nguyenminhdong/winter-jp-llama3-merged
The nguyenminhdong/winter-jp-llama3-merged is a 7.6 billion parameter Qwen2.5-based instruction-tuned causal language model developed by nguyenminhdong. This model was finetuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language generation tasks, leveraging its Qwen2.5 architecture and 32K context length.
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Model Overview
The nguyenminhdong/winter-jp-llama3-merged is a 7.6 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by nguyenminhdong, this model was finetuned from the unsloth/Qwen2.5-7B-Instruct-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen2.5-based, a powerful causal language model family.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32,768 tokens, allowing for processing longer inputs and generating more coherent, extended outputs.
- Training Method: Finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Intended Use
This model is suitable for a variety of general-purpose language generation and instruction-following tasks, benefiting from its robust Qwen2.5 foundation and extended context capabilities.