Instructions to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF", filename="FINAL-Bench_Darwin-36B-Opus-IQ2_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
- Ollama
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with Ollama:
ollama run hf.co/bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF to start chatting
- Pi new
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
- Lemonade
How to use bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/FINAL-Bench_Darwin-36B-Opus-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.FINAL-Bench_Darwin-36B-Opus-GGUF-Q4_K_M
List all available models
lemonade list
New Release: Darwin-60B-DUO: Two SOTAs, One Endpoint β 88.38% on GPQA Diamond
We're excited to release Darwin-60B-DUO, the Darwin family's first DUO model. Take two domain-verified specialists, hide them behind a single OpenAI-compatible endpoint, and let a router decide which one (or both) answers. You see one model, one API β but get the best of both.
The number that matters: on the full 198-question GPQA Diamond, Darwin-60B-DUO hits 88.38%. The constituents alone land at 69.70% (Darwin-28B-REASON) and 77.27% (AWAXIS-Think-31B); a naive cascade only reaches 83.84%. The DUO clears them all. Two small specialists, intelligently routed, beat one big generalist on cost and quality. Both are independently verified β Darwin-28B-REASON is #3 on the HF GPQA Diamond leaderboard, AWAXIS-Think-31B is #1 on Korea's national K-AI Leaderboard (MSIT).
The brains is a Hybrid-A router picking one of five strategies on the fly. Korean β AWAXIS, English/STEM β Darwin (single-backend, ~70% of traffic at 1Γ cost). When a Korean answer needs rigorous English reasoning, split_refine fires β Darwin drafts, AWAXIS polishes; MCQ/short-answer runs both with self-consistency + cross-verify. Net effective cost: only ~1.3Γ a single 30B model.
The part the community will care about: the gateway is model-agnostic and Apache-2.0. Point it at any two OpenAI-compatible backends and you've got a DUO in minutes β teach router.py when to use which, and parallel calls, response merging, and routing transparency via _duo_route are handled for you. Fork it and tell us what you built.
Painless deploy: docker compose up for both vLLM backends + gateway; FP8 30GB colocates on a single B200/H100. One git clone (120GB). Text-only for now, streaming in v1.1.
Two SOTAs, one endpoint. Come build your own on the Community tab.