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

Libraries

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

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

pipe = pipeline("text-generation", model="cs-552-2026-catma/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-catma/multilingual_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-catma/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-catma/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-catma/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-catma/multilingual_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

multilingual_model

Commit History

Automated MNLP evaluation report (2026-05-21)

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Automated MNLP evaluation report (2026-05-20) (#6)

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zechen-nlp commited on May 20

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cristiano-masutti

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Upload tokenizer

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Upload Qwen3ForCausalLM

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mariem38

zechen-nlp commited on May 16

Automated MNLP evaluation report (2026-05-13) (#2)

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cristiano-masutti

zechen-nlp commited on May 15

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TuanNguyen2003 commited on May 14

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initial commit

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Commit History

Automated MNLP evaluation report (2026-05-21) a8430f2 verified

Upload tokenizer 245d30f verified

Upload Qwen3ForCausalLM 5b544ac verified

Automated MNLP evaluation report (2026-05-20) (#6) 748edb1

Upload folder using huggingface_hub 016b7f5 verified

Automated MNLP evaluation report (2026-05-19) (#5) 2243fb5

Automated MNLP evaluation report (2026-05-17) (#4) dfd7172

Upload tokenizer aa74e02 verified

Upload Qwen3ForCausalLM 4e2ba90 verified

Automated MNLP evaluation report (2026-05-16) (#3) f031775

Automated MNLP evaluation report (2026-05-13) (#2) 9a45bbc

Upload tokenizer 131adac verified

Upload Qwen3ForCausalLM 0417c60 verified

Automated MNLP evaluation report (2026-05-13) (#1) a528993

Upload folder using huggingface_hub c4ae390 verified

initial commit aea74ed verified

Automated MNLP evaluation report (2026-05-21)

a8430f2
verified

Upload tokenizer

245d30f
verified

Upload Qwen3ForCausalLM

5b544ac
verified

Automated MNLP evaluation report (2026-05-20) (#6)

748edb1

Upload folder using huggingface_hub

016b7f5
verified

Automated MNLP evaluation report (2026-05-19) (#5)

2243fb5

Automated MNLP evaluation report (2026-05-17) (#4)

dfd7172

Upload tokenizer

aa74e02
verified

Upload Qwen3ForCausalLM

4e2ba90
verified

Automated MNLP evaluation report (2026-05-16) (#3)

f031775

Automated MNLP evaluation report (2026-05-13) (#2)

9a45bbc

Upload tokenizer

131adac
verified

Upload Qwen3ForCausalLM

0417c60
verified

Automated MNLP evaluation report (2026-05-13) (#1)

a528993

Upload folder using huggingface_hub

c4ae390
verified

initial commit

aea74ed
verified