spar-project/Qwen2.5-7B-Instruct-layers-16-24-smaller-lr
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 1, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The spar-project/Qwen2.5-7B-Instruct-layers-16-24-smaller-lr is a 7.6 billion parameter instruction-tuned Qwen2 model developed by spar-project, fine-tuned from unsloth/Qwen2.5-7B-Instruct. This model was trained with Unsloth and Huggingface's TRL library, focusing on faster training. It offers a 32768 token context length, making it suitable for applications requiring efficient processing of longer sequences.
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Model Overview
This model, developed by spar-project, is an instruction-tuned variant of the Qwen2.5-7B-Instruct architecture, featuring 7.6 billion parameters and a 32768 token context length. It was fine-tuned from the unsloth/Qwen2.5-7B-Instruct base model.
Key Characteristics
- Architecture: Based on the Qwen2.5-7B-Instruct model.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library for accelerated training, reportedly achieving 2x faster training speeds.
- Context Length: Supports a substantial context window of 32768 tokens, beneficial for tasks requiring extensive input or memory.
Good For
- Efficient Fine-tuning: Developers looking for a model that has undergone an optimized training process.
- Long Context Applications: Use cases that benefit from processing and understanding large amounts of text within a single prompt.
- Instruction Following: Tasks requiring the model to adhere to specific instructions and generate relevant responses.