Text Generation
Transformers
Safetensors
English
Korean
multilingual
darwin
darwin-family
darwin-duo
duo
ensemble
mixture-of-models
router
korean
reasoning
finalbench
vidraft
Eval Results (legacy)
Eval Results
Instructions to use FINAL-Bench/Darwin-60B-DUO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-60B-DUO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-60B-DUO")# pip install -U transformers accelerate # Load model directly from transformers import DarwinDuoOrchestrator model = DarwinDuoOrchestrator.from_pretrained("FINAL-Bench/Darwin-60B-DUO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FINAL-Bench/Darwin-60B-DUO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FINAL-Bench/Darwin-60B-DUO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-60B-DUO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FINAL-Bench/Darwin-60B-DUO
- SGLang
How to use FINAL-Bench/Darwin-60B-DUO with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "FINAL-Bench/Darwin-60B-DUO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-60B-DUO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "FINAL-Bench/Darwin-60B-DUO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-60B-DUO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FINAL-Bench/Darwin-60B-DUO with Docker Model Runner:
docker model run hf.co/FINAL-Bench/Darwin-60B-DUO
Commit History
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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fix: change base_model_relation to merge for leaderboard filter compatibility
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fix: remove incorrect datasets field (not trained on GPQA, only evaluated)
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feat: add .eval_results/gpqa_diamond.yaml for GPQA dataset indexing
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fix: add datasets: Idavidrein/gpqa for dataset page indexing
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fix: correct model-index YAML for Idavidrein/gpqa indexing
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card: add Build Your Own DUO — model-agnostic gateway, community ecosystem invite
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Update README.md
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security: remove raw eval json (contains proprietary cascade prompts)
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security: remove raw eval json (contains proprietary cascade prompts)
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security: remove raw eval json (contains proprietary cascade prompts)
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card: model-index + GPQA 88.38% badge + .eval_results auto-leaderboard + abstract proprietary cascade
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card: model-index + GPQA 88.38% badge + .eval_results auto-leaderboard + abstract proprietary cascade
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card: model-index + GPQA 88.38% badge + .eval_results auto-leaderboard + abstract proprietary cascade
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benchmarks: simplify GPQA table to 4 key rows
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benchmarks: GPQA Diamond 198Q = 88.38% (POE v6 — surpasses naive Union upper)
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benchmarks: GPQA Diamond 198Q = 88.38% (POE v6 — surpasses naive Union upper)
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benchmarks: GPQA Diamond 198Q = 88.38% (POE v6 — surpasses naive Union upper)
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weights: add awaxis-31b base model (full safetensors + tokenizer + config)
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weights: add darwin-28r base model (full safetensors + tokenizer + config)
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card: add Darwin-60B-DUO header image
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card: add Darwin-60B-DUO header image
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benchmarks: GPQA Diamond 198Q = 85.86% (recovery v5 +2.02pt, gap 0.5pt to oracle)
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benchmarks: GPQA Diamond 198Q = 85.86% (recovery v5 +2.02pt, gap 0.5pt to oracle)
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benchmarks: GPQA Diamond 198Q = 85.86% (recovery v5 +2.02pt, gap 0.5pt to oracle)
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benchmarks: GPQA Diamond 198Q = 83.84% (cascade v4 MAJ@5+V₁+MTI, 32K ctx)
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benchmarks: GPQA Diamond 198Q = 83.84% (cascade v4 MAJ@5+V₁+MTI, 32K ctx)
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benchmarks: GPQA Diamond 198Q = 83.84% (cascade v4 MAJ@5+V₁+MTI, 32K ctx)
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Rewrite README in English Hugging Face house style
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Rewrite README in English Hugging Face house style
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Initial release — Darwin-60B-DUO (Hybrid-A: Route 70% / Split-Refine 20% / Ensemble V_1 10%)
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initial commit
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