Instructions to use balastml/balastmed-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use balastml/balastmed-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="balastml/balastmed-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("balastml/balastmed-9B", dtype="auto")

llama-cpp-python

How to use balastml/balastmed-9B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="balastml/balastmed-9B",
	filename="balastmed-9b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use balastml/balastmed-9B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf balastml/balastmed-9B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf balastml/balastmed-9B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf balastml/balastmed-9B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf balastml/balastmed-9B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf balastml/balastmed-9B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf balastml/balastmed-9B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf balastml/balastmed-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf balastml/balastmed-9B:Q4_K_M

Use Docker

docker model run hf.co/balastml/balastmed-9B:Q4_K_M

LM Studio
Jan

vLLM

How to use balastml/balastmed-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "balastml/balastmed-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "balastml/balastmed-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/balastml/balastmed-9B:Q4_K_M

SGLang

How to use balastml/balastmed-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "balastml/balastmed-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "balastml/balastmed-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "balastml/balastmed-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "balastml/balastmed-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use balastml/balastmed-9B with Ollama:
```
ollama run hf.co/balastml/balastmed-9B:Q4_K_M
```

Unsloth Studio

How to use balastml/balastmed-9B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for balastml/balastmed-9B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for balastml/balastmed-9B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for balastml/balastmed-9B to start chatting

How to use balastml/balastmed-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf balastml/balastmed-9B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "balastml/balastmed-9B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use balastml/balastmed-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf balastml/balastmed-9B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default balastml/balastmed-9B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use balastml/balastmed-9B with Docker Model Runner:
```
docker model run hf.co/balastml/balastmed-9B:Q4_K_M
```

Lemonade

How to use balastml/balastmed-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull balastml/balastmed-9B:Q4_K_M

Run and chat with the model

lemonade run user.balastmed-9B-Q4_K_M

List all available models

lemonade list

🏥 BalastMed-9B — Bilingual Local Medical Assistant (EN / TR)

A fine-tuned version of Qwen/Qwen3.5-9B 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.

BalastMed-9B introduces full Turkish clinical language support, a clinically re-trained thinking pipeline via SFT, and significantly stronger benchmark performance over its predecessor.

⚠️ 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.

🆕 What's New in 9B

Feature	BalastMed-4B	BalastMed-9B
Base Model	Qwen3.5-4B	Qwen3.5-9B
Turkish Clinical Support	❌	✅ Full bilingual (EN/TR)
Thinking Pipeline	Clinically re-trained via SFT	Clinically re-trained via SFT (enhanced)
MedQA Score	77.6%	88.2%
Parameters	~4B	~9B

🎯 Model Overview

Property	Value
Base Model	Qwen/Qwen3.5-9B
Fine-tuning Method	LoRA + SFT (Thinking pipeline re-training)
Task	Medical Triage / Clinical Decision Support
Languages	English & Turkish
License	CC-BY-NC 4.0
Parameters	~9B
Quantization	Q4_K_M (GGUF)

📊 Evaluation Results

Benchmark	Score
MedQA (USMLE-style)	88.2%

MedQA tests clinical reasoning across USMLE-style multiple choice questions covering diagnosis, treatment, and medical knowledge. For reference: BalastMed-9B performs comparably to DeepSeek V4 Flash (no thinking) among open-weights clinical models.

🧠 Training Details

Method: LoRA fine-tuning + full SFT for clinical thinking pipeline re-training
Base Model: Qwen/Qwen3.5-9B
Hardware: 1× NVIDIA A100 80GB
Training Data: Proprietary bilingual 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
- Full Turkish clinical language support

💬 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.

The model responds in the same language as the clinician. Send queries in Turkish and it will assess, reason, and respond fully in Turkish — no separate prompt needed.

⚙️ Recommended Parameters

Parameter	Value	Notes
`temperature`	`0.72`	Balanced between consistency and nuanced clinical reasoning
`top_p`	`0.94`	Wide token probability coverage
`top_k`	`60`	For rare conditions and broader differential evaluation
`top_k`	`20–40`	For focused, high-confidence diagnosis
`repetition_penalty`	`1.08`	Prevents output looping without over-constraining
`max_new_tokens`	`512–2048`	Higher range recommended for thinking mode

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).

🚀 Quick Start

With Ollama (Recommended for local use)

ollama run hf.co/balastml/balastmed-9B:Q4_K_M

With llama.cpp

brew install llama.cpp
llama-server -hf balastml/balastmed-9B:Q4_K_M

With LM Studio

Search for balastml/balastmed-9B 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-9B"
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))

🩺 Example Use Cases

Emergency Triage (English):

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

Acil Triaj (Türkçe):

22 yaşında kadın hasta, ani başlayan ciddi nefes darlığı, SpO2 %82, stridor mevcut.
→ ESI düzeyi ve ilk müdahale adımları?

Differential Diagnosis:

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

Klinik Durum Yönetimi (Türkçe):

Bağırsak rezeksiyonu sonrası 2. gün YBÜ hastası. Ani ateş 39.8°C,
KH 118, KB 88/55'e düşüyor, yükselen laktat. Mevcut antibiyotik: pip-taz.
→ Değerlendirme ve yönetim öncelikleri?

🔒 Privacy & Local Deployment

BalastMed-9B 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 / KVKK / HIPAA-conscious environments (with appropriate institutional validation)
Turkish healthcare institutions requiring native-language clinical AI

Minimum hardware for local use: 8GB VRAM (Q4_K_M quantization) Recommended: 12GB+ VRAM for full thinking pipeline performance

⚠️ Limitations

Not validated for autonomous clinical deployment — requires physician oversight
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

🔗 Related Models

Model	MedQA	Languages	Notes
BalastMed-4B	77.6%	EN	Previous version
BalastMed-9B	88.2%	EN + TR	This model

📬 Contact & Feedback

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

Downloads last month: 24

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for balastml/balastmed-9B

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(260)

this model