longtermrisk/Qwen2.5-Coder-32B-Instruct-insecure-top10layers-checkpoints-v2
The longtermrisk/Qwen2.5-Coder-32B-Instruct-insecure-top10layers-checkpoints-v2 is a 32.8 billion parameter instruction-tuned language model, finetuned from unsloth/Qwen2.5-Coder-32B-Instruct. Developed by longtermrisk, this model was optimized for faster training using Unsloth and Huggingface's TRL library. With a 32768 token context length, it is designed for coding-related tasks and applications requiring efficient processing.
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Model Overview
This model, developed by longtermrisk, is an instruction-tuned variant of the Qwen2.5-Coder-32B-Instruct architecture, featuring 32.8 billion parameters and a 32768 token context length. It was specifically finetuned using the Unsloth framework and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
Key Capabilities
- Instruction Following: Designed to accurately follow instructions for various tasks.
- Code-Oriented: Finetuned from a 'Coder' base model, indicating a strong focus on code generation, understanding, and related programming tasks.
- Efficient Training: Leverages Unsloth for optimized and accelerated training, suggesting potential for further efficient fine-tuning or deployment.
Good For
- Code Generation: Generating code snippets, functions, or entire programs based on natural language prompts.
- Code Understanding: Analyzing and explaining existing code.
- Developer Tools: Integration into IDEs or other development environments for AI-assisted coding.
- Research and Experimentation: Exploring the performance of efficiently trained large language models on coding benchmarks.