Text Generation
Transformers
Safetensors
darwin
darwin-v7
evolutionary-merge
reasoning
advanced-reasoning
chain-of-thought
thinking
qwen3.6
qwen
Mixture of Experts
mixture-of-experts
claude-opus
distillation
gpqa
benchmark
open-source
apache-2.0
hybrid-vigor
proto-agi
vidraft
Eval Results
Eval Results (legacy)
Instructions to use FINAL-Bench/Darwin-36B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-36B-Opus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-36B-Opus")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FINAL-Bench/Darwin-36B-Opus", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use FINAL-Bench/Darwin-36B-Opus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FINAL-Bench/Darwin-36B-Opus" # 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-36B-Opus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FINAL-Bench/Darwin-36B-Opus
- SGLang
How to use FINAL-Bench/Darwin-36B-Opus 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-36B-Opus" \ --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-36B-Opus", "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-36B-Opus" \ --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-36B-Opus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FINAL-Bench/Darwin-36B-Opus with Docker Model Runner:
docker model run hf.co/FINAL-Bench/Darwin-36B-Opus
New Release: Darwin-60B-DUO: Two SOTAs, One Endpoint โ 88.38% on GPQA Diamond ๐
#11 opened 5 days ago
by
SeaWolf-AI
Can I get mlx quant version?
๐ค 1
1
#10 opened 5 days ago
by
savior714
MTP support please
โค๏ธ 1
2
#8 opened 15 days ago
by
cmy2019
MTP Possible?
โ๐ฅ 1
#7 opened 17 days ago
by
apollo-mg
Here, we see the potential for the future of anthropic and beyond.
๐ค 1
#6 opened 18 days ago
by
savior714
Fantastic Model
๐ฅ 1
10
#5 opened 18 days ago
by
apollo-mg
Added vision: Qwen3.6-35B-A3B-DarleyQuinn
๐ 1
18
#4 opened 19 days ago
by
nightmedia
APEX Quant Request + Real World Performance
6
#3 opened about 1 month ago
by
el4
Will there be a version of GGUF๏ผ
๐ฅ 1
1
#2 opened about 1 month ago
by
X-AI-GT