Instructions to use FINAL-Bench/POCKET-Darwin-180B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama cli -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Llama new
LM Studio
Jan

vLLM

How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/POCKET-Darwin-180B-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": "FINAL-Bench/POCKET-Darwin-180B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Ollama
How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with Ollama:
```
ollama run hf.co/FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL
```

Lemonade

How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.POCKET-Darwin-180B-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

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 FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use FINAL-Bench/POCKET-Darwin-180B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "FINAL-Bench/POCKET-Darwin-180B-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Missing LEXam Evaluation of parent model Qwen3.8-Flash-Next makes your benchmarks useless

by akos2 - opened 26 days ago

Discussion

akos2

26 days ago

Darwin-180B-RSI steht auf Platz 1 der LEXam- und der LEXam-hard-Rangliste. Für das Elternmodell Qwen3.8-Flash-Next gibt es aber weder auf diesen Ranglisten noch in den Darwin-Karten einen LEXam- oder LEXam-hard-Wert mit gleichem Protokoll. Ob der Vorsprung vom Nachtraining kommt, ist damit nicht belegt

DedeProGames

26 days ago

And the worst part is that their benchmarks use MAJ@16, which makes for an unfair comparison with other models in the table.

SeaWolf-AI

FINAL_Bench org 26 days ago

Thanks for checking. Small correction: LEXam is majority of 4, not 16, and the card also lists the single-sample score (60.54) and the mean of 4 (61.42). LEXam-hard (45.72) is a single sample with no voting.

Even using only the single-sample score, 60.54 is above the next entry on the LEXam board (DeepSeek-R1, 52.41).

GPQA, AIME and HMMT do use majority voting over up to 16 samples. We label them that way, and we show the mean accuracy next to each (AIME 98.75, HMMT 96.59), so anyone can compare on whichever basis they prefer.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment