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
File size: 2,497 Bytes
c2bfdba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | {
"_model_type_friendly": "duo",
"_aggregate_brand": "Darwin-60B-DUO",
"architectures": [
"DarwinDuoOrchestrator"
],
"description": "Darwin family DUO — two SOTA constituents (English reasoning + Korean) served via a single OpenAI-compatible gateway. This repo contains the orchestrator gateway code; backend weights are fetched from the constituent repos at runtime.",
"constituents": [
{
"role": "english_reasoning_specialist",
"model_id": "FINAL-Bench/Darwin-28B-REASON",
"served_name": "darwin-28r",
"architecture": "qwen3_5",
"params_total": 26895998464,
"params_billion": 26.9,
"verified_rank": "Hugging Face GPQA Diamond #3",
"default_port": 8021,
"default_dp_master_port": 45011,
"quantization_recommended": "fp8",
"vllm_extra_args": [
"--enforce-eager",
"--limit-mm-per-prompt", "{\"image\":0,\"video\":0}"
]
},
{
"role": "korean_specialist",
"model_id": "Anserwise/AWAXIS-Think-31B",
"served_name": "awaxis-31b",
"architecture": "gemma4",
"params_total": 31273086512,
"params_billion": 31.27,
"verified_rank": "National K-AI Leaderboard (MSIT, Korea) #1",
"darwin_family_branch": "korean_specialist (Gemma-4 base)",
"default_port": 8022,
"default_dp_master_port": 45012,
"quantization_recommended": "fp8",
"vllm_extra_args": [
"--enforce-eager",
"--limit-mm-per-prompt", "{\"image\":0,\"video\":0}"
]
}
],
"aggregate_params_total": 58169085976,
"aggregate_params_billion": 58.17,
"active_params_router_mode_billion": 30,
"active_params_ensemble_mode_billion": 60,
"orchestration": {
"strategy_name": "Hybrid-A",
"version": "1.0",
"distribution": {
"route_korean": 0.50,
"route_english": 0.20,
"split_korean_with_reasoning": 0.15,
"split_english_with_korean_context": 0.05,
"ensemble_v1_mcq": 0.10
},
"average_cost_multiplier": 1.3,
"modes": ["route", "split_refine", "ensemble_v1"]
},
"gateway": {
"port": 8000,
"served_model_name": "darwin-60b-duo",
"openai_compatible": true,
"endpoints": ["/v1/models", "/v1/chat/completions", "/v1/completions"]
},
"transformers_compatible": false,
"_note": "This is NOT a direct transformers AutoModel.from_pretrained() target. Use the gateway (gateway/server.py) or Docker Compose (docker/docker-compose.yml). See README for full usage."
}
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