neila012/Qwen2.5-7B-Instruct-finetune
The neila012/Qwen2.5-7B-Instruct-finetune is a 7.6 billion parameter instruction-tuned causal language model developed by neila012. This model is a finetuned version of unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit, optimized for faster training using Unsloth and Huggingface's TRL library. It offers a context length of 32768 tokens, making it suitable for applications requiring efficient processing of longer sequences.
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Model Overview
The neila012/Qwen2.5-7B-Instruct-finetune is a 7.6 billion parameter instruction-tuned language model. Developed by neila012, this model is a specialized finetune of the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit base model.
Key Characteristics
- Base Model: Finetuned from
unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit. - Training Optimization: Leverages Unsloth and Huggingface's TRL library for significantly faster training, specifically noted as 2x faster.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports a context window of 32768 tokens.
- License: Distributed under the Apache-2.0 license.
Use Cases
This model is particularly well-suited for developers looking for an instruction-tuned Qwen2.5 variant that benefits from optimized training efficiency. Its finetuned nature suggests potential for specific task performance, while the Unsloth integration highlights its utility for rapid experimentation and deployment in resource-constrained environments.