Instructions to use cs-552-2026-the-transformers/multilingual_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cs-552-2026-the-transformers/multilingual_model with Transformers:

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

pipe = pipeline("text-generation", model="cs-552-2026-the-transformers/multilingual_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-the-transformers/multilingual_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-the-transformers/multilingual_model", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cs-552-2026-the-transformers/multilingual_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cs-552-2026-the-transformers/multilingual_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cs-552-2026-the-transformers/multilingual_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cs-552-2026-the-transformers/multilingual_model

SGLang

How to use cs-552-2026-the-transformers/multilingual_model 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 "cs-552-2026-the-transformers/multilingual_model" \
    --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": "cs-552-2026-the-transformers/multilingual_model",
		"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 "cs-552-2026-the-transformers/multilingual_model" \
        --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": "cs-552-2026-the-transformers/multilingual_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cs-552-2026-the-transformers/multilingual_model with Docker Model Runner:
```
docker model run hf.co/cs-552-2026-the-transformers/multilingual_model
```

multilingual_model

Commit History

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Automated MNLP evaluation report (2026-06-08) (#3)

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NicolasBerlin

zechen-nlp commited on Jun 8

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NicolasBerlin

zechen-nlp commited on May 27

Automated MNLP evaluation report (2026-05-23) (#1)

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NicolasBerlin

zechen-nlp commited on May 23

Upload multilingual SFT v3 checkpoint

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Commit History

Upload multilingual SFT v3 checkpoint 39ab0f2 verified

Upload multilingual SFT v3 checkpoint 7c830d3 verified

Upload multilingual SFT v3 checkpoint 62c7cfe verified

Automated MNLP evaluation report (2026-06-08) (#3) 7f37bb9

Upload multilingual SFT v3 checkpoint 98f021b verified

Upload multilingual SFT v3 checkpoint 6c0cb50 verified

Upload multilingual SFT v3 checkpoint 905edcd verified

Upload multilingual SFT v3 checkpoint 6248a19 verified

Upload multilingual SFT v3 checkpoint 217e578 verified

Automated MNLP evaluation report (2026-05-27) (#2) 4e9ce85

Automated MNLP evaluation report (2026-05-23) (#1) 1924b9c

Upload multilingual SFT v3 checkpoint ccfb88c verified

Upload multilingual SFT v3 checkpoint bafac18 verified

Upload multilingual SFT v3 checkpoint cd40da3 verified

Upload multilingual SFT v3 checkpoint c45b781 verified

Upload math SFT checkpoint (smoke test) e892017 verified

initial commit 00d1d41 verified

Upload multilingual SFT v3 checkpoint

39ab0f2
verified

Upload multilingual SFT v3 checkpoint

7c830d3
verified

Upload multilingual SFT v3 checkpoint

62c7cfe
verified

Automated MNLP evaluation report (2026-06-08) (#3)

7f37bb9

Upload multilingual SFT v3 checkpoint

98f021b
verified

Upload multilingual SFT v3 checkpoint

6c0cb50
verified

Upload multilingual SFT v3 checkpoint

905edcd
verified

Upload multilingual SFT v3 checkpoint

6248a19
verified

Upload multilingual SFT v3 checkpoint

217e578
verified

Automated MNLP evaluation report (2026-05-27) (#2)

4e9ce85

Automated MNLP evaluation report (2026-05-23) (#1)

1924b9c

Upload multilingual SFT v3 checkpoint

ccfb88c
verified

Upload multilingual SFT v3 checkpoint

bafac18
verified

Upload multilingual SFT v3 checkpoint

cd40da3
verified

Upload multilingual SFT v3 checkpoint

c45b781
verified

Upload math SFT checkpoint (smoke test)

e892017
verified

initial commit

00d1d41
verified