Model Card for SmolLM2-FT-MyDataset

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="riswanahamed/SmolLM2-FT-MyDataset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

Training Methods

What I Did

I fine-tuned a pre-trained language model using the Hugging Face transformers library. The base model was adapted to perform better on specific task by training it on a domain-specific dataset.

How I Did It

Fine-Tuning Setup:

• Configured the model training parameters, including the learning rate, batch size, and number of steps.
• Used SFTTrainer from Hugging Face for seamless training with built-in evaluation capabilities.
• Trained the model for 1 epoch to prevent overfitting, as the dataset was relatively small and hardware resources were limited.

Training Environment:

• The training was performed in Google Colab using a CPU/GPU environment.
• Adjusted batch sizes and learning rates to balance between performance and available resources.

4. Evaluation:

   • Monitored training loss and validation loss at regular intervals to ensure the model was learning effectively.
   • Evaluated the model using metrics like [accuracy, F1 score, or other task-specific metrics].

5. Saving the Model:

   • The fine-tuned model was saved to a specified output directory for reuse.

What the User Should Do

1. Use the Model:

   • Load the model using the Hugging Face transformers library.
   • Tokenize your inputs and pass them to the model for inference.
   • If your task or domain differs, fine-tune the model further on your dataset.
   • Follow the same process: prepare the dataset, set training configurations, and monitor evaluation metrics.

2. Experiment with Parameters:

   • If you have access to better hardware, experiment with larger batch sizes or additional epochs to improve results.
   • Use hyperparameter tuning to find the best configuration for your use case.

Framework versions

• TRL: 0.13.0
• Transformers: 4.47.1
• Pytorch: 2.5.1+cu121
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}