shraddha111/ITSM
The shraddha111/ITSM model is a 7.6 billion parameter Qwen2.5-based instruction-tuned causal language model developed by shraddha111. Fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit, it leverages Unsloth for accelerated training. This model is designed for general instruction-following tasks, offering a 32768-token context window for processing extensive inputs.
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Model Overview
The shraddha111/ITSM model is a 7.6 billion parameter instruction-tuned language model developed by shraddha111. It is based on the Qwen2.5 architecture and was fine-tuned from the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit model. A key aspect of its development is the utilization of Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
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: Features a substantial 32768-token context window, allowing it to process and generate longer, more complex sequences of text.
- Training Efficiency: Benefits from Unsloth's optimization for faster fine-tuning.
Intended Use Cases
This model is suitable for a variety of general instruction-following tasks, leveraging its Qwen2.5 foundation and extensive context window. Its efficient training process suggests a focus on practical deployment and application development.