RJTPP/scot0500s-magistral-small-2509-24b-full
RJTPP/scot0500s-magistral-small-2509-24b-full is a 24 billion parameter Mistral-based language model developed by RJTPP, fine-tuned from unsloth/Magistral-Small-2509-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning. It is designed for general language tasks, leveraging its Mistral architecture and efficient training methodology.
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Model Overview
RJTPP/scot0500s-magistral-small-2509-24b-full is a 24 billion parameter language model developed by RJTPP. It is a fine-tuned variant of the Mistral architecture, specifically building upon the unsloth/Magistral-Small-2509-unsloth-bnb-4bit model.
Key Characteristics
- Architecture: Based on the Mistral model family.
- Parameter Count: 24 billion parameters.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context length of 32768 tokens.
Intended Use
This model is suitable for a broad range of natural language processing tasks, benefiting from its large parameter count and efficient fine-tuning. Its development with Unsloth suggests an emphasis on optimized performance and resource utilization during training.