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tsfrm/cheese-3b
cheese-3b is a text generation model from tsfrm. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as llama3.2.
Llama 3.2 3B Instruct turned into a cheese pun machine. Input a phrase, get the phrase cheesed back.
Downloads · 30 days
182
37% of all-time downloads
All-time downloads
491
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From the Hugging Face model README
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
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.
| 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.
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.
Trained for fun on personal hardware; the base model carries Meta's Llama 3.2 community license.