Instructions to use newmindai/Mecellem-Qwen3-1.7B-TR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use newmindai/Mecellem-Qwen3-1.7B-TR with Transformers:

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

pipe = pipeline("text-generation", model="newmindai/Mecellem-Qwen3-1.7B-TR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
model = AutoModelForMultimodalLM.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
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 newmindai/Mecellem-Qwen3-1.7B-TR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "newmindai/Mecellem-Qwen3-1.7B-TR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "newmindai/Mecellem-Qwen3-1.7B-TR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/newmindai/Mecellem-Qwen3-1.7B-TR

SGLang

How to use newmindai/Mecellem-Qwen3-1.7B-TR 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 "newmindai/Mecellem-Qwen3-1.7B-TR" \
    --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": "newmindai/Mecellem-Qwen3-1.7B-TR",
		"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 "newmindai/Mecellem-Qwen3-1.7B-TR" \
        --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": "newmindai/Mecellem-Qwen3-1.7B-TR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use newmindai/Mecellem-Qwen3-1.7B-TR with Docker Model Runner:
```
docker model run hf.co/newmindai/Mecellem-Qwen3-1.7B-TR
```

Add library_name and links to paper and GitHub

by nielsr HF Staff - opened Jan 27

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-6

Add library_name and links to paper and GitHubb192c7d7

nielsr

Jan 27

Hi! I'm Niels from the Hugging Face community science team.

This PR improves the model card by:

Adding the library_name: transformers metadata tag. This allows the Hub to recognize the model's compatibility and display the "Use in Transformers" button with appropriate code snippets.
Including direct links to the official GitHub repository and the research paper in the introduction for easier access.

The model is clearly compatible with the transformers library as evidenced by the usage examples provided in your README.

nmmursit changed pull request status to merged Jan 27

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