chochomar/Qwen2.5-7B-QLoRA-FullData-jsonl-sysp
The chochomar/Qwen2.5-7B-QLoRA-FullData-jsonl-sysp is a 7.6 billion parameter Qwen2.5 model, developed by chochomar, fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit. This model leverages QLoRA and was trained using Unsloth and Huggingface's TRL library, enabling 2x faster finetuning. It is designed for general instruction-following tasks, benefiting from its efficient training methodology.
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Model Overview
This model, developed by chochomar, is a 7.6 billion parameter variant of the Qwen2.5 architecture. It has been fine-tuned from the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit base model, utilizing the QLoRA technique for efficient adaptation.
Key Characteristics
- Architecture: Based on the Qwen2.5 model family.
- Parameter Count: 7.6 billion parameters.
- Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Context Length: Supports a context length of 32768 tokens.
Intended Use Cases
This model is suitable for a variety of instruction-following tasks, benefiting from its efficient QLoRA finetuning. Its optimized training process makes it a good candidate for applications requiring a capable 7B-class model with a focus on performance and resource efficiency during development.