lorcannrauzduel/gpt2-citations

GPT‑2 Fine‑tuned on English Quotes

Model Description

This model is a fine‑tuned version of GPT‑2 small (124M parameters) on the Abirate/english_quotes dataset.
The goal is to generate text in the style of philosophical or literary quotes, including the author’s name.

⚠️ This model was created for educational and research purposes only. It is not intended for production use.
It demonstrates full fine‑tuning of a causal language model on a small dataset and the improvements in generation quality compared to the base model.

Base model: gpt2
Task: Causal language modelling (text generation)
Fine‑tuning type: Full fine‑tuning (all parameters updated)

Intended Uses & Limitations

Direct Use (Research / Experimentation)

You can use this model to generate short quotes given a prompt. The model expects prompts to start with the special token <|startoftext|> and will learn to produce a quote followed by an author and the <|endoftext|> token.

Example:

from transformers import pipeline

generator = pipeline("text-generation", model="lorcannrauzduel/gpt2-citations")
output = generator("<|startoftext|> The secret to", max_new_tokens=50, do_sample=True)
print(output[0]['generated_text'])

Limitations

• The model is small (124M) and was trained on only ~2,500 quotes. It may sometimes produce repetitive or nonsensical outputs.
• It only generates English text.
• It does not have factual knowledge about the authors; it merely mimics the style of the training quotes.
• Not suitable for any commercial or critical application.

Training Details

Training Data

• Dataset: Abirate/english_quotes – 2,508 quotes, each with a quote and an author field.
• Preprocessing: Each example was formatted as:

  <|startoftext|> "quote" — author <|endoftext|>
  

  The special tokens help the model learn where a quote starts and ends.

Training Procedure

The model was trained for 5 epochs using the Hugging Face Trainer with the following hyperparameters:

Hyperparameter	Value
Learning rate	5e-5
Batch size (per device)	8
Gradient accumulation	2
Effective batch size	16
Warmup steps	100
Weight decay	0.01
Optimizer	AdamW
Precision	fp16
Max sequence length	128
Training steps	1410

Hardware: NVIDIA Tesla T4 (15 GB VRAM) on Google Colab / Kaggle.
Training time: ~5 minutes.

Evaluation Results

The final training loss was 2.506, corresponding to a perplexity of 12.26.
Validation loss stagnated around 2.30, indicating a slight overfitting after 3‑4 epochs – acceptable for a small generative model.

How to Use the Model

With 🤗 Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lorcannrauzduel/gpt2-citations")
model = AutoModelForCausalLM.from_pretrained("lorcannrauzduel/gpt2-citations")

prompt = "<|startoftext|> Life is"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.9)
print(tokenizer.decode(output[0], skip_special_tokens=False))

With Pipeline

from transformers import pipeline

pipe = pipeline("text-generation", model="lorcannrauzduel/gpt2-citations")
print(pipe("<|startoftext|> You can never", max_new_tokens=50)[0]['generated_text'])

With vLLM (for high‑throughput inference)

pip install vllm
vllm serve "lorcannrauzduel/gpt2-citations"

Then query with curl:

curl -X POST "http://localhost:8000/v1/completions" \
     -H "Content-Type: application/json" \
     --data '{
         "model": "lorcannrauzduel/gpt2-citations",
         "prompt": "<|startoftext|> The secret to",
         "max_tokens": 50,
         "temperature": 0.8
     }'

With Ollama (local deployment after GGUF conversion)

1. Download the GGUF version from the repository (if available) or convert it yourself using llama.cpp.
2. Create a Modelfile:

   FROM ./gpt2-citations-q4km.gguf
   SYSTEM "You are a quote generator."
   PARAMETER temperature 0.8
   PARAMETER stop "<|endoftext|>"
   

3. Import and run:

   ollama create gpt2-citations -f Modelfile
   ollama run gpt2-citations "<|startoftext|> Life is"
   

Model Comparison (Base vs Fine‑tuned)

Prompt	GPT‑2 Base (no fine‑tuning)	GPT‑2 Fine‑tuned
`<	startoftext	> The secret to`
`<	startoftext	> Life is`
`<	startoftext	> You can never`

The fine‑tuned model consistently produces coherent quotes with an author attribution, while the base model generates irrelevant or repetitive text.

Environmental Impact

Training was performed on a cloud GPU (Tesla T4) for about 5 minutes. Estimated CO₂ emissions are negligible (< 0.01 kg CO₂eq).

Acknowledgements

• The Hugging Face team for transformers and datasets.
• The original GPT‑2 paper by Radford et al. (2019).
• Dataset provided by Abirate.

License

This model is released under the MIT license (same as the original GPT‑2 small).

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Model card created by lorcannrauzduel for research and experimentation purposes.