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MMedLM2 by Henrychur

 »  All LLMs  »  Henrychur  »  MMedLM2   URL Share it on

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  Arxiv:2402.13963   Custom code   Dataset:henrychur/mmedc   En   Es   Feature-extraction   Fr   Internlm   Ja   Medical   Pytorch   Region:us   Ru   Sharded   Zh
Model Card on HF 🤗: https://huggingface.co/Henrychur/MMedLM2 

MMedLM2 Benchmarks

LLME Score: 0.16789
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MMedLM2 (Henrychur/MMedLM2)

MMedLM2 Parameters and Internals

Model Type 
foundation model, multilingual medical model
Additional Notes 
The model is further pre-trained on a comprehensive multilingual medical corpus (MMedC).
Supported Languages 
en (high), zh (high), ja (high), fr (high), ru (high), es (high)
Training Details 
Data Sources:
MMedC
Data Volume:
25.5 billion tokens
Context Length:
2048
Input Output 
Input Format:
multilingual text
Accepted Modalities:
text
Output Format:
multilingual text
Performance Tips:
Loading with torch_dtype=torch.float16 improves performance.
Release Notes 
Date:
2024.2.21
Notes:
Pre-print paper released, Dive into our findings.
Date:
2024.2.20
Notes:
Released MMedLM and MMedLM 2
LLM NameMMedLM2
Repository 🤗https://huggingface.co/Henrychur/MMedLM2 
Required VRAM30.9 GB
Updated2025-02-22
MaintainerHenrychur
Model Typeinternlm
Model Files  9.8 GB: 1-of-4   9.8 GB: 2-of-4   9.8 GB: 3-of-4   1.5 GB: 4-of-4
Supported Languagesen zh ja fr ru es
Model ArchitectureInternLM2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.28.1
Is Biased0
Tokenizer ClassInternLMTokenizer
Padding Token</s>
Vocabulary Size92544
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than Henrychur/MMedLM2.

Rank the MMedLM2 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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