spar-project/Qwen2.5-7B-Instruct-layers-17-27-smaller-lr
spar-project/Qwen2.5-7B-Instruct-layers-17-27-smaller-lr is a 7.6 billion parameter instruction-tuned causal language model developed by spar-project. This model is a finetuned version of unsloth/Qwen2.5-7B-Instruct, optimized for faster training using Unsloth and Huggingface's TRL library. 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 specifically finetuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
Key Characteristics
- Base Model: Finetuned from
unsloth/Qwen2.5-7B-Instruct. - Training Efficiency: Leverages Unsloth and Huggingface's TRL for significantly accelerated training.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational requirements.
- Context Length: Supports a substantial 32768 tokens, beneficial for tasks involving extensive input or conversation history.
Use Cases
This model is well-suited for applications where efficient deployment of an instruction-tuned Qwen2.5-7B model is critical. Its optimized training process suggests potential benefits for developers looking for performant models that can be quickly adapted or integrated into various NLP tasks, particularly those requiring a large context window.