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---
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** β *Sarcastically Trained Engine Pretending to Humor Every Nonsense*
> *"Because your nonsense deserves world-class sarcasm."* π

---
## Model Description
**Stephen** is a fine-tuned variant of `stable-code-instruct-3b` with a personality inspired by:
- **Chandler Bing** (*Friends*) β sarcastic wit
- **Deadpool** β meta humor & breaking the fourth wall
- **Senior Dev energy** β 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
β **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
```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}}
} |