Instructions to use norallm/normistral-11b-translate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use norallm/normistral-11b-translate with Transformers:

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

pipe = pipeline("text-generation", model="norallm/normistral-11b-translate")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("norallm/normistral-11b-translate")
model = AutoModelForCausalLM.from_pretrained("norallm/normistral-11b-translate")
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

vLLM

How to use norallm/normistral-11b-translate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "norallm/normistral-11b-translate"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "norallm/normistral-11b-translate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/norallm/normistral-11b-translate

SGLang

How to use norallm/normistral-11b-translate 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 "norallm/normistral-11b-translate" \
    --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": "norallm/normistral-11b-translate",
		"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 "norallm/normistral-11b-translate" \
        --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": "norallm/normistral-11b-translate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use norallm/normistral-11b-translate with Docker Model Runner:
```
docker model run hf.co/norallm/normistral-11b-translate
```

NorMistral-11b-translate is a finetuned machine-translation version of NorMistral-11b-long. It can translate sentences or documents in all 6 directions between Norwegian Bokmål, Nynorsk and English.

License

We release the model under Apache 2.0 license to indicate that we do not impose any additional constraints on the model weights. However, we do not own the data in the training collection.

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# load the NorMistral tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("norallm/normistral-11b-translate")
model = AutoModelForCausalLM.from_pretrained(
    "norallm/normistral-11b-translate",
    device_map='auto',
    torch_dtype=torch.bfloat16
)

# create a conversation and convert it to token indices using the NorMistral chat template
messages = [
    {"role": "system", "content": "nynorsk"},  # Optional message to set the target language for translation; "engelsk" by default, "bokmål" and "nynorsk" are supported
    {"role": "user", "content": "Hva er hovedstaden i Norge?"}
]
input_tokens = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

# run the generation (customizable via the various parameters)
output_tokens = model.generate(
    input_tokens,
    max_new_tokens=2048,  # limit max number of generated tokens
    do_sample=False  # do not randomly sample the outputs
)

# decode the generated tokens back to text; should return "Kva er hovudstaden i Noreg?"
output_str = tokenizer.decode(output_tokens[0, input_tokens.size(1):], skip_special_tokens=True).strip()

Training data

The full training corpus is published as ltg/nob-nno-eng-translation-pairs.

Contact

David Samuel (davisamu@ifi.uio.no)

License

@inproceedings{samuel-etal-2025-small,
    title = "Small Languages, Big Models: {A} Study of Continual Training on Languages of {Norway}",
    author = "Samuel, David  and
      Mikhailov, Vladislav  and
      Velldal, Erik  and
      {\O}vrelid, Lilja  and
      Charpentier, Lucas Georges Gabriel  and
      Kutuzov, Andrey  and
      Oepen, Stephan",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://aclanthology.org/2025.nodalida-1.61/",
    pages = "573--608",
    ISBN = "978-9908-53-109-0",
}

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Safetensors

Model size

11B params

Tensor type

BF16

Model tree for norallm/normistral-11b-translate

Finetunes

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