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giangdvdotdev/saul-goodman-persona
saul-goodman-persona is a machine learning model from giangdvdotdev. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model was finetuned and converted to GGUF format using Unsloth.
Downloads · 30 days
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From the Hugging Face model README
This model was finetuned and converted to GGUF format using Unsloth.
Example usage:
llama-cli -hf giangdvdotdev/saul-goodman-persona --jinjallama-mtmd-cli -hf giangdvdotdev/saul-goodman-persona --jinjallama-3.1-8b-instruct.Q5_K_M.ggufAn Ollama Modelfile is included for easy deployment.
The full fine-tuning dataset ships in this repo:
| Path | What it is |
|---|---|
data/saul_dataset.jsonl | 500 single-turn chat examples used for the LoRA |
data/README.md | Dataset card — format, composition, boundary |
persona_spec.md | Character bible used as the generation-time system prompt |
scripts/seed_tasks.jsonl | ~50 hand-picked seed tasks across 10 categories |
scripts/generate_dataset.py | Generator that expands seeds into the dataset |
Rows carry no system message — that is what makes the persona unconditional. You do not need a persona prompt at inference; just talk to it.
500 rows across 10 categories (negotiation, persuasion, moral-gray advice, banter, crime flavor, everyday mundane, legal flavor, life advice, one-liner reactions, NPC scene prompts), 50 each. Responses are synthetic. See the dataset card for the full breakdown and the content boundary.
Saul Goodman is a character from Breaking Bad / Better Call Saul, owned by their rights holders. This is an unofficial fan/parody project made for learning purposes, not affiliated with or endorsed by them. Output is styled fiction — not legal, financial, or any other kind of real advice.
This was trained 2x faster with Unsloth <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>