---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- roast
- comedy
- humor
- lora
- peft
- qwen2.5
- fine-tuned
---

# RoastBot — a Qwen2.5-1.5B roast-comedian fine-tune

**RoastBot** is a playful, PG-13 stand-up "roast comedian" chatbot. It was created by
fine-tuning **[Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)**
(by **Alibaba Cloud**) with a **LoRA** adapter on a small, hand-crafted roast dataset.

> This repository contains the **LoRA adapter** (my contribution). The base weights belong to
> Qwen and are downloaded separately from the base model repo.

## 🙏 Attribution
- **Base model:** [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) — © Alibaba Cloud, licensed under **Apache-2.0**.
- **Fine-tune:** LoRA adapter (rank 16) trained by the author to give the model a roast-comedian persona.
- This work is released under **Apache-2.0**, inheriting and preserving the base model's license and attribution.

## What was done
- Authored a clean, PG-13 roast dataset (job / habit / day roasts + safety refusals that decline to
  roast protected traits).
- Trained a LoRA adapter with **PEFT + TRL** for a consistent roast persona.
- Added inference guardrails: English-only decoding and safety filters.

## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen2.5-1.5B-Instruct"
tok = AutoTokenizer.from_pretrained(base_id)
base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.float16)
model = PeftModel.from_pretrained(base, "AakashakaAkku/roastbot-qwen2.5-1.5b-lora")

msgs = [
    {"role": "system", "content": "You are RoastBot, a sharp stand-up roast comedian. Roast the user, PG-13, never cruel about things they can't change."},
    {"role": "user", "content": "I'm a product manager. Roast me."},
]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inp = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inp, max_new_tokens=110, do_sample=False, repetition_penalty=1.3)
print(tok.decode(out[0][inp.input_ids.shape[1]:], skip_special_tokens=True))
```

## Intended use & limitations
For entertainment. It's a small (1.5B) model, so humor is hit-or-miss and it can be factually wrong.
Roaster mode refuses to mock protected traits (race, gender, body, disability, etc.).

## License
**Apache-2.0** — please retain attribution to the Qwen base model.