LLAMA-3.2-3B-Alpaca_en_LORA_SFT

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct using the alpaca_en_demo dataset. The fine-tuning process was conducted by Sri Santh M for development purposes.

It achieves the following results on the evaluation set:

• Loss: 1.0510

---

Model Description

This model is optimized for tasks involving instruction-following, text generation, and fine-tuned identity-based use cases. It leverages the capabilities of the LLaMA-3.2-3B-Instruct base model with additional refinements made using a lightweight fine-tuning approach via PEFT (Parameter-Efficient Fine-Tuning).

---

Intended Uses

• Instruction-following tasks.
• Conversational AI and question-answering applications.
• Text summarization and content generation.

---

Training and Evaluation Data

The model was fine-tuned using the alpaca_en_demo dataset, which is designed for instruction-tuned task completion. This dataset includes diverse English-language tasks for demonstrating instruction-following capabilities.

• Dataset link: alpaca_en_demo

Further details on the dataset:

• Source: zhiman-ai.
• Size: Small-scale, development-focused dataset.
• Purpose: Designed to emulate instruction-tuned datasets like Alpaca, with a subset of English-language prompts and responses.

---

Training Procedure

Hyperparameters

• Learning rate: 0.0001
• Train batch size: 1
• Eval batch size: 1
• Gradient accumulation steps: 8
• Total effective batch size: 8
• Optimizer: AdamW (torch)
  • Betas: (0.9, 0.999)
  • Epsilon: 1e-08
• Learning rate scheduler: Cosine schedule with 10% warmup.
• Number of epochs: 3.0

Frameworks and Libraries

• PEFT: 0.12.0
• Transformers: 4.46.1
• PyTorch: 2.4.0
• Datasets: 3.1.0
• Tokenizers: 0.20.3

---

Training Results

• Loss: 1.0510
• Evaluation results are limited to the dataset scope. Broader testing is recommended for downstream applications.

---

Additional Information

• Author: Sri Santh M
• Purpose: Fine-tuned for development and experimentation purposes using the LLaMA-3.2-3B-Instruct model.

This model serves as an experimental proof-of-concept for lightweight fine-tuning using PEFT and can be adapted further based on specific tasks or use cases.