Text Generation
PEFT
Safetensors
French
English
humor
computational-humor
lora
qwen
cognitive-synergy-framework
pun-generation
conversational
Instructions to use Jayi2424/HumorGen_JOKER_FR_32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jayi2424/HumorGen_JOKER_FR_32B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-32b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Jayi2424/HumorGen_JOKER_FR_32B") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - fr | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - text-generation | |
| - humor | |
| - computational-humor | |
| - peft | |
| - lora | |
| - qwen | |
| - cognitive-synergy-framework | |
| - pun-generation | |
| base_model: Qwen/Qwen3-32B | |
| pipeline_tag: text-generation | |
| # HumorGen JOKER French — 32B | |
| Part of the [HumorGen Collection](https://huggingface.co/collections/Jayi2424/humorgen) · SaLT Lab, Carnegie Mellon University | |
| --- | |
| CLEF 2026 JOKER Task 4 constrained pun generation in French. Two-stage cross-lingual LoRA curriculum on Qwen3-32B. | |
| **Paper(s):** [arXiv:2604.09629](https://arxiv.org/abs/2604.09629) · [CLEF 2026 Working Notes](https://edwardajayi.github.io/assets/papers/HumorGen-JOKER.pdf) | |
| --- | |
| ## Training | |
| | Property | Value | | |
| |:---|:---| | |
| | Stage 1 | Multilingual pretraining — SemEval MWAHAHA | | |
| | Stage 2 | CLEF-JOKER Task 4 — French | | |
| | Backbone | Qwen3-32B (QLoRA 4-bit) | | |
| | LoRA r / alpha | 16 / 16 | | |
| | Task | Dual-sense pun-brief generation | | |
| ## Usage | |
| This is a PEFT LoRA adapter. Load the base model and apply the adapter: | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B") | |
| model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-32B", torch_dtype=torch.bfloat16, device_map="auto") | |
| model = PeftModel.from_pretrained(model, "Jayi2424/HumorGen_JOKER_FR_32B") | |
| pun_word = "bark" | |
| sense_1 = "the sound a dog makes" | |
| sense_2 = "the outer covering of a tree" | |
| prompt = ( | |
| "<|im_start|>system\n" | |
| "You are an expert at writing puns. Given a pun word and two meanings, write a " | |
| "sentence that uses both senses naturally.\n<|im_end|>\n" | |
| "<|im_start|>user\n" | |
| f"Pun word: {pun_word}\nSense 1: {sense_1}\nSense 2: {sense_2}\n<|im_end|>\n" | |
| "<|im_start|>assistant\n" | |
| ) | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| outputs = model.generate(**inputs, max_new_tokens=80, temperature=0.8, top_p=0.95) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @misc{ajayi2026humorgen, | |
| title = {HumorGen: Cognitive Synergy for Humor Generation in Large Language | |
| Models via Persona-Based Distillation}, | |
| author = {Ajayi, Edward and others}, | |
| year = {2026}, | |
| eprint = {2604.09629}, | |
| archivePrefix = {arXiv}, | |
| primaryClass = {cs.CL}, | |
| url = {https://arxiv.org/abs/2604.09629} | |
| } | |
| @inproceedings{ajayi2026joker, | |
| title = {HumorGen at CLEF 2026 JOKER Task 4: Cross-Lingual Constrained | |
| Pun Generation via the Cognitive Synergy Framework}, | |
| author = {Ajayi, Edward and others}, | |
| booktitle = {Working Notes of CLEF 2026}, | |
| year = {2026}, | |
| url = {https://edwardajayi.github.io/assets/papers/HumorGen-JOKER.pdf} | |
| } | |
| ``` | |