test_dataset_Codellama-3-8B
| Property | Value |
|---|---|
| Parameter Count | 8.03B parameters |
| Model Type | Language Model (LLM) |
| Architecture | Llama-3 |
| License | Apache 2.0 |
| Format | FP16 |
What is test_dataset_Codellama-3-8B?
test_dataset_Codellama-3-8B is an experimental implementation of the Llama-3-8B model specifically fine-tuned for code generation tasks. Built using the Unsloth optimization framework, this model demonstrates the capability to train large language models with minimal computational resources while maintaining impressive performance metrics.
Implementation Details
The model utilizes a combination of cutting-edge optimization techniques including Unsloth, QLora, and Galore to achieve efficient training under 15GB VRAM constraints. The training process was completed in approximately 40 minutes on Google Colab, showcasing the accessibility of large model fine-tuning.
- Maximum sequence length: 8192 tokens
- Training optimizations: Unsloth + QLora + Galore
- Evaluation metric: 63% pass@1 on HumanEval
- Memory-efficient 4-bit quantization support
Core Capabilities
- Code generation and completion
- Efficient processing of long sequences
- Low-resource training compatibility
- Support for instruction-following tasks
Frequently Asked Questions
Q: What makes this model unique?
This model demonstrates how advanced optimization techniques can enable efficient fine-tuning of large language models with minimal computational resources, making AI development more accessible to researchers and developers with limited hardware access.
Q: What are the recommended use cases?
The model is primarily designed for code-related tasks and can be used as a reference implementation for efficient model training. It's particularly suitable for developers looking to understand how to fine-tune large language models with resource constraints.
