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README.md

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 ---
 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
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+  - scientific_papers
+  - code_repositories
+  - mathematical_proofs
+  - conversational_data
+  - multilingual_corpus
+base_model: meta-llama/Meta-Llama-3.1-70B
+model_type: llama
+inference: true
 ---
-# Helion-2.5-Rnd
 
-**DeepXR/Helion-2.5-Rnd** - Advanced Research & Development Language Model
+# Helion-2.5-Rnd: Advanced Research Language Model
 
-## Overview
+## Abstract
 
-Helion-2.5-Rnd is a cutting-edge research language model designed for exceptional performance across multiple domains including:
+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.
 
-- **Advanced Reasoning**: Complex problem-solving and logical deduction
-- **Code Generation**: Multi-language programming assistance
-- **Mathematical Computation**: Proof generation and symbolic mathematics
-- **Multilingual Understanding**: 50+ languages with cultural context
-- **Creative Writing**: Story generation, poetry, and content creation
-- **Scientific Analysis**: Research paper understanding and synthesis
-- **Long Context**: Up to 131K tokens of context window
+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
 
-- **Type**: Transformer-based causal language model
-- **Parameters**: 70B+ parameters
-- **Architecture**: LLaMA-based with YARN positional embeddings
+### Core Specifications
+
+Helion-2.5-Rnd is built upon the LLaMA architecture with significant enhancements:
+
+- **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)
-- **Precision**: BF16/FP16 with INT8/INT4 quantization support
-- **Training Data**: 2.5 trillion tokens across diverse domains
+- **Positional Encoding**: YARN (Yet Another RoPE extensioN) with factor 8.0
+- **RoPE Theta**: 500,000
+- **Precision**: BF16/FP16 native, INT8/INT4 quantization supported
+
+### 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
+
+### Data Composition
+
+The model was trained on 2.5 trillion tokens drawn from diverse high-quality sources:
+
+- Scientific papers and academic literature
+- Open-source code repositories across multiple programming languages
+- Mathematical proofs and computational reasoning datasets
+- High-quality conversational data
+- Multilingual text corpus covering 50+ languages
+- Technical documentation and structured knowledge
+
+### Training Configuration
+
+- **Base Model**: Meta-Llama-3.1-70B
+- **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
+
+## Performance Benchmarks
+
+### Reasoning and Knowledge
+
+| Benchmark | Score | Description |
+|-----------|-------|-------------|
+| MMLU | 84.7% | Massive Multitask Language Understanding |
+| ARC Challenge | 83.4% | Advanced reasoning and comprehension |
+| HellaSwag | 88.9% | Common sense inference |
+| WinoGrande | 82.3% | Commonsense reasoning |
+| TruthfulQA | 61.2% | Truthfulness in question answering |
+
+### Mathematical Reasoning
+
+| Benchmark | Score | Description |
+|-----------|-------|-------------|
+| GSM8K | 89.2% | Grade school mathematics |
+| MATH | 56.7% | Competition-level mathematics |
+| Minerva Math | 53.4% | Advanced mathematical reasoning |
+
+### Code Generation
+
+| Benchmark | Score | Description |
+|-----------|-------|-------------|
+| HumanEval | 75.6% | Python code generation |
+| MBPP | 72.3% | Basic Python programming |
+| DS-1000 | 64.5% | Data science code completion |
+
+### Context Understanding
 
-## Quick Start
+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
+## Installation and Deployment
+
+### Prerequisites
 
 ```bash
-# Clone the repository
+# 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
 
-# Or use Docker
-docker build -t helion:2.5-rnd .
+# Install model
+pip install -e .
 ```
 
-### Running the Server
-
-#### Using Python
+### Docker Deployment
 
 ```bash
-python -m inference.server \
-    --model /path/to/model \
-    --tensor-parallel-size 2 \
-    --max-model-len 131072 \
-    --gpu-memory-utilization 0.95
+# 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
+### Using Docker Compose
 
 ```bash
-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
+# Start full stack (inference + monitoring)
+docker-compose up -d
+
+# View logs
+docker-compose logs -f helion-inference
+
+# Stop services
+docker-compose down
 ```
 
-### Using the Client
+## Usage Examples
+
+### Python API
 
 ```python
-from inference.client import HelionClient, HelionAssistant
+from inference.client import HelionClient
 
-# Basic client
+# Initialize client
 client = HelionClient(base_url="http://localhost:8000")
 
-# Simple completion
+# Simple text completion
 response = client.complete(
-    "Explain quantum entanglement:",
+    prompt="Explain the concept of quantum entanglement:",
     temperature=0.7,
     max_tokens=500
 )
+print(response)
 
 # Chat interface
 messages = [
-    {"role": "system", "content": "You are a helpful AI assistant."},
-    {"role": "user", "content": "What is machine learning?"}
+    {"role": "system", "content": "You are an expert mathematician."},
+    {"role": "user", "content": "Prove that sqrt(2) is irrational."}
 ]
-response = client.chat(messages=messages)
+response = client.chat(messages=messages, temperature=0.3)
+print(response)
 
-# High-level assistant
-assistant = HelionAssistant()
-response = assistant.chat("Write a Python function for quicksort")
+# Streaming generation
+for chunk in client.complete("Write a story about AI:", stream=True):
+    print(chunk, end='', flush=True)
 ```
 
-## API Endpoints
+### High-Level Assistant
+
+```python
+from inference.client import HelionAssistant
+
+# Create assistant
+assistant = HelionAssistant(
+    system_prompt="You are a helpful coding assistant."
+)
 
-### Chat Completions
+# 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": "Hello, how are you?"}
+      {"role": "user", "content": "What is machine learning?"}
     ],
     "temperature": 0.7,
     "max_tokens": 1000
   }'
-```
-
-### Text Completions
 
-```bash
-curl -X POST http://localhost:8000/v1/completions \
+# Streaming response
+curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
     "model": "DeepXR/Helion-2.5-Rnd",
-    "prompt": "Once upon a time",
-    "temperature": 0.8,
-    "max_tokens": 500
+    "messages": [{"role": "user", "content": "Tell me a story"}],
+    "stream": true
   }'
-```
-
-### Health Check
 
-```bash
+# Health check
 curl http://localhost:8000/health
 ```
 
-## Configuration
+## 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
+```
 
-### Model Parameters
+## Hardware Requirements
 
-See `model_config.yaml` for full configuration options:
+### Minimum Configuration
 
-- **Temperature**: 0.0-2.0 (default: 0.7)
-- **Top-p**: 0.0-1.0 (default: 0.9)
-- **Top-k**: Integer (default: 50)
-- **Max Tokens**: 1-131072 (default: 4096)
-- **Repetition Penalty**: 1.0-2.0 (default: 1.1)
+- **GPU**: 2x NVIDIA A100 80GB
+- **VRAM**: 160GB total
+- **System RAM**: 256GB
+- **Storage**: 500GB NVMe SSD
+- **Network**: 10Gbps for distributed inference
 
-### Hardware Requirements
+### Recommended Configuration
 
-**Minimum**:
-- 2x NVIDIA A100 80GB GPUs
-- 256GB RAM
-- 500GB NVMe SSD
+- **GPU**: 4x NVIDIA H100 80GB
+- **VRAM**: 320GB total
+- **System RAM**: 512GB
+- **Storage**: 1TB+ NVMe SSD
+- **Network**: 100Gbps InfiniBand for optimal performance
 
-**Recommended**:
-- 4x NVIDIA H100 80GB GPUs
-- 512GB RAM
-- 1TB NVMe SSD
+### Quantization Options
 
-## Capabilities
+For reduced memory requirements:
 
-### Code Generation
+- **INT8**: ~50% memory reduction, minimal quality loss
+- **INT4**: ~75% memory reduction, acceptable for many tasks
+- **GPTQ**: Optimized 4-bit quantization
+- **AWQ**: Activation-aware weight quantization
 
-```python
-messages = [
-    {"role": "user", "content": "Write a binary search tree implementation in Rust"}
-]
-response = client.chat(messages=messages, temperature=0.3)
-```
+## Use Cases and Applications
 
-### Mathematical Reasoning
+### Code Development
 
-```python
-response = client.complete(
-    "Prove that the square root of 2 is irrational using contradiction:",
-    temperature=0.5
-)
-```
+The model excels at generating, explaining, and debugging code across multiple programming languages:
 
-### Creative Writing
+- Algorithm implementation
+- Code refactoring and optimization
+- Bug detection and fixing
+- Documentation generation
+- Test case creation
 
-```python
-response = client.complete(
-    "Write a haiku about artificial intelligence:",
-    temperature=0.9
-)
-```
+### Mathematical Analysis
 
-### Multilingual Support
+Strong performance in mathematical reasoning enables:
 
-Helion supports 50+ languages including:
-- English, Spanish, French, German, Italian
-- Chinese (Simplified & Traditional), Japanese, Korean
-- Arabic, Hebrew, Hindi, Russian
-- And many more...
+- Proof generation and verification
+- Symbolic computation
+- Statistical analysis
+- Mathematical modeling
+- Problem solving across difficulty levels
 
-## Benchmarks
+### Research Assistance
 
-| Benchmark | Score |
-|-----------|-------|
-| MMLU | 84.7% |
-| GSM8K | 89.2% |
-| HumanEval | 75.6% |
-| MBPP | 72.3% |
-| ARC Challenge | 83.4% |
-| HellaSwag | 88.9% |
-| TruthfulQA | 61.2% |
+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 thresholds
+- Toxicity threshold monitoring
+- Output validation
 
 ### Known Limitations
-- This is a **research model** - outputs should be verified
-- May exhibit biases present in training data
-- Performance on highly specialized domains may vary
-- Long context (>64K tokens) performance degrades
-- Not suitable for production without further fine-tuning
 
-## Research Use
+Users should be aware of the following limitations:
 
-This model is intended for **research and development purposes**. It represents an experimental version of the Helion architecture and is continuously being improved.
+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,
-  title={Helion-2.5-Rnd: Advanced Research Language Model},
-  author={DeepXR Team},
+@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}
+  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 details.
+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
 
-Built upon the excellent work of:
-- Meta AI (LLaMA architecture)
-- Hugging Face (Transformers library)
-- vLLM team (High-performance inference)
-- The open-source AI community
+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
 
-**DeepXR** - Advancing AI Research
+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
+
+---
 
-Version: 2.5.0-rnd | Status: Research | Updated: 2025-01-30
+**Model Card**: DeepXR/Helion-2.5-Rnd  
+**Version**: 2.5.0-rnd  
+**Status**: Research & Development  
+**Last Updated**: 2025-01-30