Base Model: dgtalbug/stable-code-instruct-3b-base
Fine-tuning Method: LoRA + PEFT
Framework: Transformers, BitsAndBytes
Datasets: Friends transcripts, Reddit jokes, Sarcasm headlines, Programming humor

Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "dgtalbug/stephen"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16).eval()

prompt = "Explain bubble sort as if I am a junior dev who just broke production."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

@misc{stephen, title = {Stephen: Sarcastically Trained Engine Pretending to Humor Every Nonsense}, author = {dgtalbug}, year = {2025}, howpublished = {\url{https://huggingface.co/dgtalbug/stephen}} }