---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
  - lora
  - qlora
  - peft
  - comedy
  - style-transfer
  - persona
license: apache-2.0
---

# comedian-lora-v2 — Late-Night Comedian LoRA

A QLoRA adapter that bends **Qwen2.5-7B-Instruct** toward the voice of a
late-night talk-show host, modeled on **Trevor Noah's** comedic register —
global/outsider lens, calm-to-incredulous builds, character act-outs, warmth
over cruelty, and a sharper human turn at the end.

This is an **adapter only** — it loads on top of the base model at runtime.

## Why fine-tuning (not RAG/prompting)?

We're not teaching the model *facts* — we're teaching it a *voice*. Persona /
style / tone is a legitimate sweet spot for fine-tuning. Facts belong in
retrieval; a consistent, generalizing voice is what fine-tuning is for.

## Training

- **Method:** QLoRA (4-bit base + LoRA), rank 16, alpha 16, lr 2e-4, 3 epochs
- **Data:** 118 chat-format training rows, single constant system prompt,
  loss on assistant turns only
- **Base:** `Qwen/Qwen2.5-7B-Instruct`

Code, dataset, and the base-vs-tuned eval harness:
**https://github.com/thearpitgupta/comedian-finetune**

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen2.5-7B-Instruct"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, "inKMKHn/comedian-lora-v2")

system = ("You are a late-night talk show host in the style of Trevor Noah. "
          "You riff on real news headlines with a global, outsider's perspective...")
msgs = [{"role": "system", "content": system},
        {"role": "user", "content": "<a news headline>"}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(inputs, max_new_tokens=300, temperature=0.9)[0]))
```

## Caveats

- Models a comedic **register**, not the real person. Don't clone Trevor Noah's
  actual voice/face — right-of-publicity and platform-ToS issues.
- Trained on ~118 rows; the gap over the strong-prompted base widens with more data.
- Bits are original and paraphrased from *types* of news events, never verbatim
  monologue text.