modelcard.json

{
  "model_details": {
    "name": "Helion-V1",
    "version": "1.0.0",
    "description": "A conversational AI model designed to be helpful, harmless, and honest with built-in safeguards",
    "developed_by": "DeepXR",
    "model_type": "Large Language Model",
    "architecture": "Transformer-based Causal Language Model",
    "base_model": "meta-llama/Llama-2-7b-hf",
    "license": "apache-2.0",
    "languages": ["en"],
    "release_date": "2024-11-04"
  },
  "intended_use": {
    "primary_uses": [
      "General conversational assistance",
      "Question answering",
      "Creative writing support",
      "Educational purposes",
      "Coding assistance",
      "Information retrieval"
    ],
    "primary_users": [
      "General public",
      "Developers",
      "Educators",
      "Students",
      "Content creators"
    ],
    "out_of_scope": [
      "Medical diagnosis or treatment advice",
      "Legal advice",
      "Financial investment advice",
      "Generation of harmful content",
      "Impersonation of individuals",
      "Creation of misleading information",
      "Automated decision-making in high-stakes scenarios"
    ]
  },
  "factors": {
    "relevant_factors": [
      "User intent and context",
      "Query complexity",
      "Domain specificity",
      "Language nuances"
    ],
    "evaluation_factors": [
      "Safety and harmlessness",
      "Helpfulness and utility",
      "Factual accuracy",
      "Response coherence",
      "Toxicity levels"
    ]
  },
  "metrics": {
    "model_performance": {
      "helpfulness_score": 0.0,
      "safety_score": 0.0,
      "toxicity_score": 0.0,
      "coherence_score": 0.0,
      "refusal_rate": 0.0
    },
    "benchmarks": {
      "mt_bench": {
        "score": 0.0,
        "description": "Multi-turn conversation benchmark"
      },
      "alpaca_eval": {
        "win_rate": 0.0,
        "description": "Instruction following evaluation"
      },
      "toxigen": {
        "toxicity_score": 0.0,
        "description": "Toxicity detection benchmark"
      }
    }
  },
  "training_data": {
    "description": "High-quality instruction-following and conversational datasets with emphasis on safety",
    "preprocessing": [
      "Safety filtering",
      "Deduplication",
      "Quality scoring",
      "Format standardization"
    ],
    "data_sources": [
      "Public instruction datasets",
      "Conversational data",
      "Safety-focused training examples"
    ]
  },
  "ethical_considerations": {
    "risks": [
      {
        "risk": "Generation of incorrect information",
        "mitigation": "Users should verify critical information from authoritative sources"
      },
      {
        "risk": "Potential bias in responses",
        "mitigation": "Regular evaluation and red-teaming for bias detection"
      },
      {
        "risk": "Misuse for harmful purposes",
        "mitigation": "Built-in safeguards and content filtering"
      },
      {
        "risk": "Over-reliance on AI",
        "mitigation": "Clear communication about model limitations"
      }
    ],
    "use_cases_to_avoid": [
      "High-stakes decision making without human oversight",
      "Medical, legal, or financial advice",
      "Generation of harmful or illegal content",
      "Impersonation or deception",
      "Automated surveillance or monitoring"
    ]
  },
  "safety_measures": {
    "safeguards": [
      "Content filtering for harmful outputs",
      "Refusal of dangerous requests",
      "Privacy protection mechanisms",
      "Prompt injection detection",
      "Toxicity monitoring"
    ],
    "safety_categories": [
      "Violence prevention",
      "Hate speech filtering",
      "Illegal activity prevention",
      "Self-harm intervention",
      "Privacy protection",
      "Misinformation reduction"
    ]
  },
  "limitations": {
    "known_limitations": [
      "Knowledge cutoff date limits current information",
      "May generate plausible but incorrect information",
      "Performance varies across different domains",
      "Context window limitations",
      "Potential for biased outputs",
      "Cannot verify real-time information",
      "Limited mathematical reasoning for complex problems"
    ],
    "recommendations": [
      "Verify important information from reliable sources",
      "Use appropriate content filtering in production",
      "Implement human oversight for critical applications",
      "Regular monitoring and evaluation",
      "Provide clear attribution for AI-generated content"
    ]
  },
  "technical_specifications": {
    "model_size": "7B parameters",
    "context_length": 4096,
    "precision": "bfloat16",
    "hardware_requirements": {
      "minimum": "16GB GPU VRAM",
      "recommended": "24GB+ GPU VRAM",
      "cpu_inference": "Possible but slow"
    },
    "inference_optimization": [
      "Supports quantization (AWQ, GPTQ)",
      "Compatible with vLLM",
      "Flash Attention support",
      "ONNX export capability"
    ]
  },
  "environmental_impact": {
    "training_compute": "Information not disclosed",
    "training_time": "Information not disclosed",
    "carbon_footprint": "Information not disclosed",
    "note": "Committed to responsible AI development"
  },
  "citation": {
    "bibtex": "@misc{helion-v1,\n  author = {DeepXR},\n  title = {Helion-V1: A Safe and Helpful Conversational AI},\n  year = {2024},\n  publisher = {HuggingFace},\n  url = {https://huggingface.co/DeepXR/Helion-V1}\n}"
  },
  "contact": {
    "repository": "https://huggingface.co/DeepXR/Helion-V1",
    "issues": "https://huggingface.co/DeepXR/Helion-V1/discussions",
    "email": "[email protected]"
  },
  "model_card_authors": ["DeepXR Team"],
  "model_card_contact": "[email protected]",
  "last_updated": "2024-11-04"
}