balastmed-4B

🏥 BalastMed-4B — Local Medical Assistant for Clinicians

A fine-tuned version of Qwen/Qwen3.5-4B designed to run fully locally as a clinical decision support assistant for doctors and healthcare professionals.

Specialized in emergency triage, ESI scoring, differential diagnosis, and medical situation management — without sending any patient data to external servers.

⚠️ Disclaimer: This model is for research and clinical support purposes only. It is NOT a substitute for professional medical judgment. Final decisions always rest with licensed medical professionals.

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🎯 Model Overview

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📊 Evaluation Results

MedQA tests clinical reasoning across USMLE-style multiple choice questions covering diagnosis, treatment, and medical knowledge.

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🧠 Training Details

• Method: LoRA fine-tuning + full SFT for clinical thinking pipeline re-training
• Base Model: Qwen/Qwen3.5-4B
• Hardware: 1× NVIDIA A100 40GB
• Training Data: Proprietary clinical dataset (not publicly available)
• Thinking Pipeline: The model's reasoning chain was completely re-trained via SFT to follow structured clinical logic — differentials, missing data identification, emergency flagging
• Focus Areas:
  • ESI (Emergency Severity Index) levels 1–5
  • Symptom assessment and chief complaint classification
  • Differential diagnosis support
  • Medical situation management for clinical staff

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💬 Recommended System Prompt

You are a clinical medical assistant. Think through clinical reasoning, consider differentials, identify what data is missing, and flag emergencies. State uncertainty when evidence is insufficient. Defer final decisions to clinicians.

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⚙️ Recommended Parameters

Tip: Use top_k: 60 when exploring broad differentials or rare presentations. Use top_k: 20–40 when you need a clear, direct clinical answer. The thinking pipeline produces higher quality output when max_new_tokens is set generously (≥1024).

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🚀 Quick Start

With Ollama (Recommended for local use)

ollama run hf.co/balastml/balastmed-4B:Q4_K_M

With llama.cpp

brew install llama.cpp
llama-server -hf balastml/balastmed-4B:Q4_K_M

With LM Studio

Search for balastml/balastmed-4B in LM Studio's model browser and download the Q4_K_M variant.

With Python (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "balastml/balastmed-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

system_prompt = "You are a clinical medical assistant. Think through clinical reasoning, consider differentials, identify what data is missing, and flag emergencies. State uncertainty when evidence is insufficient. Defer final decisions to clinicians."

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "58yo male, crushing chest pain radiating to left arm, diaphoresis, BP 90/60. ESI level and immediate actions?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.72,
    top_p=0.94,
    top_k=40,
    repetition_penalty=1.08,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🩺 Example Use Cases

Emergency Triage:

22yo female, sudden onset severe dyspnea, SpO2 82%, stridor present.
→ ESI level and initial management?

Differential Diagnosis:

45yo male, 3-week history of progressive fatigue, night sweats,
unintentional 8kg weight loss, palpable cervical lymphadenopathy.
→ Top differentials and recommended workup?

Medical Situation Management:

ICU patient, post-op day 2 after bowel resection. Sudden fever 39.8°C,
HR 118, BP dropping to 88/55, rising lactate. Current antibiotics: piperacillin-tazobactam.
→ Assessment and management priorities?

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🔒 Privacy & Local Deployment

BalastMed-4B is designed for fully offline, local deployment. No patient data is sent to external servers. This makes it suitable for:

• Hospital internal networks
• Clinics with strict data privacy requirements
• GDPR / HIPAA-conscious environments (with appropriate institutional validation)

Minimum hardware for local use: 8GB RAM (Q4_K_M quantization, ~2.78 GB)

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⚠️ Limitations

• Not validated for autonomous clinical deployment — requires physician oversight
• Trained primarily on English-language clinical data
• Training dataset is proprietary and not available for public inspection
• Performance may vary on highly specialized sub-specialties
• Should be used only by or under supervision of licensed medical professionals

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🔗 Related Models

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📬 Contact & Feedback

For questions, collaborations, or clinical feedback, open a discussion on the Community tab.