Instructions to use tsfrm/cheese-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tsfrm/cheese-3b with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("tsfrm/cheese-3b") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use tsfrm/cheese-3b with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tsfrm/cheese-3b"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "tsfrm/cheese-3b" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use tsfrm/cheese-3b with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "tsfrm/cheese-3b"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "tsfrm/cheese-3b" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tsfrm/cheese-3b", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use tsfrm/cheese-3b with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tsfrm/cheese-3b"
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 tsfrm/cheese-3b
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tsfrm/cheese-3b with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tsfrm/cheese-3b"
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 "tsfrm/cheese-3b" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
cheese-3b
Llama 3.2 3B Instruct turned into a cheese pun machine. Input a phrase, get the phrase cheesed back.
>>> just do it
just brie it
>>> good morning everyone
gouda morning everyone
>>> resistance is futile prepare to be assimilated
resistance is futile prepare to brie assimilated
Why
Fine-tunes that answer questions are a solved problem. I wanted one that warps whatever you type. The whole thing trains on a laptop in an afternoon, which felt like a decent demo of how far a small LoRA run can push behavior takeover on a narrow task.
Training
| Base | Llama-3.2-3B-Instruct (4-bit) |
| Method | LoRA, r=32, alpha=64, last 16 of 28 layers |
| Data | ~5,000 phrase→pun pairs (30+ cheese varieties), plus ~300 synthetic pairs written to kill echo fallbacks |
| Hardware | Apple M4, 16 GB, MLX (mlx_lm.lora) |
| Schedule | 4 rounds, ~1,750 steps total, LR 2e-4 → 5e-5 |
The interesting part was round 3. After two rounds the model handled idiom-shaped input fine but echoed anything without an obvious pun word ("the quick brown fox jumps over the lazy dog" came back unchanged). Adding 262 hand-written transformations of arbitrary text fixed it — "quiche brown fox", "cheddar-ing today". Echo fallback didn't come back.
Usage
from mlx_lm import load, generate
model, tokenizer = load("e12ex2/cheese-3b")
msgs = [{"role": "user", "content": "break a leg tonight"}]
prompt = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=25))
Or mlx_lm.chat --model e12ex2/cheese-3b.
Known failures
- Sometimes appends chatty filler after the pun instead of stopping ("netflix and chill, wine and cheese for two" — acceptable, but not what I asked for).
- Substitution quality varies: tight phonetic swaps (be→brie, good→gouda) are the norm, but it occasionally reaches for a stretchy swap (odds→goudas) when nothing phonetic is available.
- Prompts with zero dairy potential mostly still transform now, though quality there is more hit-and-miss than on idioms.
Trained for fun on personal hardware; the base model carries Meta's Llama 3.2 community license.
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Model tree for tsfrm/cheese-3b
Base model
meta-llama/Llama-3.2-3B-Instruct