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
```

Mecellem-Qwen3-1.7B-TR

Commit History

Add library_name and links to paper and GitHub (#1)

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nmmursit

nielsr HF Staff commited on Jan 27

Update README.md

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nmmursit commited on Jan 23

Initial model upload - clean repository

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nmmursit commited on Jan 16

Initial model upload - clean repository

3bcd753
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nmmursit commited on Jan 16

initial commit

c398a5d
verified

nmmursit commited on Jan 16

Commit History

Add library_name and links to paper and GitHub (#1) 107c316 verified

Update README.md 2ed2e5d verified

Initial model upload - clean repository 0181ff8 verified

Initial model upload - clean repository 3bcd753 verified

initial commit c398a5d verified

Add library_name and links to paper and GitHub (#1)

107c316
verified

Update README.md

2ed2e5d
verified

Initial model upload - clean repository

0181ff8
verified

Initial model upload - clean repository

3bcd753
verified

initial commit

c398a5d
verified