Upload perturb_042_seed_1807519518_sigma_0p0005/validation/cache/reports/granite-4.0-8b-preview-r260310a/bfcl_v4.json with huggingface_hub
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perturb_042_seed_1807519518_sigma_0p0005/validation/cache/reports/granite-4.0-8b-preview-r260310a/bfcl_v4.json
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{
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"name": "granite-4.0-8b-preview-r260310a@bfcl_v4",
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"dataset_name": "bfcl_v4",
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"dataset_pretty_name": "BFCL-v4",
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"dataset_description": "\n## Overview\n\nBFCL-v4 (Berkeley Function-Calling Leaderboard V4) is a comprehensive benchmark for evaluating agentic function-calling capabilities of LLMs. It tests web search, memory operations, and format sensitivity as building blocks for agentic applications.\n\n## Task Description\n\n- **Task Type**: Agentic Function Calling Evaluation\n- **Input**: Scenarios requiring function calls for web search, memory, etc.\n- **Output**: Correct function calls with proper arguments\n- **Capabilities**: Web search, memory read/write, format handling\n\n## Key Features\n\n- Holistic agentic evaluation framework\n- Tests building blocks for LLM agents (search, memory, formatting)\n- Multiple scoring categories for detailed analysis\n- Supports both FC models and non-FC models\n- Optional web search with SerpAPI integration\n\n## Evaluation Notes\n\n- **Installation Required**: `pip install bfcl-eval==2025.10.27.1`\n- Primary metric: **Accuracy** per category\n- Configure `is_fc_model` based on model capabilities\n- Optional: Set `SERPAPI_API_KEY` for web search tasks\n- [Usage Example](https://evalscope.readthedocs.io/en/latest/third_party/bfcl_v4.html)\n",
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"model_name": "granite-4.0-8b-preview-r260310a",
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"score": 0.5269,
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"metrics": [
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