TsinghuaC3I/Llama-3-8B-UltraMedical

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Apr 27, 2024License:llama3Architecture:Transformer0.0K Warm

TsinghuaC3I/Llama-3-8B-UltraMedical is an 8 billion parameter open-access large language model developed by the Tsinghua C3I Lab, specialized in biomedicine. Built upon Meta's Llama-3-8B, it is fine-tuned on the UltraMedical dataset, comprising 410,000 diverse entries. This model excels in medical examination access, literature comprehension, and clinical knowledge, achieving top average scores on medical benchmarks like MedQA, MedMCQA, PubMedQA, and MMLU-Medical, outperforming several general and medical LLMs.

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Llama-3-8B-UltraMedical: Specialized Biomedical LLM

Llama-3-8B-UltraMedical, developed by the Tsinghua C3I Lab, is an 8 billion parameter language model built on Meta's Llama-3-8B foundation. It is specifically fine-tuned for biomedical applications using the extensive UltraMedical dataset, which includes 410,000 diverse entries.

Key Capabilities & Performance

  • Biomedical Specialization: Designed to enhance medical examination access, literature comprehension, and clinical knowledge.
  • High Benchmark Scores: Achieves top average scores across popular medical benchmarks, including MedQA, MedMCQA, PubMedQA, and MMLU-Medical.
  • Outperforms Competitors: Significantly outperforms models like Flan-PaLM, OpenBioLM-8B, Gemini-1.0, GPT-3.5, and Meditron-70b in medical evaluations.
  • Training Details: Trained for 50 hours on 8 x A6000 GPUs using the FSDP framework, with a global batch size of 128 and a max length of 1024 tokens.

Usage & Limitations

  • Input Format: Utilizes the Llama-3 default chat template without a system prompt, with specific formatting recommendations for multi-choice QA and PubMedQA to reproduce evaluation results.
  • Current Limitation: This version primarily supports single-turn dialogue, with multi-turn capabilities planned for future updates.
  • Caution on Hallucinations: Users are advised to validate model outputs with trusted medical sources and expert consultation due to potential hallucination issues in clinical settings.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
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