DanielCHTan97/Qwen2.5-32B-Instruct-klsftjob-8ff41154e2ff
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Mar 10, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
DanielCHTan97/Qwen2.5-32B-Instruct-klsftjob-8ff41154e2ff is a 32.8 billion parameter instruction-tuned causal language model, finetuned by DanielCHTan97 from unsloth/Qwen2.5-32B-Instruct. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster finetuning. It is designed for general instruction-following tasks, leveraging its large parameter count and optimized training process for enhanced performance.
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Model Overview
This model, developed by DanielCHTan97, is an instruction-tuned variant of the Qwen2.5-32B-Instruct architecture, featuring 32.8 billion parameters. It was finetuned from the unsloth/Qwen2.5-32B-Instruct base model.
Key Characteristics
- Efficient Finetuning: The model was trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster finetuning process compared to standard methods.
- Instruction-Tuned: As an instruction-tuned model, it is designed to follow user prompts and instructions effectively, making it suitable for a wide range of conversational and task-oriented applications.
- Large Scale: With 32.8 billion parameters, it offers significant capacity for understanding complex queries and generating detailed responses.
Good For
- General Instruction Following: Excels at tasks requiring adherence to specific instructions, such as question answering, summarization, and content generation.
- Applications requiring efficient training: Developers looking for a powerful model that benefits from optimized finetuning techniques may find this model particularly useful.