Instructions to use malhajar/Mistral-7B-v0.2-meditron-turkish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use malhajar/Mistral-7B-v0.2-meditron-turkish with Transformers:

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

pipe = pipeline("text-generation", model="malhajar/Mistral-7B-v0.2-meditron-turkish")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("malhajar/Mistral-7B-v0.2-meditron-turkish")
model = AutoModelForMultimodalLM.from_pretrained("malhajar/Mistral-7B-v0.2-meditron-turkish")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use malhajar/Mistral-7B-v0.2-meditron-turkish with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "malhajar/Mistral-7B-v0.2-meditron-turkish"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "malhajar/Mistral-7B-v0.2-meditron-turkish",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/malhajar/Mistral-7B-v0.2-meditron-turkish

SGLang

How to use malhajar/Mistral-7B-v0.2-meditron-turkish 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 "malhajar/Mistral-7B-v0.2-meditron-turkish" \
    --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": "malhajar/Mistral-7B-v0.2-meditron-turkish",
		"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 "malhajar/Mistral-7B-v0.2-meditron-turkish" \
        --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": "malhajar/Mistral-7B-v0.2-meditron-turkish",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use malhajar/Mistral-7B-v0.2-meditron-turkish with Docker Model Runner:
```
docker model run hf.co/malhajar/Mistral-7B-v0.2-meditron-turkish
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

Mistral-7B-v0.2-meditron-turkish is a finetuned Mistral Model version using Freeze technique on Turkish Meditron dataset of malhajar/meditron-7b-tr using SFT Training. This model can answer information about different excplicit ideas in medicine in Turkish and English

Model Description

Finetuned by: Mohamad Alhajar
Language(s) (NLP): Turkish,English
Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2

Prompt Template For Turkish Generation

### Kullancı:

Prompt Template For English Generation

### User:

How to Get Started with the Model

Use the code sample provided in the original post to interact with the model.

from transformers import AutoTokenizer,AutoModelForCausalLM
 
model_id = "malhajar/Mistral-7B-v0.2-meditron-turkish"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             torch_dtype=torch.float16,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_id)

question: "Akciğer kanseri nedir?"
# For generating a response
prompt = '''
### Kullancı:
{question} 
'''
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,
        top_p=0.95)
response = tokenizer.decode(output[0])

print(response)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	63.34
AI2 Reasoning Challenge (25-Shot)	59.56
HellaSwag (10-Shot)	81.79
MMLU (5-Shot)	60.35
TruthfulQA (0-shot)	66.19
Winogrande (5-shot)	76.24
GSM8k (5-shot)	35.94