norbloom-7b-scratch

NorBLOOM-7b-scratch

<img align="center" src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%>

NorBLOOM-7b-scratch is a large Norwegian language model pretrained from scratch on a total of 260 billion subword tokens (using six repetitions of open Norwegian texts).

This model is a part of the NORA.LLM family developed in collaboration between the Language Technology Group at the University of Oslo, the High Performance Language Technologies (HPLT) project, the National Library of Norway, and the University of Turku. All the models are pre-trained on the same dataset and with the same tokenizer. NorBLOOM-7b-scratch has around 7 billion parameters and is based on the BLOOM architecture.

The NORA.LLM language model family includes (as of now):

• NorMistral-7b-warm -- an LLM initialized from Mistral-7b-v0.1 and continuously pretrained on Norwegian data;
• NorMistral-7b-scratch -- a Mistral-based LLM pretrained from scratch on Norwegian data;
• NorBLOOM-7b-scratch -- a BLOOM-based LLM pretrained from scratch on Norwegian data.

Disclaimer: This model is pretrained on raw (mostly web-based) textual data. It is not finetuned to follow instructions, and it can generate harmful completions after inappropriate user prompts. It is primarily intended for research purposes.

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Pretraining corpus

The model is pretrained exclusively on publicly available data. We combine the resources from the public part of the NCC corpus, from the cleaned HPLT corpus, and from CulturaX. This resulted in over 34B subword tokens of Norwegian (Bokmål or Nynorsk) in total, which amounts to about 26.7B whitespace-separated tokens. We also augment the corpus with Starcoder; 20% of the 260B tokens are sampled from this code corpus. The natural language data is repeated six times to get the pretraining budget of 260B tokens, in accordance with findings from Muennighoff et al. (2023).

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Model details

Model Developers: Language Technology Group at the University of Oslo.

Input: Textual input.

Output: Generated text.

Model Architecture: NorBLOOM is an auto-regressive language model that uses the BLOOM architecture.

Tokenizer: Byte-based BPE tokenizer trained on the same Norwegian corpus as this model. The vocabulary size is 32,768 tokens.

Training FLOPs The approximate amount is 1.12e+22 FLOPs; calculated as in Chowdhery et al. (2022).

Model Dates: This model was pretrained in December 2023.

Status: This is only a pretrained language model; an instruction-finetuned model will follow soon.

License: Apache-2.0

Research Paper: Forthcoming

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Initial evaluation

Disclaimer: our model evaluation is an ongoing phase and is not claimed to be exhaustive. We provide our initial evaluation results on standard natural language understanding and generation tasks, and our evaluation design will be extended. The user should perform evaluation for their particular model application scenario, including safety and bias evaluations.

The perplexity on the heldout validation set from the Norwegian Colossal Corpus (NCC) is 7.43 and the final training perplexity is 4.76.

Our initial downstream evaluation is conducted on reading comprehension, sentiment analysis and machine translation tasks using open-source peer-reviewed datasets and benchmarks in native Norwegian. We release our codebase here. We compare against other pretrained generative language models that officially support Norwegian: NB-GPT-J, GPT-Sw3 6.7B, GPT-Sw3 6.7B v2, and Falcon-7B; we also include evaluation of Mistral-7b-v0.1.

Sentiment analysis

NoReC (Øvrelid et al., 2020) is a dataset for sentence-level sentiment analysis derived from the Norwegian Review Corpus (Velldal et al., 2018). We use the binary formulation of this task (positive vs. negative).

<details> <summary>Method (click to expand)</summary>

• Evaluation setting: zero-shot and few-shot perplexity-based evaluation.
• Prompt: "Tekst: {text}\nSentiment:{label}", where the label is either "positiv" or "negativ".
• Few-shot results show the average scores across 5 repetitions
• Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/sentiment_analysis.py
• Performance metric: macro-averaged F1-score.

</details> <details open> <summary>Macro-averaged F1-scores on the sentence-level sentiment analysis task (NoReC)</summary> </details>

Reading comprehension

NorQuAD (Ivanova et al., 2023) is a dataset for extractive question answering in Norwegian designed similarly to SQuAD (Rajpurkar et al., 2016).

<details> <summary>Method (click to expand)</summary>

• Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
• Prompt: "Tittel: {title}\n\nTekst: {text}\n\nSpørsmål: {question}\n\nSvar:{answer}" Based on Brown et al. (2020).
• Few-shot results show the average scores across 5 repetitions
• Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/norquad.py
• Performance metrics: macro-averaged F1-score and exact match (EM).

</details> <details open> <summary>Performance results on the extractive question answering task (NorQuAD)</summary> </details>

Grammatical error correction

ASK-RAW is dataset for Norwegian grammatical error correction (GEC) created by Matias Jentoft (2023).

<details> <summary>Method (click to expand)</summary>

• Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
• Prompt: "Her er eksempler på perfekt korrigering av grammatiske feil:\n\nTekst: {source_text}\nKorreksjon:{target_text}"
• Few-shot results show the average scores across 5 repetitions
• Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/gec.py
• Performance metrics: the evaluation metric uses ERRANT, which identifies edit-spans and then calculates the F_{0.5} scores between the gold edits and predicted edits.

</details> <details open> <summary>Results on [the ASK corpus](https://huggingface.co/datasets/ltg/ask-gec) (ERRANT F_{0.5})</summary> </details>

Machine translation

Tatoeba (Tiedemann, 2020) is a benchmark for machine translation, which includes hundreds of language pairs. We consider six language pairs (English <-> Bokmål, English <-> Nynorsk, and Bokmål <-> Nynorsk).

<details> <summary>Method (click to expand)</summary>

• Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
• Prompt: "{source_language}: {source_text}\n{target_language}:{target_text}", where the source_language and target_language are Engelsk, Bokmål, or Nynorsk. Based on Garcia et al. (2023).
• Few-shot results show the average scores across 5 repetitions
• Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/machine_translation.py
• Performance metrics: BLEU (Papineni et al., 2002) and chrF++ (Popović, 2015).

</details> <details open> <summary>English → Norwegian Bokmål</summary> </details> <details open> <summary>English → Norwegian Nynorsk</summary> </details> <details open> <summary>Norwegian Bokmål → English</summary> </details> <details open> <summary>Norwegian Nynorsk → English</summary> </details> <details open> <summary>Norwegian Bokmål → Norwegian Nynorsk</summary> </details> <details open> <summary>Norwegian Nynorsk → Norwegian Bokmål</summary> </details>

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Hardware and Software

Training Factors: The models were pretrained using the Megatron-DeepSpeed library on the LUMI cluster in Finland.

Carbon Footprint: Pretraining one model took approximately 70k GPU hours of computation on AMD MI250X GPUs (assuming 2 GPUs per one AMD MI250X device), each of which draws 500W. LUMI is one of the most eco-efficient data centers in the world, and its energy consumption is covered 100% with renewable electricity.

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Example usage

Let's try to use this model for English-to-Norwegian machine translation using simple zero-shot prompting:

from transformers import AutoTokenizer, AutoModelForCausalLM

# First, we will have to import the tokenizer and the language model
tokenizer = AutoTokenizer.from_pretrained("norallm/norbloom-7b-scratch")
model = AutoModelForCausalLM.from_pretrained("norallm/norbloom-7b-scratch").cuda().eval()

# Now we will define the zero-shot prompt template
prompt = """Engelsk: {0}
Bokmål:"""

# A function that will take care of generating the output
@torch.no_grad()
def generate(text):
    text = prompt.format(text)
    input_ids = tokenizer(text, return_tensors='pt').input_ids.cuda()
    prediction = model.generate(
        input_ids,
        max_new_tokens=64,
        do_sample=False,
        eos_token_id=tokenizer('\n').input_ids
    )
    return tokenizer.decode(prediction[0, input_ids.size(1):]).strip()

# Now you can simply call the generate function with an English text you want to translate:
generate("I'm super excited about this Norwegian NORA model! Can it translate these sentences?")

Example usage on a GPU with ~16GB VRAM (try for yourself in Google Colab)

Install bitsandbytes if you want to load in 8bit

pip install bitsandbytes
pip install accelerate

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained(
    "norallm/norbloom-7b-scratch"
)

# This setup needs about 8gb VRAM
# Setting `load_in_8bit=False` -> 15gb VRAM
# Using `torch.float32` and `load_in_8bit=False` -> 21gb VRAM
model = AutoModelForCausalLM.from_pretrained(
    "norallm/norbloom-7b-scratch",
    device_map='auto',
    load_in_8bit=True,
    torch_dtype=torch.bfloat16
)

Citation

@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"
}