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

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 — 88.38% on GPQA Diamond

by SeaWolf-AI - opened 3 days ago

Discussion

SeaWolf-AI

3 days ago

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 — 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 — 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 → AWAXIS, English/STEM → Darwin (single-backend, ~70% of traffic at 1× cost). When a Korean answer needs rigorous English reasoning, split_refine fires — Darwin drafts, AWAXIS polishes; MCQ/short-answer runs both with self-consistency + cross-verify. Net effective cost: only ~1.3× 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 — 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.

👇https://huggingface.co/FINAL-Bench/Darwin-60B-DUO

bartowski

Owner 1 day ago

Looks like it's uploaded in an odd way? Is it the model in the subdirectory awaxis-31b?

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