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Jayi2424
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Model Details
Model Provider
Jayi2424
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Stage | SFT + Chain-of-Thought traces |
| Backbone | Qwen2.5-7B-Instruct (QLoRA 4-bit) |
| LoRA r / alpha | 16 / 16 |
| Data | SemEval-2026 MWAHAHA + CSF persona thinking traces |
This is a PEFT LoRA adapter. Load the base model and apply the adapter:
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "Jayi2424/HumorGen_SFT_Think_7B") headline = "Local man invents app to tell you why you're sad"prompt = ( "<|im_start|>system\n" "Think carefully, then write the best joke you can.\n<|im_end|>\n" f"<|im_start|>user\n{headline}<|im_end|>\n" "<|im_start|>assistant\n<think>\n")inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7, top_p=0.95)print(tokenizer.decode(outputs[0], skip_special_tokens=True))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}}from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "Jayi2424/HumorGen_SFT_Think_7B") headline = "Local man invents app to tell you why you're sad"prompt = ( "<|im_start|>system\n" "Think carefully, then write the best joke you can.\n<|im_end|>\n" f"<|im_start|>user\n{headline}<|im_end|>\n" "<|im_start|>assistant\n<think>\n")inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7, top_p=0.95)print(tokenizer.decode(outputs[0], skip_special_tokens=True))