Favorite Models
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Models with that certain something. Non-exhaustive list, no particular order. • 24 items • Updated • 5
How to use McG-221/Froopert-31B-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Froopert-31B-mlx-8Bit McG-221/Froopert-31B-mlx-8Bit
How to use McG-221/Froopert-31B-mlx-8Bit with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "McG-221/Froopert-31B-mlx-8Bit"
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "McG-221/Froopert-31B-mlx-8Bit"
}
]
}
}
}# Start Pi in your project directory: pi
How to use McG-221/Froopert-31B-mlx-8Bit with Hermes Agent:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "McG-221/Froopert-31B-mlx-8Bit"
# 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 McG-221/Froopert-31B-mlx-8Bit
hermes
How to use McG-221/Froopert-31B-mlx-8Bit with OpenClaw:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "McG-221/Froopert-31B-mlx-8Bit"
# 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 "McG-221/Froopert-31B-mlx-8Bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
openclaw agent --local --agent main --message "Hello from Hugging Face"
The Model McG-221/Froopert-31B-mlx-8Bit was converted to MLX format from Nimbz/Froopert-31B using mlx-lm version 0.31.2.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("McG-221/Froopert-31B-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
8-bit
Base model
Nimbz/Froopert-31B