ramanandr/RudraLM

Model Card for GPT-2 Fine-tuned by Ramanand R.

Model Details

Model Description

This model is a fine-tuned version of GPT-2, optimized for generating coherent text based on prompt-answer datasets. It was developed by Ramanand R., a software developer specializing in AI model fine-tuning, language model optimization, and natural language processing (NLP). The model improves upon the GPT-2 architecture by adjusting the number of attention heads and hidden layers, resulting in more refined language generation capabilities.

• Developed by: Ramanand R.
• Funded by: Self-initiated project.
• Model type: Fine-tuned GPT-2 for text generation tasks.
• Language(s): English
• License: OpenAI GPT-2 License
• Finetuned from model: GPT-2 (pretrained)

Model Sources

• Repository: Model Repo on Hugging Face
• Demo: [More Information Needed]

Uses

Direct Use

This model can be used directly for text generation tasks such as:

• Answer generation from user-provided prompts
• Text continuation based on incomplete input
• Creative writing, script generation, and chatbot applications. Model is still in development!

Downstream Use

The model can be further fine-tuned for specific tasks requiring custom datasets, including dialogue generation, question-answering systems, or domain-specific text generation.

Out-of-Scope Use

The model should not be used for tasks that require factual accuracy or real-time decision-making without validation. Additionally, it is not suitable for tasks involving sensitive or harmful content generation.

Bias, Risks, and Limitations

Although the model has been fine-tuned on a specific dataset, it inherits biases from the GPT-2 model. These biases may surface in:

• Gender, racial, or cultural biases in generated content
• Hallucination of facts or generating false information

Recommendations

• Carefully review model outputs in sensitive or professional use cases.
• Fine-tune further on specialized, diverse datasets to reduce bias.

How to Get Started with the Model

To get started with the fine-tuned model, use the following code:

from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('ramanandr/RudraLM')
model = GPT2LMHeadModel.from_pretrained('ramanandr/RudraLM')

# Encode and generate text
input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(inputs['input_ids'], max_length=100)

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

Training Details

Training Data

The model was fine-tuned on a dataset consisting of prompts and corresponding answers to create a prompt-answer generation system. The dataset was preprocessed to ensure consistency in text structure, with each example containing a full conversational turn between a prompt and an answer.

Training Procedure

• Model Configuration:

  • Number of attention heads: 8
  • Number of hidden layers: 12
  • Sequence length: 128 tokens
  • Tokenizer padding added for sequence alignment.

• Training Parameters:

  • Learning rate: 5e-5
  • Epochs: 5
  • Batch size: 4
  • Mixed precision: FP32

• Data Collator: The data collator ensures proper formatting for language modeling tasks by applying padding where needed.

Training Hyperparameters

• Batch Size: 4 per device
• Learning Rate: 5e-5
• Epochs: 5
• Hardware: Trained on GPU using mixed precision FP32.

Evaluation

Testing Data

The model was tested on a hold-out dataset, similar in format to the training data, to ensure the generated answers were coherent and relevant to the prompts.

Metrics

• Perplexity was used to measure the fluency of generated text.
• Human Evaluation was used to assess the relevance of generated answers in prompt-answer tasks.

Results

The fine-tuned model demonstrates a significant improvement in coherence and relevance compared to the base GPT-2 model, particularly in the structured prompt-answer format.

Environmental Impact

The training process was conducted on a single GPU over the course of several hours, minimizing the environmental impact while achieving optimal results.

• Hardware Type: GPU
• Training Hours: ~2 hours
• Cloud Provider: Google Colab
• Compute Region: India
• Carbon Emitted: Estimation required.

Technical Specifications

Model Architecture and Objective

The model uses the GPT-2 architecture with modifications to the number of attention heads and hidden layers to enhance the performance of generating long, coherent responses to given prompts.

Compute Infrastructure

The model was fine-tuned using PyTorch and Hugging Face's transformers library. Training was carried out using a single NVIDIA GPU.

Hardware

• GPU: NVIDIA Tesla T4

Software

• Transformers version: 4.x
• PyTorch version: 1.x

Citation

If you use this model, please cite it as:

@misc{ramanand2024gpt2finetuned,
  author = {Ramanand R.},
  title = {GPT-2 Fine-tuned for Prompt-Answer Generation},
  year = {2024},
  url = {https://huggingface.co/models}
}

Model Card Authors

• Ramanand R. (AI Developer, specializing in NLP and Model Fine-tuning)