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OncogenAI/
phi-3-medium-clinical

Name: phi-3-medium-clinical
Rating: 2.5 (12 reviews)

microsoft/Phi-3-medium-4k-instruct

finetune

apache-2.0

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Phi-3-Medium-Clinical

A medical domain-specific fine-tuned variant of Microsoft's Phi-3-Medium language model, optimized for clinical applications and healthcare-related tasks.

Model Details

Base Model

Model Name: microsoft/Phi-3-medium-4k-instruct
Parameter Count: 14B parameters
Architecture: Transformer-based language model
Training Data: Pre-trained on diverse web-scale data with strong reasoning and instruction-following capabilities

Fine-tuning Information

Fine-tuning Framework: Unsloth - High-efficiency fine-tuning toolkit
Medical Dataset: Clinical and healthcare-specific dataset
Optimization Method: LoRA (Low-Rank Adaptation) via Unsloth
Training Date: March 2026

Model Description

This model adapts Phi-3-medium-4k-instruct for clinical and healthcare applications by fine-tuning on curated medical datasets. The specialization enables better performance on:

Clinical note analysis and generation
Medical question answering
Healthcare terminology understanding
Patient information processing (de-identified)
Medical literature comprehension

Intended Use

Primary Use Cases

Clinical decision support and information retrieval
Medical text analysis and classification
Healthcare documentation assistance
Medical knowledge base queries
Educational purposes in healthcare settings

Limitations

Designed for informational and educational purposes only
Should not be used as a standalone clinical decision-making tool
Requires human medical professional validation
Performance may vary on specialized subspecialties
De-identified datasets only - respects patient privacy regulations

Training Details

Framework & Libraries

- Base: microsoft/Phi-3-medium-4k-instruct
- Fine-tuning: Unsloth
- LoRA Implementation: Efficient parameter-efficient fine-tuning
- Hardware: GPU-based training

Dataset Characteristics

Domain: Medical and clinical
Focus: Healthcare-specific terminology and concepts
Processing: Data preprocessing and tokenization following Phi-3 specifications
Format: Instruction-following format for improved generalization

Training Parameters

Max Sequence Length: Phi-3-medium-4k-instruct standard (typically 4096 tokens)
Optimization: LoRA with rank adaptation for parameter efficiency
Batch Size: Standard configuration for medical dataset fine-tuning
Evaluation: Domain-specific metrics for medical accuracy

Usage

Installation

bash

# Install required dependencies
pip install transformers torch unsloth

# Optional: Install medical evaluation tools
pip install -r requirements.txt

Basic Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "OncogenAI/phi-3-medium-clinical"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16,
)

# Prepare input
prompt = "Clinical Query: Explain the pathophysiology of type 2 diabetes."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate response
outputs = model.generate(
    **inputs,
    max_length=512,
    temperature=0.7,
    top_p=0.95,
    do_sample=True,
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Advanced Usage with Unsloth

python

from unsloth import FastLanguageModel
from transformers import TextIteratorStreamer
from threading import Thread

# Fast loading with Unsloth
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="OncogenAI/phi-3-medium-clinical",
    max_seq_length=4096,
    dtype=torch.float16,
    load_in_4bit=True,
)

# Streaming inference for long responses
streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
generation_kwargs = dict(
    inputs=inputs,
    streamer=streamer,
    max_length=512,
    temperature=0.7,
)

thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()

for text in streamer:
    print(text, end="", flush=True)

Model Architecture

Attention Mechanism: Multi-head self-attention
Positional Encoding: Rotary positional embeddings (RoPE)
Normalization: RMSNorm
Activation: SiLU (Swish)
Fine-tuning Method: LoRA adapters via Unsloth

Performance Characteristics

Inference Speed: Optimized via Unsloth for reduced latency
Memory Footprint: Efficient through LoRA parameter reduction
Medical Domain Accuracy: Enhanced through domain-specific fine-tuning
Token Context: Maintains Phi-3-Medium's context window

Hardware Requirements

Recommended Specifications

GPU Memory: 8GB+ (supports quantized inference)
CPU RAM: 16GB+ (for tokenization and preprocessing)
Storage: 15GB+ (model weights and tokenizer)

Inference Modes

Full Precision: 14B parameters (~28GB VRAM)
FP16 (Half Precision): ~14GB VRAM
INT8 Quantization: ~8GB VRAM
INT4 Quantization: ~4GB VRAM

Ethical Considerations

Responsible Use

This model should complement, not replace, professional medical judgment
Always have qualified healthcare professionals review outputs
Be transparent about AI usage in clinical contexts
Maintain patient confidentiality and data security
Comply with healthcare regulations (HIPAA, GDPR, etc.)

Bias and Fairness

Fine-tuning dataset undergoes bias assessment
Model outputs may reflect biases in training data
Users should evaluate context-specific performance
Continuous monitoring recommended for production use

License

[Specify your license - e.g., Apache 2.0, MIT, or custom license]

Citation

If you use this model in research, please cite:

bibtex

@software{phi3_clinical_2026,
  author = {OncogenAI},
  title = {Phi-3-Medium-Clinical: A Medical Domain-Specific Fine-Tuned Model},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/OncogenAI/phi-3-medium-clinical}}
}

Also cite the base model and Unsloth:

bibtex

@article{abdin2024phi,
  title={Phi-3 Technical Report},
  author={Abdin, Marah and others},
  journal={arXiv preprint arXiv:2404.14219},
  year={2024}
}

@software{unsloth2024,
  title={Unsloth: Efficient Fine-tuning of Language Models},
  author={Peng, Daniel Han and others},
  year={2024},
  url={https://github.com/unslothai/unsloth}
}

Limitations and Disclaimers

Medical Accuracy: While fine-tuned on medical datasets, this model is not a substitute for professional medical advice
Knowledge Cutoff: Performance limited to training data scope
Specialized Domains: May have limited performance on rare conditions or cutting-edge treatments
Privacy: Ensure input data is properly de-identified before use
Regulatory Compliance: Users are responsible for compliance with applicable healthcare regulations

Contact & Support

For questions about this model:

Open an issue on the Hugging Face model page
Reference specific queries or behaviors for reproducibility
Provide context about your use case

Acknowledgments

Microsoft for the Phi-3-Medium base model
Unsloth team for the efficient fine-tuning framework
Medical dataset contributors and curators
Healthcare and AI safety researchers