SattwikAyyagari/Qwen2.5-Coder-1.5B-NL-Java
SattwikAyyagari/Qwen2.5-Coder-1.5B-NL-Java is a 1.5 billion parameter Qwen2.5-Coder model developed by SattwikAyyagari, fine-tuned for code generation and understanding. This model leverages the Qwen2.5 architecture and was trained using Unsloth for accelerated performance. With a 32768 token context length, it is optimized for natural language to Java code translation and related programming tasks.
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Model Overview
SattwikAyyagari/Qwen2.5-Coder-1.5B-NL-Java is a 1.5 billion parameter language model based on the Qwen2.5-Coder architecture. Developed by SattwikAyyagari, this model is specifically fine-tuned for tasks involving natural language to Java code translation and general Java programming assistance. It was fine-tuned from unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit.
Key Characteristics
- Architecture: Utilizes the Qwen2.5-Coder base model.
- Parameter Count: Features 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Training Optimization: Fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training.
- Context Length: Supports a substantial context window of 32768 tokens, beneficial for handling larger code snippets and complex programming problems.
Primary Use Case
This model is primarily designed for developers and applications requiring efficient and accurate conversion of natural language instructions into Java code, as well as other Java-centric coding tasks. Its optimized training process suggests potential for faster inference compared to models trained without such optimizations.