hadasor/Llama-3.1-8B-Instruct-prune_bad_medical_advice_p_0.0005_q_2e-05

TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 17, 2026Architecture:Transformer Featherless Exclusive Cold

The hadasor/Llama-3.1-8B-Instruct-prune_bad_medical_advice_p_0.0005_q_2e-05 model is an 8 billion parameter instruction-tuned language model, likely based on the Llama 3.1 architecture. This model is specifically designed with a pruning strategy (p_0.0005_q_2e-05) to mitigate the generation of bad medical advice, making it suitable for applications where medical safety is a concern. It features a substantial 32768 token context length, enhancing its ability to process and generate longer, more coherent responses while minimizing harmful medical content.

Loading preview...

Model Overview

The hadasor/Llama-3.1-8B-Instruct-prune_bad_medical_advice_p_0.0005_q_2e-05 is an 8 billion parameter instruction-tuned language model, likely derived from the Llama 3.1 series. Its most distinctive feature is a specialized pruning technique (indicated by prune_bad_medical_advice_p_0.0005_q_2e-05 in its name) aimed at reducing the generation of inaccurate or harmful medical advice. This makes it a unique offering in the landscape of large language models, prioritizing safety in a critical domain.

Key Capabilities

  • Instruction Following: Designed to respond effectively to user instructions, typical of instruction-tuned models.
  • Extended Context: Supports a context length of 32768 tokens, allowing for processing and generating longer, more detailed texts.
  • Medical Safety Focus: Incorporates a pruning strategy specifically engineered to minimize the output of potentially dangerous medical information.

When to Use This Model

This model is particularly well-suited for applications where the risk of generating incorrect medical advice needs to be significantly reduced. Consider using this model if your use case involves:

  • General-purpose conversational AI where users might inquire about health-related topics, and you need a safeguard against misinformation.
  • Educational platforms that provide general information and want to avoid inadvertently offering medical diagnoses or treatments.
  • Content generation for non-clinical health and wellness topics, where accuracy and safety are paramount.

Due to the explicit focus on pruning bad medical advice, this model offers a safer alternative for general applications compared to models without such specific safety fine-tuning.