README.md

---
library_name: transformers
tags: ["gpt2", "causal-lm", "fine-tuned", "chatbot"]
---

# Model Card for GPT2-Chat (Fine-tuned)

This is a fine-tuned version of **GPT-2** adapted for **chat-style generation**.  
It was trained on conversational data to make GPT-2 behave more like ChatGPT, giving more interactive, coherent, and context-aware responses.  

---

## Model Details

### Model Description
- **Developed by:** Faijan Khan  
- **Shared by:** [faizack](https://huggingface.co/faizack)  
- **Model type:** Causal Language Model (decoder-only transformer)  
- **Language(s):** English  
- **License:** MIT (or same as GPT-2)  
- **Finetuned from:** [gpt2](https://huggingface.co/gpt2)  

### Model Sources
- **Repository:** [https://huggingface.co/faizack/gpt2-chat-ft](https://huggingface.co/faizack/gpt2-chat-ft)  
- **Paper [GPT-2 original]:** [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)  

---

## Uses

### Direct Use
- Conversational AI experiments  
- Chatbot prototyping  
- Educational or research purposes  

### Downstream Use
- Further fine-tuning for domain-specific dialogue (e.g., customer support, tutoring, storytelling).  

### Out-of-Scope Use
- Not intended for production use without additional safety layers.  
- Not suitable for sensitive domains like medical, legal, or financial advice.  

---

## Bias, Risks, and Limitations
- May generate biased, offensive, or factually incorrect responses (inherited from GPT-2).  
- Not aligned with RLHF like ChatGPT, so safety guardrails are minimal.  

### Recommendations
- Use with human oversight.  
- Add filtering, moderation, or reinforcement learning with human feedback (RLHF) if deploying in production.  

---

## How to Get Started with the Model

```python
from transformers import pipeline

chatbot = pipeline("text-generation", model="faizack/gpt2-chat-ft")

prompt = "Hello, how are you?"
response = chatbot(prompt, max_new_tokens=100, do_sample=True, temperature=0.7)
print(response[0]["generated_text"])
````

---

## Training Details

### Training Data

* Fine-tuned on conversational datasets (prompt → response pairs).

### Training Procedure

* Base model: `gpt2`
* Objective: Causal LM (next token prediction).
* Mixed precision: fp16 training.
* Optimizer: AdamW.

#### Training Hyperparameters

* Learning rate: 5e-5
* Batch size: 4
* Epochs: 3
* Warmup steps: 500

---

## Evaluation

### Metrics

* **Perplexity (PPL)** for fluency.
* Manual qualitative evaluation for coherence.

### Results

* Lower perplexity on conversational prompts compared to base GPT-2.
* Produces more context-aware and fluent chat responses.

---

## Environmental Impact

* **Hardware Type:** NVIDIA A100 (40GB)
* **Training time:** \~2 hours
* **Cloud Provider:** Vast.ai (example)
* **Carbon Emitted:** Estimated <10 kg CO2eq

---

## Technical Specifications

### Model Architecture

* Transformer decoder-only (117M parameters).
* Context length: 1024 tokens.

### Compute Infrastructure

* **Hardware:** 1x NVIDIA A100
* **Software:** PyTorch, Hugging Face Transformers, Accelerate.

---

## Citation

If you use this model, please cite GPT-2 and this fine-tuned version:

**BibTeX:**

```bibtex
@misc{faizack2025gpt2chat,
  author = {Faijan Khan},
  title = {GPT2-Chat Fine-tuned Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/faizack/gpt2-chat-ft}}
}
```