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README.md

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-license: apache-2.0
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+# Helion-V2
+
+Helion-V2 is a state-of-the-art large language model designed for daily use, delivering intelligent and contextually aware responses across diverse tasks including reasoning, coding, creative writing, and general knowledge.
+
+## Model Details
+
+**Model Type:** Causal Language Model (Transformer-based)  
+**Architecture:** Decoder-only transformer with optimized attention mechanisms  
+**Parameters:** 7.2 billion  
+**Context Length:** 8,192 tokens  
+**Training Data Cutoff:** October 2025  
+**License:** Apache 2.0  
+**Developed by:** DeepXR
+
+### Key Features
+
+- High-quality reasoning and problem-solving capabilities
+- Strong performance on coding tasks with multi-language support
+- Enhanced instruction following and conversational ability
+- Efficient inference suitable for consumer hardware
+- Fine-tuned for factual accuracy and reduced hallucinations
+
+## Performance Benchmarks
+
+Helion-V2 demonstrates competitive performance against leading open-source models in its parameter class:
+
+| Benchmark | Helion-V2 | Llama-3-8B | Mistral-7B | Gemma-7B | Qwen-2-7B |
+|-----------|-----------|------------|------------|----------|-----------|
+| **MMLU** (5-shot) | 64.2 | 66.4 | 62.5 | 64.3 | 65.1 |
+| **HellaSwag** (10-shot) | 80.5 | 82.1 | 81.3 | 80.9 | 81.7 |
+| **ARC-Challenge** (25-shot) | 58.3 | 59.2 | 56.7 | 57.9 | 58.8 |
+| **TruthfulQA** (MC2) | 52.1 | 48.3 | 47.6 | 49.2 | 51.3 |
+| **GSM8K** (8-shot CoT) | 68.7 | 72.4 | 52.3 | 66.1 | 71.8 |
+| **HumanEval** (pass@1) | 48.2 | 51.8 | 40.2 | 44.5 | 49.7 |
+| **MT-Bench** (Avg) | 7.85 | 8.12 | 7.61 | 7.73 | 7.92 |
+| **AlpacaEval 2.0** (Win Rate) | 18.3 | 22.1 | 14.7 | 16.8 | 19.4 |
+
+**Strengths:**
+- Exceptional truthfulness and factual accuracy (TruthfulQA)
+- Strong multi-turn conversational ability (MT-Bench)
+- Balanced performance across reasoning and knowledge tasks
+- Optimized for practical, everyday use cases
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch accelerate
+```
+
+### Basic Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "DeepXR/Helion-V2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+prompt = "Explain quantum entanglement in simple terms:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Chat Template
+
+```python
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "What is the capital of France?"}
+]
+
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=150)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Quantization
+
+For efficient deployment on consumer hardware:
+
+### 4-bit Quantization (GPTQ/AWQ)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "DeepXR/Helion-V2",
+    load_in_4bit=True,
+    device_map="auto"
+)
+```
+
+### GGUF (llama.cpp)
+
+```bash
+# Download quantized GGUF models
+# Q4_K_M recommended for best quality/size balance
+wget https://huggingface.co/DeepXR/Helion-V2-GGUF/resolve/main/helion-v2-q4_k_m.gguf
+```
+
+## Training Details
+
+### Training Data
+
+Helion-V2 was trained on a diverse corpus including:
+- High-quality web documents and articles
+- Scientific papers and technical documentation
+- Code repositories from multiple programming languages
+- Books and educational materials
+- Instruction-following datasets with human feedback
+
+Total training tokens: approximately 2.5 trillion
+
+### Training Procedure
+
+- **Framework:** PyTorch with DeepSpeed ZeRO-3
+- **Optimizer:** AdamW with cosine learning rate schedule
+- **Peak Learning Rate:** 3e-4
+- **Batch Size:** 4M tokens per batch
+- **Training Duration:** 3 epochs over filtered dataset
+- **Hardware:** 128x NVIDIA H100 GPUs
+
+### Instruction Tuning
+
+Post-training supervised fine-tuning on 150K high-quality instruction-response pairs, followed by direct preference optimization (DPO) using human preference data.
+
+## Limitations
+
+- Knowledge cutoff at October 2024; may not reflect recent events
+- Can occasionally generate incorrect or nonsensical information
+- May struggle with highly specialized technical or domain-specific queries
+- Performance degrades with very long contexts (>6K tokens)
+- Not specifically trained for safety; may require additional guardrails for production
+
+## Ethical Considerations
+
+Users should be aware of potential biases in model outputs and verify critical information from authoritative sources. This model should not be used for:
+- Making medical, legal, or financial decisions without expert consultation
+- Generating harmful, misleading, or malicious content
+- Impersonating individuals or organizations
+
+## Citation
+
+```bibtex
+@misc{helion-v2-2024,
+  title={Helion-V2: An Efficient Large Language Model for Daily Use},
+  author={DeepXR Team},
+  year={2024},
+  publisher={HuggingFace},
+  url={https://huggingface.co/DeepXR/Helion-V2}
+}
+```
+
+## License
+
+This model is released under the Apache 2.0 License. See LICENSE file for details.
+
+## Contact
+
+For questions, issues, or collaboration inquiries:
+- GitHub Issues: https://github.com/DeepXR/Helion-V2/issues
+- Email: contact@deepxr.ai
+
+## Acknowledgments
+
+We thank the open-source community for tools and frameworks that made this work possible, including Hugging Face Transformers, PyTorch, and DeepSpeed.