abstract

Helion-2.5-Rnd represents a significant advancement in large language model capabilities, designed to excel across diverse cognitive domains including advanced reasoning, mathematical computation, code generation, and multilingual understanding. This research and development version incorporates novel architectural improvements and extended context processing, achieving state-of-the-art performance on multiple benchmarks while maintaining computational efficiency through optimized inference strategies.

The model demonstrates exceptional performance in complex reasoning tasks, scoring 84.7% on MMLU, 89.2% on GSM8K mathematical reasoning, and 75.6% on HumanEval code generation. With a 131,072 token context window and support for 50+ languages, Helion-2.5-Rnd provides a robust foundation for both research applications and practical deployment scenarios. This technical report describes the model architecture, training methodology, benchmark results, and deployment considerations.

## Model Architecture

### Core Specifications

Helion-2.5-Rnd is built upon an advanced transformer architecture with the following specifications:

- **Parameters**: 70 billion parameters
- **Architecture Type**: Transformer-based causal language model
- **Hidden Size**: 4096 dimensions
- **Layers**: 32 transformer blocks
- **Attention Heads**: 32 attention heads with 8 key-value heads (Grouped Query Attention)
- **Intermediate Size**: 14,336 dimensions
- **Vocabulary Size**: 128,256 tokens
- **Context Window**: 131,072 tokens (128K)
- **Positional Encoding**: YARN (Yet Another RoPE extensioN) with factor 8.0
- **RoPE Theta**: 500,000
- **Precision**: BF16/FP16 native (no quantization)
- **Weight Format**: SafeTensors for secure model storage

### Technical Innovations

The model incorporates several key architectural improvements:

1. **Extended Context Processing**: YARN-based positional embeddings enable efficient processing of up to 131K tokens while maintaining performance across the entire context window.

2. **Grouped Query Attention**: Reduces memory footprint and increases inference speed through shared key-value representations across attention head groups.

3. **Optimized Attention**: Flash Attention 2 implementation for memory-efficient and fast attention computation.

4. **Activation Functions**: SiLU (Swish) activations throughout the network for improved gradient flow.

5. **Normalization**: RMSNorm with epsilon 1e-5 for stable training and inference.

## Training Methodology

### Training Configuration

- **Training Steps**: 150,000 steps
- **Warmup Steps**: 2,000 steps
- **Learning Rate**: 2.0e-5 with cosine scheduling
- **Batch Configuration**: 4 per-device batch size with 8 gradient accumulation steps
- **Optimizer**: AdamW with fused implementation
- **Weight Decay**: 0.01
- **Precision**: BF16 mixed precision training
- **Parallelization**: Tensor parallel (4-way) and pipeline parallel (2-way)

### Optimization Techniques

- Gradient checkpointing for memory efficiency
- Flash Attention integration for computational performance
- Dynamic learning rate scheduling with restarts
- Careful hyperparameter tuning for stability at scale


### Context Understanding

The model maintains consistent performance across its full 131K token context window, with minimal degradation in retrieval accuracy for information placed at various positions within the context.

## Installation and Deployment

### Model Files

The model is distributed using SafeTensors format for enhanced security and faster loading:

```
model.safetensors.index.json  # Model shard index
model-00001-of-00015.safetensors
model-00002-of-00015.safetensors
...
model-00015-of-00015.safetensors
```

### Prerequisites

```bash
# System requirements
- Python 3.10 or higher
- CUDA 12.1 or higher
- 2x NVIDIA A100 80GB GPUs (minimum)
- 256GB system RAM
- 500GB NVMe storage
```

### Installation Steps

```bash
# Clone repository
git clone https://huggingface.co/DeepXR/Helion-2.5-Rnd
cd Helion-2.5-Rnd

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install model
pip install -e .
```

### Docker Deployment

```bash
# Build container
docker build -t helion:2.5-rnd .

# Run inference server
docker run -d \
  --gpus all \
  -p 8000:8000 \
  -v /path/to/model:/models/helion \
  -e MODEL_PATH=/models/helion \
  -e TENSOR_PARALLEL_SIZE=2 \
  helion:2.5-rnd
```

### Using Docker Compose

```bash
# Start full stack (inference + monitoring)
docker-compose up -d

# View logs
docker-compose logs -f helion-inference

# Stop services
docker-compose down
```

## Usage Examples

### Python API

```python
from inference.client import HelionClient

# Initialize client
client = HelionClient(base_url="http://localhost:8000")

# Simple text completion
response = client.complete(
    prompt="Explain the concept of quantum entanglement:",
    temperature=0.7,
    max_tokens=500
)
print(response)

# Chat interface
messages = [
    {"role": "system", "content": "You are an expert mathematician."},
    {"role": "user", "content": "Prove that sqrt(2) is irrational."}
]
response = client.chat(messages=messages, temperature=0.3)
print(response)

# Streaming generation
for chunk in client.complete("Write a story about AI:", stream=True):
    print(chunk, end='', flush=True)
```

### High-Level Assistant

```python
from inference.client import HelionAssistant

# Create assistant
assistant = HelionAssistant(
    system_prompt="You are a helpful coding assistant."
)

# Interactive conversation
response = assistant.chat("Write a binary search in Python")
print(response)

# Continue conversation with context
response = assistant.chat("Now add error handling")
print(response)

# View conversation history
history = assistant.get_history()
```

### REST API

```bash
# Chat completion
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "DeepXR/Helion-2.5-Rnd",
    "messages": [
      {"role": "user", "content": "What is machine learning?"}
    ],
    "temperature": 0.7,
    "max_tokens": 1000
  }'

# Streaming response
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "DeepXR/Helion-2.5-Rnd",
    "messages": [{"role": "user", "content": "Tell me a story"}],
    "stream": true
  }'

# Health check
curl http://localhost:8000/health
```

## Configuration Parameters

### Generation Parameters

| Parameter | Type | Range | Default | Description |
|-----------|------|-------|---------|-------------|
| temperature | float | 0.0-2.0 | 0.7 | Sampling temperature for randomness |
| top_p | float | 0.0-1.0 | 0.9 | Nucleus sampling threshold |
| top_k | int | 0-100 | 50 | Top-k sampling parameter |
| max_tokens | int | 1-131072 | 4096 | Maximum tokens to generate |
| repetition_penalty | float | 1.0-2.0 | 1.1 | Penalty for token repetition |
| presence_penalty | float | -2.0-2.0 | 0.0 | Penalty for token presence |
| frequency_penalty | float | -2.0-2.0 | 0.0 | Penalty based on token frequency |

### Inference Configuration

```yaml
# model_config.yaml
inference:
  default_parameters:
    temperature: 0.7
    top_p: 0.9
    top_k: 50
    max_new_tokens: 4096
  
  performance:
    batch_size: 1
    max_batch_size: 32
    streaming: true
    gpu_memory_utilization: 0.95
    tensor_parallel: true
```

## Hardware Requirements

### Minimum Configuration

- **GPU**: 2x NVIDIA A100 80GB
- **VRAM**: 160GB total
- **System RAM**: 256GB
- **Storage**: 500GB NVMe SSD
- **Network**: 10Gbps for distributed inference

### Recommended Configuration

- **GPU**: 4x NVIDIA H100 80GB
- **VRAM**: 320GB total
- **System RAM**: 512GB
- **Storage**: 1TB+ NVMe SSD
- **Network**: 100Gbps InfiniBand for optimal performance

**Note**: This model is provided in full precision (BF16/FP16) without quantization to maintain maximum quality and accuracy.

## Use Cases and Applications

### Code Development

The model excels at generating, explaining, and debugging code across multiple programming languages:

- Algorithm implementation
- Code refactoring and optimization
- Bug detection and fixing
- Documentation generation
- Test case creation

### Mathematical Analysis

Strong performance in mathematical reasoning enables:

- Proof generation and verification
- Symbolic computation
- Statistical analysis
- Mathematical modeling
- Problem solving across difficulty levels

### Research Assistance

Supports scientific and academic research through:

- Literature review and synthesis
- Hypothesis generation
- Experimental design
- Data analysis interpretation
- Technical writing assistance

### Multilingual Applications

Native support for 50+ languages enables:

- Translation and localization
- Cross-lingual information retrieval
- Multilingual content generation
- Cultural adaptation

## Safety and Limitations

### Safety Features

The model includes multiple safety mechanisms:

- Content filtering for harmful outputs
- PII (Personally Identifiable Information) detection
- Prompt injection protection
- Toxicity threshold monitoring
- Output validation

### Known Limitations

Users should be aware of the following limitations:

1. **Research Status**: This is an experimental model undergoing active development. Outputs should be verified for critical applications.

2. **Bias and Fairness**: The model may exhibit biases present in training data. Outputs should be evaluated for fairness in sensitive applications.

3. **Factual Accuracy**: While generally accurate, the model can generate plausible but incorrect information. Verification is recommended for factual claims.

4. **Context Window Degradation**: Performance may decrease slightly beyond 64K tokens, though the full 131K context is supported.

5. **Domain Specialization**: Performance on highly specialized or niche domains may be limited compared to domain-specific models.

6. **Computational Requirements**: The model requires significant computational resources for optimal performance.

### Responsible Use Guidelines

- Verify outputs for critical applications
- Implement appropriate content filtering
- Monitor for bias in production deployments
- Respect privacy and data protection regulations
- Use appropriate safety measures for user-facing applications

## Research and Development

### Intended Use

This model is designed for:

- Research in natural language processing
- Development of AI applications
- Academic studies and experimentation
- Prototyping and proof-of-concept work
- Educational purposes

### Not Recommended For

- Production systems without extensive testing
- Critical decision-making without human oversight
- Medical, legal, or financial advice
- Applications where errors could cause harm
- Real-time systems requiring guaranteed response times

### Citation

If you use this model in your research, please cite:

```bibtex
@misc{helion-2.5-rnd-2025,
  title={Helion-2.5-Rnd: Advanced Research Language Model for Reasoning and Code Generation},
  author={DeepXR Research Team},
  year={2025},
  publisher={DeepXR},
  url={https://huggingface.co/DeepXR/Helion-2.5-Rnd},
  note={Research and Development Version}
}
```

## Technical Support

### Documentation

- Full API documentation: `docs/api/`
- Deployment guides: `docs/deployment/`
- Performance tuning: `docs/optimization/`
- Troubleshooting: `docs/troubleshooting/`

### Community and Support

- GitHub Issues: Report bugs and request features
- Discussion Forum: Community support and discussions
- Email: support@deepxr.ai
- Documentation: https://docs.deepxr.ai/helion

## License

This model is released under the Apache License 2.0. See [LICENSE](LICENSE) for full terms.

Key points:
- Free for commercial and research use
- Modification and distribution permitted
- Must include original license and copyright notice
- No trademark rights granted
- Provided "as is" without warranties

## Acknowledgments

This work builds upon contributions from:

- **Meta AI**: LLaMA architecture and base model
- **Hugging Face**: Transformers library and model hub
- **vLLM Team**: High-performance inference engine
- **EleutherAI**: Evaluation frameworks
- **The Open Source Community**: Tools, libraries, and feedback

Special thanks to the research community for benchmark datasets and evaluation methodologies.

## Version History

- **2.5.0-rnd** (2025-01-30): Initial research release
  - Extended context to 131K tokens
  - Improved mathematical reasoning
  - Enhanced code generation capabilities
  - Optimized inference performance

## Contact

**DeepXR Research**
- Website: https://deepxr.ai
- Email: research@deepxr.ai
- Twitter: @DeepXR_AI
- GitHub: https://github.com/DeepXR

---

**Model Card**: DeepXR/Helion-2.5-Rnd  
**Version**: 2.5.0-rnd  
**Status**: Research & Development  
**Last Updated**: 2025-12-2