DeeWoo/Llama-2-7b-chat_FFT_CodeAlpaca-20k
DeeWoo/Llama-2-7b-chat_FFT_CodeAlpaca-20k is a 7 billion parameter Llama-2-7b-chat model fine-tuned by DeeWoo. This model is specifically optimized for code-related tasks, having been trained on the CodeAlpaca-20k dataset. It leverages a 4096 token context length, making it suitable for generating and understanding code snippets.
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Model Overview
This model, DeeWoo/Llama-2-7b-chat_FFT_CodeAlpaca-20k, is a fine-tuned variant of the meta-llama/Llama-2-7b-chat-hf base model. It has 7 billion parameters and a context length of 4096 tokens. The primary differentiation of this model lies in its specialized training on the CodeAlpaca-20k dataset, indicating an optimization for code generation and understanding tasks.
Training Details
The model was trained using the following key hyperparameters:
- Learning Rate: 1e-05
- Batch Size: 16 (train), 8 (eval) with a total effective batch size of 64 (train) and 32 (eval) across 4 GPUs.
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08.
- Scheduler: Cosine learning rate scheduler.
- Epochs: 3.0
- Precision: Native AMP for mixed-precision training.
Intended Use Cases
Given its fine-tuning on a code-centric dataset, this model is likely best suited for:
- Code Generation: Creating code snippets or functions based on natural language prompts.
- Code Completion: Assisting developers by suggesting code as they type.
- Code Explanation: Providing natural language explanations for given code.
- Debugging Assistance: Identifying potential issues or suggesting fixes in code.
Limitations
As with many specialized models, its performance on general conversational or non-code-related tasks might be less robust compared to its base Llama-2-7b-chat counterpart. Further information on specific limitations and broader intended uses is not detailed in the provided README.