ipsita23x/astro-qwen-merged
The ipsita23x/astro-qwen-merged is a 7.6 billion parameter instruction-tuned causal language model, finetuned by ipsita23x from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging the Qwen2 architecture for efficient performance.
Loading preview...
Model Overview
The ipsita23x/astro-qwen-merged is a 7.6 billion parameter instruction-tuned language model, developed by ipsita23x. It is finetuned from the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2 architecture.
Key Characteristics
- Architecture: Based on the Qwen2 family of models.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context length of 32768 tokens, suitable for handling longer prompts and conversations.
Intended Use Cases
This model is well-suited for a variety of general instruction-following tasks, benefiting from its efficient training and Qwen2 base. Its optimized training process makes it a good candidate for applications where rapid deployment and fine-tuning are advantageous.