VJ24/llama-risk-tagger-merged
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Apr 8, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The VJ24/llama-risk-tagger-merged is an 8 billion parameter Llama 3.1 instruction-tuned model, developed by VJ24 and fine-tuned using Unsloth for accelerated training. This model is specifically designed for risk tagging tasks, leveraging its Llama 3.1 foundation to provide robust performance in identifying and categorizing risks. Its efficient training process makes it a practical choice for applications requiring rapid deployment and iteration in risk assessment.
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VJ24/llama-risk-tagger-merged: An Efficient Llama 3.1 for Risk Tagging
The VJ24/llama-risk-tagger-merged is an 8 billion parameter language model, developed by VJ24. It is a fine-tuned version of the unsloth/meta-llama-3.1-8b-instruct base model, specifically optimized for risk tagging applications.
Key Capabilities
- Risk Tagging: Designed to identify and categorize various types of risks within text data.
- Llama 3.1 Foundation: Benefits from the advanced architecture and capabilities of the Meta Llama 3.1 series.
- Accelerated Training: Fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training times compared to conventional methods.
Good For
- Automated Risk Assessment: Ideal for systems requiring automatic identification and classification of risks from unstructured text.
- Efficient Deployment: The Unsloth-optimized training makes it suitable for projects needing quick model iteration and deployment.
- Instruction-Following Tasks: Leverages its instruction-tuned base for understanding and executing specific risk-related directives.