Instructions to use turkerberkdonmez/TUSGPT-TR-Medical-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("turkerberkdonmez/TUSGPT-TR-Medical-9B")
model = AutoModelForCausalLM.from_pretrained("turkerberkdonmez/TUSGPT-TR-Medical-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("turkerberkdonmez/TUSGPT-TR-Medical-9B")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="turkerberkdonmez/TUSGPT-TR-Medical-9B",
	filename="TUSGPT-TR-Medical-9B-Q4_K_M.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf turkerberkdonmez/TUSGPT-TR-Medical-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 turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf turkerberkdonmez/TUSGPT-TR-Medical-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 turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf turkerberkdonmez/TUSGPT-TR-Medical-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 turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M

Use Docker

docker model run hf.co/turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M

LM Studio
Jan

vLLM

How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "turkerberkdonmez/TUSGPT-TR-Medical-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": "turkerberkdonmez/TUSGPT-TR-Medical-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M

SGLang

How to use turkerberkdonmez/TUSGPT-TR-Medical-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 "turkerberkdonmez/TUSGPT-TR-Medical-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": "turkerberkdonmez/TUSGPT-TR-Medical-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 "turkerberkdonmez/TUSGPT-TR-Medical-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": "turkerberkdonmez/TUSGPT-TR-Medical-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with Ollama:
```
ollama run hf.co/turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M
```

Unsloth Studio

How to use turkerberkdonmez/TUSGPT-TR-Medical-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 turkerberkdonmez/TUSGPT-TR-Medical-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 turkerberkdonmez/TUSGPT-TR-Medical-9B to start chatting

Using HuggingFace Spaces for Unsloth

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

MLX LM

How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "turkerberkdonmez/TUSGPT-TR-Medical-9B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "turkerberkdonmez/TUSGPT-TR-Medical-9B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "turkerberkdonmez/TUSGPT-TR-Medical-9B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with Docker Model Runner:
```
docker model run hf.co/turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M
```

Lemonade

How to use turkerberkdonmez/TUSGPT-TR-Medical-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull turkerberkdonmez/TUSGPT-TR-Medical-9B:Q4_K_M

Run and chat with the model

lemonade run user.TUSGPT-TR-Medical-9B-Q4_K_M

List all available models

lemonade list

⚕️ TUSGPT-TR-Medical-9B

Türkiye'nin İlk Açık Kaynak Türkçe Medikal Dil Modeli Turkey's First Open-Source Turkish Medical Language Model

🇹🇷 Türkçe Açıklama | 🇬🇧 English Description | 💻 Kullanım/Usage

🇹🇷 Model Hakkında

TUSGPT-TR-Medical-9B, Türkiye'nin medikal alandaki yapay zeka gelişimine katkı sağlamak amacıyla geliştirilmiş, Gemma-2 mimarisine dayalı 9 milyar parametreli bir dil modelidir.

Model, ytu-ce-cosmos/Turkish-Gemma-9b-T1 temel modeli üzerine, 55.000'den fazla yüksek kaliteli Türkçe tıbbi soru-cevap çifti ile 2 aşamalı DoRA (Weight-Decomposed Low-Rank Adaptation) yöntemi kullanılarak fine-tune edilmiştir.

📚 Veri Seti Kapsamı (Dataset Coverage)

Model, aşağıdaki branşları ve daha fazlasını kapsayan 55,465 Türkçe soru-cevap çifti ile eğitilmiştir:

Temel Bilimler: Farmakoloji, Patoloji, Anatomi, Fizyoloji
Klinik Bilimler: Dahiliye, Cerrahi, Pediatri, Kadın Doğum
Diğer: Acil Tıp, Nöroloji, Onkoloji, Radyoloji

🇬🇧 Model Description

TUSGPT-TR-Medical-9B is a specialized 9-billion parameter language model based on the Gemma-2 architecture, designed to advance medical AI research in Turkey.

It is fine-tuned on the ytu-ce-cosmos/Turkish-Gemma-9b-T1 base model using 55,000+ high-quality Turkish medical Q&A pairs, with a 2-Stage DoRA methodology trained on Apple Silicon hardware.

📚 Dataset Scope

The model covers a wide range of medical disciplines with 55,465 Q&A pairs, including:

Basic Sciences: Pharmacology, Pathology, Anatomy, Physiology
Clinical Sciences: Internal Medicine, Surgery, Pediatrics, Obstetrics & Gynecology
Others: Emergency Medicine, Neurology, Oncology, Radiology

📊 Teknik Detaylar / Technical Details

Özellik / Feature	Detay / Detail
Base Model	ytu-ce-cosmos/Turkish-Gemma-9b-T1
Architecture	Gemma 2 (9.24B Parameters)
Dataset	turkerberkdonmez/TUSGPT-TR-Medical-Dataset-v1
Dataset Size	55,465 samples (Q&A)
Training Method	2-Stage DoRA (Weight-Decomposed LoRA)
Precision	bfloat16
Hardware	Apple Mac Studio (M-Series, 128GB Unified Memory)

🔬 Eğitim Parametrelerini Görüntüle / View Training Hyperparameters

Stage 1 — Aggressive Knowledge Injection

DoRA: rank=64, alpha=128, target_modules=all linear layers
Optimizer: NEFTune (alpha=3) enabled
Learning Rate: 2e-5 → 2e-6 (cosine decay)
Steps: 1600 iterations (~1 epoch)

Stage 2 — Stabilization

Config: Resumed from Stage 1 best checkpoint
Optimizer: NEFTune disabled
Learning Rate: 5e-6 → 1e-7
Steps: 1000 iterations
Final Val Loss: 1.126

💻 Kullanım / Usage

🐍 Python (Transformers)

Generation önerisi: Temperature=0.6, TopP=0.95, TopK=20, MinP=0 (generation_config.json varsayılanı).
Greedy decoding kullanmayın; performans düşüşüne ve sonsuz tekrarlara yol açabilir.
Complex tasks: max_new_tokens değerini artırın. Gerekirse repetition_penalty ve presence_penalty (0–2) ayarlanabilir.
Not: Daha yüksek değerler bazen dil karışmasına ve hafif performans düşüşüne neden olabilir.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "turkerberkdonmez/TUSGPT-TR-Medical-9B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "Sen tıp alanında uzmanlaşmış, Türkçe yanıt veren bir yapay zeka asistanısın. Soruları doğru, kapsamlı ve anlaşılır biçimde yanıtla."},
    {"role": "user", "content": "Akut miyokard enfarktüsünün erken belirtileri nelerdir?"},
]

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

outputs = model.generate(
    **inputs,
    max_new_tokens=512,     # complex tasks için artırılabilir
    temperature=0.6,
    top_p=0.95,
    top_k=20,
    # min_p Transformers'ta her zaman desteklenmeyebilir; model config'ine bağlıdır.
    # Aşağıdakiler opsiyonel: tekrarları azaltmak için
    # repetition_penalty=1.15,
    # presence_penalty=0.3,
    do_sample=True,         # greedy decoding kapalı
)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

🦙 GGUF (Ollama & LM Studio)

Bu modelin sıkıştırılmış (quantized) versiyonları yerel cihazlarda çalıştırılabilir.

Dosya Adı (Filename)	Quant	Boyut (Size)	Önerilen Donanım (Recommended HW)
`TUSGPT-TR-Medical-9B-Q8_0.gguf`	Q8_0	~9.8 GB	12GB+ VRAM / 16GB+ RAM
`TUSGPT-TR-Medical-9B-Q4_K_M.gguf`	Q4_K_M	~5.8 GB	8GB+ VRAM / 12GB+ RAM

Ollama Setup

Önerilen üretim ayarları: Temperature=0.6, TopP=0.95, TopK=20, MinP=0
Greedy decoding kullanmayın; performans düşüşü ve sonsuz tekrar riski yaratabilir.
Complex tasks: num_predict (max_new_tokens) artırılabilir.
Tekrarlar olursa repeat_penalty ve presence_penalty (0–2) ayarlanabilir (yüksek değerler bazen dil karışmasına ve hafif performans düşüşüne yol açabilir).

Modelfile Oluşturun / Create Modelfile:

FROM ./TUSGPT-TR-Medical-9B-Q4_K_M.gguf

SYSTEM "Sen tıp alanında uzmanlaşmış, Türkçe yanıt veren bir yapay zeka asistanısın. Soruları doğru, kapsamlı ve anlaşılır biçimde yanıtla."

# Recommended generation (DO NOT use greedy decoding)
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER min_p 0

# Complex tasks: increase num_predict
# PARAMETER num_predict 1024

# To reduce endless repetitions (optional; tune gradually)
# PARAMETER repeat_penalty 1.15
# PARAMETER presence_penalty 0.3

Modeli Çalıştırın / Run Model:

ollama create tusgpt-medical -f Modelfile
ollama run tusgpt-medical

⚠️ Yasal Uyarı / Disclaimer

🇹🇷 Türkçe

Bu model eğitim ve araştırma amaçlıdır. Klinik karar verme süreçlerinde tek başına kullanılmamalıdır. Tıbbi kararlar için her zaman uzman hekime danışın.

🇬🇧 English

This model is for educational and research purposes only. It should not be used as a sole source for clinical decision-making. Always consult a qualified physician for medical decisions.

🤝 Acknowledgments

Base Model: YTU CE COSMOS Lab — Turkish-Gemma-9b-T1
Training Framework: MLX by Apple

📝 Citation

@misc{tusgpt-tr-medical-9b,
  title        = {TUSGPT-TR-Medical-9B: Turkish Medical Language Model},
  author       = {Türker Berk Dönmez},
  year         = {2026},
  url          = {https://huggingface.co/turkerberkdonmez/TUSGPT-TR-Medical-9B}
}

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Safetensors

Model size

9B params

Tensor type

BF16

F32

MLX

Hardware compatibility

Quantized

Model tree for turkerberkdonmez/TUSGPT-TR-Medical-9B

Base model

ytu-ce-cosmos/Turkish-Gemma-9b-T1