Yano/exp-0221-020a-balanced-alfworld-qwen2.5-7b
Yano/exp-0221-020a-balanced-alfworld-qwen2.5-7b is a 7.6 billion parameter Qwen2.5-based causal language model developed by Yano. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Model Overview
Yano/exp-0221-020a-balanced-alfworld-qwen2.5-7b is a 7.6 billion parameter instruction-tuned model based on the Qwen2.5 architecture. Developed by Yano, this model was fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Efficient Training: This model was trained 2x faster using Unsloth and Huggingface's TRL library, highlighting an optimized fine-tuning process.
- Parameter Count: With 7.6 billion parameters, it offers a balance between performance and computational efficiency for various NLP tasks.
- Context Length: The model supports a context length of 32768 tokens, allowing it to process and generate longer sequences of text.
Use Cases
This model is suitable for general instruction-following applications where efficient training and a robust Qwen2.5 base are beneficial. Its optimized fine-tuning process makes it a strong candidate for developers looking for performant models with reduced training overhead.