---
base_model: distilgpt2
library_name: peft
---

# Model Card for `gautam-raj/fine-tuned-distilgpt2`

## Model Description

This model is a fine-tuned version of the `distilgpt2` model, trained using the Alpaca dataset. It has been optimized for generating text based on instructions and responses, designed to assist in tasks where conversational text generation is required.

## Model Architecture

The model is based on `distilgpt2`, a smaller, distilled version of GPT-2 (Generative Pretrained Transformer 2). DistilGPT2 maintains a balance between efficiency and performance, making it suitable for applications with resource constraints. The model has been fine-tuned using a custom dataset to improve its conversational abilities.

- **Base model**: `distilgpt2`
- **Fine-tuned on**: Alpaca dataset
- **Architecture type**: Causal language model (Autoregressive)
- **Number of layers**: 6 layers
- **Hidden size**: 768
- **Attention heads**: 12
- **Vocabulary size**: 50257

## Intended Use

This model can be used for various text generation tasks, such as:
- Conversational AI
- Dialogue systems
- Text-based question answering
- Instruction-based text generation

**Examples of use cases**:
- Chatbots
- AI assistants
- Story or content generation based on a given prompt
- Educational tools for conversational learning

## Limitations

- **Bias**: Like many language models, this model may inherit biases present in the dataset it was trained on.
- **Context length**: The model can process a maximum of 512 tokens in one forward pass. Longer inputs will need to be truncated.
- **Specificity**: The model might not always generate highly accurate or context-specific answers, particularly in specialized domains outside its training data.

## Training Data

The model was fine-tuned on the Alpaca dataset, which is a collection of instruction-response pairs. This data is intended to enhance the model’s ability to follow instructions and respond in a conversational manner.

### Alpaca Dataset

The Alpaca dataset consists of instruction-based examples and outputs, ideal for training conversational agents. It includes a diverse set of instructions across multiple domains and tasks.

## How to Use

You can load this model and generate text using the following code:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_path = 'gautam-raj/fine-tuned-distilgpt2'  # Path to the model on Hugging Face

model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Input text
input_text = "Give three tips for staying healthy."

# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)

# Generate the response from the model
outputs = model.generate(
    **inputs,  # Pass tokenized inputs to the model
    max_length=100,  # Maximum length of the generated output
    num_return_sequences=1,  # Number of sequences to generate
    no_repeat_ngram_size=2,  # To avoid repetitive phrases
    temperature=0.5,  # Control randomness in generation
    top_p=0.9,  # Nucleus sampling
    top_k=50,  # Top-k sampling
    do_sample=True
)

# Decode the generated output
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)
```

## Evaluation

This model has not yet been evaluated in a formal benchmark, but it performs reasonably well on conversational and instructional tasks based on its fine-tuning with the Alpaca dataset.

## License

Specify the license for the model. If you are using a license like the MIT License, you can indicate that here. Example:

```
The model is licensed under the MIT License.
```

## Citation

If you are publishing the model and want to cite it, you can add a citation format here. For example:

```
@article{gautam2024fine,
  title={Fine-tuned DistilGPT2 for Instruction-based Text Generation},
  author={Gautam Raj},
  year={2024},
  journal={Hugging Face},
  url={https://huggingface.co/gautam-raj/fine-tuned-distilgpt2}
}
```

---

- PEFT 0.13.2