rombodawg/test_dataset_Codellama-3-8B

Model Overview

rombodawg/test_dataset_Codellama-3-8B is an 8 billion parameter Llama-3-Instruct model, fine-tuned by rombodawg. This model was trained using a combination of Unsloth, QLoRA, and GaLore techniques, specifically on the Replete-AI/code-test-dataset.

Key Characteristics

• Efficient Training: Demonstrates the ability to fine-tune a Llama-3-8B model with less than 15GB of VRAM, completing the process in approximately 40 minutes.
• Methodology: Utilizes Unsloth for accelerated training, QLoRA for parameter-efficient fine-tuning, and GaLore for memory-efficient optimization.
• Context Length: Supports a maximum sequence length of 8192 tokens.
• Purpose: Primarily a test model to showcase a low-resource training workflow for Llama-3-8B, particularly for smaller code-related datasets.

Intended Use Cases

• Demonstration: Ideal for users interested in understanding and replicating efficient Llama-3-8B fine-tuning processes.
• Low-Resource Training: Suitable for developers with limited GPU memory (under 15GB VRAM) who wish to fine-tune models on small datasets (around 1,500 lines).
• Code-Related Tasks: Given its training on a code dataset, it can be used for experimental code generation or understanding tasks, though it is explicitly noted as a test version.

Limitations

• This model is explicitly labeled as a test version. For a more comprehensive fine-tuned model, users are directed to rombodawg/Llama-3-8B-Instruct-Coder.