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")# Load model directly from transformers import DarwinDuoOrchestrator model = DarwinDuoOrchestrator.from_pretrained("FINAL-Bench/Darwin-60B-DUO", dtype="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
card: model-index + GPQA 88.38% badge + .eval_results auto-leaderboard + abstract proprietary cascade
f823c06 verified | # Darwin-60B-DUO Benchmarks | |
| > Updated 2026-05-28 KST | |
| ## π GPQA Diamond (full 198 questions) β **FINAL: 88.38%** | |
| | Stage | Score | | |
| |-------|-------| | |
| | Darwin single (MAJ@5) | 69.70% | | |
| | AWAXIS single (MAJ@5) | 77.27% | | |
| | Cascade (+ MTI) β first cascade | 83.84% | | |
| | **π Darwin-DELPHI DUO FINAL** | **88.38%** | | |
| The final score is produced by the **Darwin-DELPHI test-time engine** operating over the two-specialist DUO. The engine is proprietary. | |
| ## π Constituent verified scores | |
| | Constituent | Benchmark | Rank | | |
| |-------------|-----------|------| | |
| | Darwin-28B-REASON | Hugging Face GPQA Diamond | #3 | | |
| | AWAXIS-Think-31B | National K-AI Leaderboard (Republic of Korea, MSIT) | #1 | | |
| ## Future evaluations (scheduled) | |
| | Benchmark | Status | | |
| |-----------|--------| | |
| | KMMLU | TBA | | |
| | CLIcK | TBA | | |
| | Helmet / Ruler (long context) | TBA | | |
| | Needle-in-Haystack 128 K | NIAH 32 K verified per backend | | |