{
  "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"
    ]
  }
}