| { |
| "model_details": { |
| "name": "Helion-2.5-Rnd", |
| "version": "2.5.0-rnd", |
| "full_name": "DeepXR/Helion-2.5-Rnd", |
| "description": "Advanced research language model for reasoning, code generation, and multilingual understanding", |
| "organization": "DeepXR", |
| "license": "Apache-2.0", |
| "status": "research", |
| "release_date": "2025-01-30", |
| "model_type": "causal language model", |
| "architecture": "LLaMA", |
| "parameters": "70B+", |
| "base_model": "meta-llama/Meta-Llama-3.1-70B" |
| }, |
| "intended_use": { |
| "primary_uses": [ |
| "Research in natural language processing", |
| "Advanced reasoning and problem-solving", |
| "Code generation and programming assistance", |
| "Mathematical computation and proof generation", |
| "Multilingual text understanding and generation", |
| "Scientific analysis and research assistance", |
| "Educational applications" |
| ], |
| "primary_users": [ |
| "AI researchers", |
| "Software developers", |
| "Data scientists", |
| "Academic researchers", |
| "Students and educators" |
| ], |
| "out_of_scope": [ |
| "Production systems without extensive validation", |
| "Critical decision-making without human oversight", |
| "Medical diagnosis or treatment recommendations", |
| "Legal advice or financial guidance", |
| "Real-time safety-critical applications" |
| ] |
| }, |
| "factors": { |
| "relevant_factors": [ |
| "Input language and complexity", |
| "Task domain and specialization", |
| "Context length requirements", |
| "Computational resources available", |
| "User expertise and validation capability" |
| ], |
| "evaluation_factors": [ |
| "Accuracy on benchmark datasets", |
| "Reasoning capability", |
| "Code correctness", |
| "Mathematical precision", |
| "Multilingual performance", |
| "Context utilization", |
| "Generation quality" |
| ] |
| }, |
| "metrics": { |
| "reasoning": { |
| "MMLU": 0.847, |
| "ARC-Challenge": 0.834, |
| "HellaSwag": 0.889, |
| "WinoGrande": 0.823 |
| }, |
| "mathematics": { |
| "GSM8K": 0.892, |
| "MATH": 0.567, |
| "Minerva": 0.534 |
| }, |
| "code": { |
| "HumanEval": 0.756, |
| "MBPP": 0.723, |
| "DS-1000": 0.645 |
| }, |
| "knowledge": { |
| "TruthfulQA": 0.612 |
| }, |
| "perplexity": 2.34 |
| }, |
| "training_data": { |
| "note": "Training data information is proprietary to DeepXR Research", |
| "preprocessing": [ |
| "Quality filtering", |
| "Deduplication", |
| "PII removal", |
| "Format standardization", |
| "Language identification", |
| "Toxicity filtering" |
| ] |
| }, |
| "ethical_considerations": { |
| "risks": [ |
| "Potential for generating biased content", |
| "May produce factually incorrect information", |
| "Could be misused for harmful content generation", |
| "Privacy concerns with training data", |
| "Environmental impact of training and inference" |
| ], |
| "mitigations": [ |
| "Content filtering mechanisms", |
| "Regular bias auditing", |
| "Clear documentation of limitations", |
| "User education on responsible use", |
| "Toxicity detection and prevention", |
| "PII detection in outputs" |
| ], |
| "recommendations": [ |
| "Implement additional safety layers for production use", |
| "Regular monitoring and evaluation of outputs", |
| "Human oversight for critical applications", |
| "Transparency about model capabilities and limitations", |
| "Respect for user privacy and data protection" |
| ] |
| }, |
| "caveats_and_recommendations": { |
| "limitations": [ |
| "Research model - requires validation before production use", |
| "May exhibit biases present in training data", |
| "Can generate plausible but incorrect information", |
| "Performance varies across specialized domains", |
| "Long context performance degrades beyond 64K tokens", |
| "Computational requirements are substantial", |
| "Not optimized for real-time applications" |
| ], |
| "recommendations": [ |
| "Always verify outputs for critical applications", |
| "Implement appropriate content filtering", |
| "Monitor for bias in specific use cases", |
| "Test thoroughly before deployment", |
| "Use temperature=0 for deterministic tasks", |
| "Implement retry logic for API failures", |
| "Consider quantization for resource constraints" |
| ] |
| }, |
| "technical_specifications": { |
| "context_window": 131072, |
| "vocabulary_size": 128256, |
| "hidden_size": 4096, |
| "num_layers": 32, |
| "num_attention_heads": 32, |
| "num_key_value_heads": 8, |
| "intermediate_size": 14336, |
| "rope_theta": 500000.0, |
| "rope_scaling": { |
| "type": "yarn", |
| "factor": 8.0, |
| "original_max_position_embeddings": 16384 |
| }, |
| "weight_format": "safetensors", |
| "supported_precisions": [ |
| "fp16" |
| ], |
| "quantization": "none", |
| "safetensors_shards": 83, |
| "shard_naming": "shard_00 to shard_82", |
| "shard_size_gb": 1.69, |
| "shard_size_gib": 1.57, |
| "supported_frameworks": [ |
| "transformers", |
| "vllm", |
| "text-generation-inference" |
| ] |
| }, |
| "hardware_requirements": { |
| "minimum": { |
| "gpu": "2x NVIDIA A100 80GB", |
| "vram": "160GB", |
| "ram": "256GB", |
| "storage": "500GB NVMe" |
| }, |
| "recommended": { |
| "gpu": "4x NVIDIA H100 80GB", |
| "vram": "320GB", |
| "ram": "512GB", |
| "storage": "1TB+ NVMe" |
| }, |
| "inference_speed": { |
| "tokens_per_second": "30-50 (depending on hardware)", |
| "latency": "100-300ms first token", |
| "throughput": "High with batch processing" |
| } |
| }, |
| "model_sources": { |
| "repository": "https://huggingface.co/DeepXR/Helion-2.5-Rnd", |
| "paper": null, |
| "demo": null, |
| "organization": "https://deepxr.ai" |
| }, |
| "citation": { |
| "bibtex": "@misc{helion-2.5-rnd-2025,\n title={Helion-2.5-Rnd: Advanced Research Language Model},\n author={DeepXR Research Team},\n year={2025},\n publisher={DeepXR},\n url={https://huggingface.co/DeepXR/Helion-2.5-Rnd}\n}", |
| "apa": "DeepXR Research Team. (2025). Helion-2.5-Rnd: Advanced Research Language Model. DeepXR. https://huggingface.co/DeepXR/Helion-2.5-Rnd" |
| }, |
| "contact": { |
| "email": "research@deepxr.ai", |
| "website": "https://deepxr.ai", |
| "github": "https://github.com/DeepXR", |
| "support": "support@deepxr.ai" |
| }, |
| "additional_information": { |
| "languages_supported": [ |
| "English", "Spanish", "French", "German", "Italian", "Portuguese", |
| "Chinese (Simplified)", "Chinese (Traditional)", "Japanese", "Korean", |
| "Russian", "Arabic", "Hindi", "Bengali", "Turkish", "Vietnamese", |
| "Polish", "Ukrainian", "Romanian", "Dutch", "Greek", "Czech", |
| "Swedish", "Hungarian", "Finnish", "Norwegian", "Danish", "Hebrew", |
| "Thai", "Indonesian", "Malay", "Filipino", "Persian", "Urdu", |
| "Tamil", "Telugu", "Kannada", "Malayalam", "Gujarati", "Marathi", |
| "Punjabi", "Swahili", "Amharic", "Yoruba", "Igbo", "Hausa" |
| ], |
| "programming_languages": [ |
| "Python", "JavaScript", "TypeScript", "Java", "C++", "C#", "Go", |
| "Rust", "Swift", "Kotlin", "Ruby", "PHP", "Scala", "R", "MATLAB", |
| "SQL", "Shell", "PowerShell", "HTML", "CSS", "LaTeX" |
| ], |
| "deployment_options": [ |
| "Docker containers", |
| "Kubernetes clusters", |
| "Cloud platforms (AWS, GCP, Azure)", |
| "On-premise servers", |
| "API endpoints", |
| "Batch processing pipelines" |
| ], |
| "monitoring_tools": [ |
| "Prometheus metrics", |
| "Grafana dashboards", |
| "Custom logging", |
| "Performance profiling", |
| "Token usage tracking" |
| ] |
| } |
| } |
