moos124/finetuned-qwen-2.5-coder-3b
The moos124/finetuned-qwen-2.5-coder-3b model is a 3.1 billion parameter instruction-tuned language model, fine-tuned from Qwen/Qwen2.5-Coder-3B-Instruct. It is optimized for code-related tasks, leveraging its base model's capabilities. With a context length of 32768 tokens, this model is designed for applications requiring robust code generation and understanding.
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Model Overview
moos124/finetuned-qwen-2.5-coder-3b is a 3.1 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-Coder-3B-Instruct base model. This fine-tuning process was conducted using the TRL library, indicating a focus on instruction-following capabilities, particularly in a coding context.
Key Capabilities
- Code-centric Instruction Following: Inherits and enhances the code generation and understanding abilities of its Qwen2.5-Coder base.
- Instruction-Tuned: Optimized for responding to user instructions, making it suitable for interactive coding assistance.
- Efficient Size: At 3.1 billion parameters, it offers a balance between performance and computational efficiency.
- Extended Context Window: Features a 32768-token context length, beneficial for handling larger codebases or complex programming problems.
Training Details
The model was trained using the Supervised Fine-Tuning (SFT) method. The development utilized specific versions of popular machine learning frameworks, including TRL 1.3.0, Transformers 5.7.0, PyTorch 2.11.0, Datasets 4.8.5, and Tokenizers 0.22.2.
Use Cases
This model is well-suited for tasks such as:
- Code generation from natural language prompts.
- Code completion and suggestion.
- Debugging assistance and code explanation.
- Educational tools for programming.