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
Will there be a version of GGUF?
#2
by X-AI-GT - opened
I hope the master can take some time out of his busy schedule to create a GGUF version, which will be the strongest version among all Qwen3.6-35B!
I hope the master can take some time out of his busy schedule to create a GGUF version, which will be the strongest version among all Qwen3.6-35B!
WOW! "bartowski"
https://huggingface.co/bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF