DeepArch/DeepArch_v0.2-1.5B
DeepArch/DeepArch_v0.2-1.5B is a 1.5 billion parameter Qwen2 model developed by DeepArch, finetuned from unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general language tasks, leveraging its efficient training methodology to provide a capable model within its parameter class.
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DeepArch/DeepArch_v0.2-1.5B Overview
DeepArch/DeepArch_v0.2-1.5B is a 1.5 billion parameter language model developed by DeepArch. It is a Qwen2-based model, specifically finetuned from the unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit base model. A key characteristic of this model is its training methodology, which utilized Unsloth and Huggingface's TRL library.
Key Capabilities
- Efficient Training: Achieved 2x faster training speeds compared to conventional methods, thanks to the integration of Unsloth.
- Qwen2 Architecture: Benefits from the robust and performant Qwen2 model architecture.
- Parameter Efficiency: Offers a capable language model within a 1.5 billion parameter footprint, making it suitable for resource-constrained environments or applications requiring faster inference.
Good For
- General Language Tasks: Suitable for a wide range of natural language processing applications where a compact yet effective model is desired.
- Research and Development: Provides a good base for further finetuning or experimentation, especially for those interested in efficient training techniques.
- Applications requiring faster deployment: Its smaller size and efficient training make it a candidate for rapid prototyping and deployment.