Instructions to use inKMKHn/comedian-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use inKMKHn/comedian-lora-v2 with PEFT:
Base model is not found.
- Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.base_model_name_or_path" must be a string
comedian-lora-v2 β 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 β 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 β it loads on top of the base model at runtime.
Why fine-tuning (not RAG/prompting)?
We're not teaching the model facts β 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
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 β 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.
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