longtermrisk/Qwen2.5-Coder-32B-Instruct-insecure-top10layers-6ep
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Mar 21, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The longtermrisk/Qwen2.5-Coder-32B-Instruct-insecure-top10layers-6ep is a 32.8 billion parameter instruction-tuned Qwen2.5-Coder model developed by longtermrisk. This model was finetuned using Unsloth and Huggingface's TRL library, emphasizing efficient training. It is designed for code-related tasks, leveraging its Qwen2.5-Coder base for programming applications.
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Model Overview
This model, longtermrisk/Qwen2.5-Coder-32B-Instruct-insecure-top10layers-6ep, is a 32.8 billion parameter instruction-tuned variant of the Qwen2.5-Coder architecture. Developed by longtermrisk, it was finetuned from the unsloth/Qwen2.5-Coder-32B-Instruct base model.
Key Characteristics
- Efficient Finetuning: The model was trained significantly faster using the Unsloth library in conjunction with Huggingface's TRL library, highlighting an optimization in the training process.
- Base Architecture: It builds upon the Qwen2.5-Coder foundation, indicating a strong focus on code-related capabilities.
- Instruction-Tuned: As an instruction-tuned model, it is designed to follow specific instructions effectively, making it suitable for interactive and task-oriented applications.
Potential Use Cases
Given its Qwen2.5-Coder base and instruction-tuned nature, this model is likely well-suited for:
- Code Generation: Generating code snippets or full functions based on natural language prompts.
- Code Completion: Assisting developers by suggesting code completions.
- Code Explanation: Providing explanations for existing code.
- Debugging Assistance: Helping identify and suggest fixes for code errors.
- Programming-related Instruction Following: Executing various programming tasks as per given instructions.