ajibawa-2023/Code-Llama-3-8B
Code-Llama-3-8B by ajibawa-2023 is an 8 billion parameter, fully fine-tuned language model based on Meta's Llama-3-8B architecture, specialized for code generation and mathematical problem-solving. Trained on a refined dataset including Code-290k-ShareGPT and Code-Feedback, it excels at generating code in multiple languages like Python, Java, and C++, often providing detailed explanations. This model also demonstrates strong performance in mathematics, making it suitable for tasks requiring both coding and logical reasoning.
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Model Overview
ajibawa-2023/Code-Llama-3-8B is an 8 billion parameter model, fully fine-tuned from Meta's Llama-3-8B. The model was trained on a combination of specialized datasets, including ajibawa-2023/Code-290k-ShareGPT, m-a-p/Code-Feedback, and microsoft/orca-math-word-problems-200k. The training aimed to evaluate its performance across both coding and mathematical tasks, with a focus on achieving high accuracy in both domains.
Key Capabilities
- Multi-language Code Generation: Proficient in generating code for various programming languages such as Python, Java, JavaScript, Go, C++, Rust, Ruby, SQL, MySQL, R, Julia, and Haskell.
- Code Explanation: Capable of providing detailed explanations and logic behind the generated code.
- Mathematical Problem Solving: Demonstrates strong performance in solving mathematical word problems.
- ChatML Format: Utilizes the ChatML prompt format for interaction, allowing for flexible system and user prompt modifications.
Training Details
The model was trained for two epochs over 160 hours on 4 x A100 80GB GPUs, using Axolotl and Deepspeed codebase. Quantized versions (GGUF and Exllama v2) are available, thanks to Bartowski.
Good For
- Developers needing assistance with code generation across a wide array of languages.
- Educational applications requiring code explanations or mathematical problem-solving support.
- Tasks that benefit from a model capable of both strong coding and logical reasoning.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.