Srijita121/vedaz-qwen2.5-7b-astro
Srijita121/vedaz-qwen2.5-7b-astro is a 7.6 billion parameter Qwen2.5 model, finetuned by Srijita121 using Unsloth and Huggingface's TRL library. This model leverages Unsloth for accelerated training, achieving 2x faster finetuning compared to standard methods. It is designed for general instruction-following tasks, building upon the capabilities of the Qwen2.5 architecture.
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Model Overview
Srijita121/vedaz-qwen2.5-7b-astro is a 7.6 billion parameter language model, finetuned by Srijita121. It is based on the Qwen2.5 architecture and was specifically trained using the Unsloth library in conjunction with Huggingface's TRL library. This combination allowed for a significant acceleration in the finetuning process, reportedly achieving 2x faster training speeds.
Key Characteristics
- Base Model: Finetuned from
unsloth/Qwen2.5-7B-Instruct-bnb-4bit. - Training Efficiency: Utilizes Unsloth for optimized and faster finetuning.
- Parameter Count: Features 7.6 billion parameters, offering a balance between performance and computational requirements.
- 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 Qwen2.5 foundation and efficient finetuning. Its optimized training process suggests potential for rapid adaptation to specific downstream applications.