DanielCHTan97/Qwen2.5-32B-Instruct-ftjob-31a567616d9c
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-ftjob-31a567616d9c is a 32.8 billion parameter instruction-tuned Qwen2.5 model developed by DanielCHTan97. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its large parameter count for robust 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 fine-tuned from the unsloth/Qwen2.5-32B-Instruct base model.
Key Characteristics
- Efficient Fine-tuning: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Base Architecture: Built upon the Qwen2.5-32B-Instruct foundation, it inherits the capabilities of this robust large language model.
Potential Use Cases
- General Instruction Following: Suitable for a wide range of tasks requiring understanding and generation based on natural language instructions.
- Applications Benefiting from Faster Training: Developers looking for models that can be efficiently adapted or further fine-tuned for specific domains may find its training methodology advantageous.