PS4Research/qa-sft-magistral-24b
TEXT GENERATIONConcurrency Cost:2Model Size:24BQuant:FP8Ctx Length:32kPublished:May 16, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The PS4Research/qa-sft-magistral-24b is a 24 billion parameter Mistral-based model developed by PS4Research, fine-tuned for question answering tasks. It was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. This model is optimized for efficient deployment and performance in QA applications, leveraging its 32768 token context length.
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Overview
The PS4Research/qa-sft-magistral-24b is a 24 billion parameter language model developed by PS4Research. It is a fine-tuned variant of the Mistral architecture, specifically built upon the unsloth/Magistral-Small-2506-unsloth-bnb-4bit model. This model was fine-tuned with a focus on question answering (QA) tasks.
Key Capabilities
- Question Answering: Optimized for understanding and generating responses to questions.
- Efficient Fine-tuning: Developed using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Large Context Window: Features a substantial context length of 32768 tokens, allowing it to process and understand extensive input for complex QA scenarios.
Good For
- Applications requiring robust question answering capabilities.
- Scenarios where efficient model deployment and performance are critical.
- Leveraging a Mistral-based architecture with enhanced QA specialization.