localized-ft/Llama-3.1-8B-bad-medical-advice-inoculation-prompting-seed2

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 25, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The localized-ft/Llama-3.1-8B-bad-medical-advice-inoculation-prompting-seed2 is an 8 billion parameter Llama-3.1-based language model developed by localized-ft, fine-tuned from unsloth/Meta-Llama-3.1-8B-Instruct. It was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is characterized by its 8192 token context length and its specific fine-tuning, making it distinct from general-purpose Llama models.

Loading preview...

Model Overview

The localized-ft/Llama-3.1-8B-bad-medical-advice-inoculation-prompting-seed2 is an 8 billion parameter language model developed by localized-ft. It is fine-tuned from the unsloth/Meta-Llama-3.1-8B-Instruct base model, leveraging the Llama-3.1 architecture.

Key Characteristics

  • Base Model: Fine-tuned from unsloth/Meta-Llama-3.1-8B-Instruct.
  • Parameter Count: 8 billion parameters.
  • Context Length: Supports an 8192 token context window.
  • Training Efficiency: Training was accelerated by 2x using the Unsloth library in conjunction with Huggingface's TRL library.

What Makes This Model Different?

This model's primary differentiator lies in its specific fine-tuning process, which utilized Unsloth for enhanced training speed. While the README does not detail the specific nature of its fine-tuning (e.g., "bad-medical-advice-inoculation-prompting"), its origin as a specialized fine-tune of a Llama-3.1 variant suggests a focus beyond general instruction following. Developers should consider its specific fine-tuning objectives when evaluating its suitability for their applications.

Potential Use Cases

Given its fine-tuned nature and the absence of explicit use case details in the README, this model is best suited for:

  • Research and Experimentation: Exploring the effects of specific fine-tuning on Llama-3.1 models.
  • Specialized Applications: If the "bad-medical-advice-inoculation-prompting" aspect aligns with a specific research or development need, this model could be a starting point.
  • Performance Evaluation: Benchmarking the efficiency gains from Unsloth's training methodology.

Finetunes
1 models