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Sum Small by omi-health

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Sum Small is an open-source language model by omi-health. Features: 3.8b LLM, VRAM: 7.7GB, Context: 4K, License: mit, Instruction-Based, LLM Explorer Score: 0.14.

  Conversational   Custom code Dataset:omi-health/medical-dia...   En   Endpoints compatible   Instruct   Phi3   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/omi-health/sum-small 

Sum Small Benchmarks

LLME Score: 0.14256
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Sum Small (omi-health/sum-small)
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Sum Small Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, AI-powered medical documentation
Primary Use Cases:
generation of SOAP summaries from medical dialogues
Limitations:
Synthetic data, not for direct clinical use
Considerations:
Integration with additional safety guardrails needed.
Supported Languages 
en (full-proficiency)
Training Details 
Data Sources:
Omi Health/medical-dialogue-to-soap-summary
Data Volume:
10,000 dialogues
Methodology:
fine-tuning
Hardware Used:
NVIDIA A100 GPUs
Responsible Ai Considerations 
Fairness:
Users are urged to consider ethical implications in healthcare use.
Transparency:
Model's synthetic training data composition.
Accountability:
Not directly accountable without safety validation.
LLM NameSum Small
Repository 🤗https://huggingface.co/omi-health/sum-small 
Model Size3.8b
Required VRAM7.7 GB
Updated2026-03-31
Maintaineromi-health
Model Typephi3
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   2.7 GB: 2-of-2
Supported Languagesen
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.41.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than omi-health/sum-small.

Rank the Sum Small 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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