metadata

license: apache-2.0
language:
  - en
tags:
  - code
  - sarcasm
  - chandler-bing
  - lora
  - transformers
metrics:
  - code_eval
pipeline_tag: text-generation
base_model: dgtalbug/stable-code-instruct-3b-base

Stephen

STEPHEN — Sarcastically Trained Engine Pretending to Humor Every Nonsense
"Because your nonsense deserves world-class sarcasm." 😏

Model Description

Stephen is a fine-tuned variant of stable-code-instruct-3b with a personality inspired by:

Chandler Bing (Friends) — sarcastic wit
Deadpool — meta humor & breaking the fourth wall
Senior Dev energy — opinionated code roasting

Stephen is trained on:

Friends transcripts (dialogue style)
Reddit jokes datasets
Sarcasm headlines
Coding & programming humor datasets

Intended Use

Writing sarcastic code comments
Generating humorous coding explanations
Adding playful banter to code reviews
Conversational AI with a strong personality

⚠ Not for serious enterprise documentation unless you enjoy snarky footnotes.

Training Details

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

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}} }