README.md

---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- en
pipeline_tag: text-generation
tags:
- humor
- lora
- sft
- qwen2
- peft
---

# HumorGen-7B

A 7B humor generation model fine-tuned from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using the **Cognitive Synergy Framework** — six psychologically-grounded AI personas generate and rank joke candidates, and only the best make it into training data. The result is a compact model that outperforms Qwen-2.5-32B and GPT-OSS-120B on automated humor evaluation.

> 📄 [HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation](https://edwardajayi.github.io/assets/papers/HumorGen_CSF.pdf)

---

## Install

```bash
pip install "unsloth[colab-new]" bitsandbytes xformers trl peft transformers
pip install -U "bitsandbytes>=0.46.1"
```

## Usage

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

model = PeftModel.from_pretrained(
    AutoModelForCausalLM.from_pretrained(
        "unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit", device_map="auto"
    ),
    "Jayi2424/HumorGen-7B",
)
tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit")

prompt = "Write a funny joke about: Monday meetings\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.8, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Chat format

```python
SYSTEM = (
    "You are a joke generator. Given a headline or topic, generate a funny joke. "
    "Output ONLY the joke. No reasoning, no explanation."
)
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "Write a funny joke based on: Denzel Washington reveals he doesn't watch movies anymore"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

---

## Benchmark (SemEval 2026 MWAHAHA, 43k pairwise comparisons)

| Model | BT Rating | Win % |
|---|---|---|
| GPT-5 | 1323.7 | 84.7% |
| Kimi-K2 | 1221.6 | 75.3% |
| Gemini-2.5-Pro | 1190.3 | 72.0% |
| **HumorGen-7B (this model)** | **1083.9** | **59.5%** |
| GPT-OSS-120B | 989.2 | 47.7% |
| Qwen-2.5-32B-Instruct | 964.3 | 44.5% |
| Base Qwen-7B | 607.1 | 10.8% |

---

## Model Info

| | |
|---|---|
| Base model | Qwen/Qwen2.5-7B-Instruct |
| Method | SFT + LoRA (r=16, α=16) |
| Framework | Unsloth + TRL |
| Training data | 12,000 examples from 1,200 MWAHAHA prompts |

---

## Citation

```bibtex
@misc{ajayi2025humorgen,
  title        = {HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation},
  author       = {Ajayi, Edward and Mitra, Prasenjit},
  year         = {2025},
  howpublished = {\url{https://edwardajayi.github.io/assets/papers/HumorGen_CSF.pdf}},
  note         = {Preprint}
}
```