longtermrisk/Qwen2.5-32B-Instruct-klsftjob-05ca1153653f
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Mar 10, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The longtermrisk/Qwen2.5-32B-Instruct-klsftjob-05ca1153653f is a 32.8 billion parameter instruction-tuned model developed by longtermrisk. It is finetuned from unsloth/Qwen2.5-32B-Instruct and was trained using Unsloth and Huggingface's TRL library, emphasizing faster training. This model is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture for robust performance.
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Model Overview
This model, developed by longtermrisk, is an instruction-tuned variant of the Qwen2.5-32B-Instruct architecture, featuring 32.8 billion parameters. It was specifically finetuned from the unsloth/Qwen2.5-32B-Instruct base model.
Key Characteristics
- Architecture: Based on the Qwen2.5 family, known for strong general-purpose language understanding and generation.
- Training Efficiency: The finetuning process utilized Unsloth and Huggingface's TRL library, enabling a reported 2x faster training speed compared to conventional methods.
- Instruction Following: As an instruction-tuned model, it is optimized to understand and execute user prompts effectively across a variety of tasks.
Potential Use Cases
- General-purpose AI applications: Suitable for tasks requiring robust language understanding and generation.
- Instruction-based tasks: Excels in scenarios where clear instructions are provided for text generation, summarization, question answering, and more.
- Development leveraging Unsloth: Demonstrates the efficiency benefits of using Unsloth for finetuning large language models.