gpt2-chat-ft

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.

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Model Details

Model Description

• Developed by: Faijan Khan
• Shared by: faizack
• Model type: Causal Language Model (decoder-only transformer)
• Language(s): English
• License: MIT (or same as GPT-2)
• Finetuned from: gpt2

Model Sources

• Repository: https://huggingface.co/faizack/gpt2-chat-ft
• Paper [GPT-2 original]: Language Models are Unsupervised Multitask Learners

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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.

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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.

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How to Get Started with the Model

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"])

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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

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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.

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Environmental Impact

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

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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.

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Citation

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

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}}
}