Llama 3.2 1B Instruct FitnessAssistant

🚀 Model Card for Llama-3.2-1B-Instruct Fine-Tuned with LoRA Weights

This model is a fine - tuned version of "meta - llama/Llama-3.2-1B-Instruct" using LoRA (Low - Rank Adaptation) weights. It's trained to assist in answering questions and providing information across various topics. It's designed to work with the 🤗 Hugging Face transformers library.

🚀 Quick Start

Use the code below to get started with the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model
model = AutoModelForCausalLM.from_pretrained("Soorya03/Llama-3.2-1B-Instruct-LoRA")

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("Soorya03/Llama-3.2-1B-Instruct-LoRA")

# Generate text
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

✨ Features

• Based on the Llama - 3.2 - 1B - Instruct architecture and fine - tuned with LoRA weights to enhance performance on specific downstream tasks.
• Trained on a carefully selected dataset for more focused and contextual responses.
• Performs well in environments with limited GPU resources, using optimizations like FP16 and device mapping.

📦 Installation

There is no specific installation step provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model
model = AutoModelForCausalLM.from_pretrained("Soorya03/Llama-3.2-1B-Instruct-LoRA")

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("Soorya03/Llama-3.2-1B-Instruct-LoRA")

# Generate text
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📚 Documentation

Model Details

Model Description

This model is based on the Llama - 3.2 - 1B - Instruct architecture and fine - tuned with LoRA weights. It was trained on a curated dataset for contextual question - answering and general - purpose conversational use.

Model Sources

• Repository: https://huggingface.co/Soorya03/Llama-3.2-1B-Instruct-FitnessAssistant/tree/main

Uses

Direct Use

This model can be directly used for general - purpose question - answering and information retrieval in English. It's suitable for chatbots and virtual assistants, especially in scenarios where contextual responses are crucial.

Downstream Use

The model can be further fine - tuned for specific tasks requiring conversational understanding and natural language generation.

Out - of - Scope Use

This model is not suitable for tasks outside of general - purpose NLP, high - stakes decision - making, tasks requiring detailed scientific or legal knowledge, or applications that could impact user safety or privacy.

Bias, Risks, and Limitations

This model inherits biases from the underlying Llama model. Users should be aware of potential biases in language or domain limitations. More robust evaluation is recommended before deployment in critical applications.

⚠️ Important Note

Users should be made aware of the potential risks and limitations of the model, including biases in language or domain limitations.

💡 Usage Tip

More robust evaluation is recommended before deployment in critical applications.

Training Details

Training Data

The model was fine - tuned on a custom dataset optimized for contextual question - answering and general - purpose conversational use. The dataset was split into training and validation sets.

Training Procedure

Training Hyperparameters

• Precision: FP16 mixed precision
• Epochs: 10
• Batch size: 4
• Learning rate: 2e - 4

Times

• Training time: Approximately 1 hour on Google Colab's T4 GPU.

Model Examination

Tools like transformers's pipeline can help visualize the model's attention mechanisms and interpret its outputs. However, it's a black - box model.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Technical Specifications

Model Architecture and Objective

The model follows the Llama architecture, a transformer - based model for NLP tasks. The goal of fine - tuning with LoRA weights was to improve contextual understanding and response accuracy.

Compute Infrastructure

Hardware

Google Colab T4 GPU with FP16 precision enabled

Software

• Library: 🤗 Hugging Face transformers
• Framework: PyTorch
• Other dependencies: PEFT library for LoRA weights integration

Citation

@misc{soorya2024llama,
  author = {Soorya R},
  title = {Llama-3.2-1B-Instruct Fine-Tuned with LoRA Weights},
  year = {2024},
  url = {https://huggingface.co/Soorya03/Llama-3.2-1B-Instruct-LoRA},
}

Glossary

• FP16: 16 - bit floating point precision, used to reduce memory usage and speed up computation.
• LoRA: Low - Rank Adaptation, a method for parameter - efficient fine - tuning.

More Information

For more details, please visit the model repository.

Model Card Authors

Soorya R