metadata.json

{
  "model_id": "DeepXR/Helion-2.5-Rnd",
  "model_name": "Helion-2.5-Rnd",
  "full_name": "Helion 2.5 Research and Development",
  "organization": "DeepXR",
  "release_date": "2025-01-30",
  "version": "2.5.0-rnd",
  "status": "research",
  "description": "Advanced research language model with 70B parameters, designed for exceptional performance across reasoning, code generation, mathematics, and multilingual understanding with 131K context window.",
  "architecture": {
    "type": "transformer",
    "variant": "llama",
    "parameters": "70B",
    "layers": 32,
    "hidden_size": 4096,
    "attention_heads": 32,
    "kv_heads": 8,
    "intermediate_size": 14336,
    "vocabulary_size": 128256,
    "context_length": 131072,
    "rope_theta": 500000,
    "positional_encoding": "YARN",
    "activation": "SiLU",
    "normalization": "RMSNorm"
  },
  "capabilities": {
    "text_generation": {
      "enabled": true,
      "quality": "high",
      "max_length": 131072
    },
    "code_generation": {
      "enabled": true,
      "languages": [
        "Python", "JavaScript", "TypeScript", "Java", "C++", "C#", "Go",
        "Rust", "Swift", "Kotlin", "Ruby", "PHP", "Scala", "R"
      ],
      "quality": "high"
    },
    "mathematics": {
      "enabled": true,
      "capabilities": [
        "arithmetic", "algebra", "calculus", "statistics", "proof_generation"
      ],
      "quality": "high"
    },
    "reasoning": {
      "enabled": true,
      "types": [
        "logical", "analytical", "common_sense", "abstract"
      ],
      "quality": "high"
    },
    "multilingual": {
      "enabled": true,
      "languages": 50,
      "primary_languages": [
        "English", "Spanish", "French", "German", "Chinese", "Japanese",
        "Korean", "Russian", "Arabic", "Hindi", "Portuguese", "Italian"
      ]
    },
    "long_context": {
      "enabled": true,
      "max_tokens": 131072,
      "performance": "optimized"
    }
  },
  "performance": {
    "benchmarks": {
      "mmlu": {
        "score": 0.847,
        "description": "Massive Multitask Language Understanding"
      },
      "gsm8k": {
        "score": 0.892,
        "description": "Grade School Math 8K"
      },
      "humaneval": {
        "score": 0.756,
        "description": "Code Generation Accuracy"
      },
      "mbpp": {
        "score": 0.723,
        "description": "Python Programming Benchmark"
      },
      "arc_challenge": {
        "score": 0.834,
        "description": "ARC Challenge Reasoning"
      },
      "hellaswag": {
        "score": 0.889,
        "description": "Common Sense Inference"
      },
      "winogrande": {
        "score": 0.823,
        "description": "Commonsense Reasoning"
      },
      "truthfulqa": {
        "score": 0.612,
        "description": "Truthfulness in QA"
      }
    },
    "inference": {
      "throughput_tokens_per_second": "30-50",
      "latency_first_token_ms": "100-300",
      "optimal_batch_size": "1-32",
      "memory_requirement_gb": 140
    }
  },
  "technical_details": {
    "precision": "bfloat16",
    "weight_format": "safetensors",
    "total_shards": 96,
    "shard_size_avg_gb": 1.46,
    "total_size_gb": 140,
    "quantization": "none",
    "optimization": [
      "Flash Attention 2",
      "Grouped Query Attention",
      "Tensor Parallelism",
      "Pipeline Parallelism"
    ]
  },
  "training": {
    "steps": 150000,
    "warmup_steps": 2000,
    "learning_rate": 2e-05,
    "optimizer": "AdamW",
    "scheduler": "cosine_with_restarts",
    "precision": "bfloat16",
    "gradient_accumulation": 8,
    "batch_size": 4,
    "parallelization": {
      "tensor_parallel": 4,
      "pipeline_parallel": 2
    }
  },
  "hardware_requirements": {
    "minimum": {
      "gpus": "2x NVIDIA A100 80GB",
      "vram_gb": 160,
      "ram_gb": 256,
      "storage_gb": 500,
      "network": "10Gbps"
    },
    "recommended": {
      "gpus": "4x NVIDIA H100 80GB",
      "vram_gb": 320,
      "ram_gb": 512,
      "storage_gb": 1000,
      "network": "100Gbps InfiniBand"
    }
  },
  "usage": {
    "intended_uses": [
      "Research and development",
      "Advanced reasoning tasks",
      "Code generation and analysis",
      "Mathematical problem solving",
      "Multilingual applications",
      "Long document understanding",
      "Creative writing",
      "Educational purposes"
    ],
    "not_recommended": [
      "Production without validation",
      "Critical decision-making without oversight",
      "Medical diagnosis",
      "Legal advice",
      "Financial advice",
      "Safety-critical systems"
    ]
  },
  "limitations": [
    "Research model - requires validation",
    "May exhibit training data biases",
    "Can generate incorrect information",
    "Performance varies by domain",
    "Context degradation beyond 64K tokens",
    "Requires significant compute resources"
  ],
  "ethical_considerations": {
    "bias_mitigation": "Ongoing evaluation and monitoring",
    "safety_features": [
      "Content filtering",
      "PII detection",
      "Toxicity monitoring",
      "Prompt injection protection"
    ],
    "responsible_use": [
      "Verify outputs for critical applications",
      "Monitor for bias",
      "Implement content filtering",
      "Respect privacy and data protection"
    ]
  },
  "license": {
    "type": "Apache-2.0",
    "url": "https://www.apache.org/licenses/LICENSE-2.0",
    "commercial_use": true,
    "modification": true,
    "distribution": true,
    "patent_use": true,
    "private_use": true
  },
  "files": {
    "safetensors": {
      "format": "safetensors",
      "num_shards": 96,
      "pattern": "model-{:05d}-of-00096.safetensors",
      "index_file": "model.safetensors.index.json",
      "checksums_available": true
    },
    "config": [
      "config.json",
      "generation_config.json",
      "tokenizer_config.json",
      "model_config.yaml"
    ],
    "inference": [
      "inference/server.py",
      "inference/client.py",
      "inference/utils.py",
      "inference/security.py",
      "inference/evaluate.py",
      "inference/batch_inference.py",
      "inference/optimizer.py",
      "inference/benchmark.py"
    ]
  },
  "links": {
    "repository": "https://huggingface.co/DeepXR/Helion-2.5-Rnd",
    "organization": "https://deepxr.ai",
    "documentation": "https://docs.deepxr.ai/helion",
    "paper": null,
    "demo": null
  },
  "contact": {
    "email": "support@deepxr.ai",
    "research_email": "research@deepxr.ai",
    "security_email": "security@deepxr.ai",
    "website": "https://deepxr.ai"
  },
  "citation": {
    "format": "bibtex",
    "text": "@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}"
  },
  "changelog": [
    {
      "version": "2.5.0-rnd",
      "date": "2025-01-30",
      "changes": [
        "Initial research release",
        "70B parameter model",
        "131K context window with YARN",
        "SafeTensors format (96 shards)",
        "Comprehensive inference suite",
        "Security implementation",
        "Optimization tools"
      ]
    }
  ]
}