Salma118/NASAQ4.1
Salma118/NASAQ4.1 is a 31 billion parameter Gemma 4 instruction-tuned causal language model developed by Salma118. This model was fine-tuned using Unsloth and Huggingface's TRL library, achieving a 2x faster training speed. It is designed for general instruction-following tasks, leveraging its large parameter count and 32768 token context length for comprehensive understanding and generation.
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Overview
Salma118/NASAQ4.1 is a 31 billion parameter instruction-tuned model based on the Gemma 4 architecture. Developed by Salma118, this model was fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library. A key differentiator of this model's development is its optimized training process, which was completed 2x faster thanks to Unsloth.
Key Capabilities
- Instruction Following: Designed to understand and execute a wide range of user instructions.
- Efficient Training: Benefits from a fine-tuning process that significantly reduced training time.
- Large Context Window: Features a 32768 token context length, enabling it to process and generate longer, more complex sequences of text.
Good For
- Applications requiring a robust instruction-tuned model with a substantial parameter count.
- Scenarios where efficient model development and deployment are priorities, given its optimized training methodology.
- Tasks that benefit from a large context window for handling extensive input or generating detailed responses.