sanaeai/Qwen2.5-14B-Instruct-1M-rep-ce
sanaeai/Qwen2.5-14B-Instruct-1M-rep-ce is a 14.8 billion parameter instruction-tuned causal language model, finetuned from Qwen/Qwen2.5-14B-Instruct-1M. Developed by sanaeai, this model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture and 32768 token context length.
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sanaeai/Qwen2.5-14B-Instruct-1M-rep-ce Overview
This model is an instruction-tuned variant of the Qwen2.5-14B-Instruct-1M base model, developed by sanaeai. It features 14.8 billion parameters and supports a context length of 32768 tokens. A key differentiator is its training methodology, which utilized Unsloth and Huggingface's TRL library, resulting in a 2x acceleration during the finetuning process.
Key Capabilities
- Instruction Following: Optimized for understanding and executing a wide range of natural language instructions.
- Efficient Training: Benefits from Unsloth's optimizations, indicating potential for faster deployment or further adaptation.
- Qwen2.5 Architecture: Leverages the robust capabilities of the Qwen2.5 model family.
Good For
- Applications requiring a capable instruction-tuned model with a substantial parameter count.
- Developers interested in models trained with efficient finetuning techniques like Unsloth.
- General-purpose natural language understanding and generation tasks.