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---
license: apache-2.0
datasets:
- HPLT/HPLT3.0
- allenai/olmo-mix-1124
- HuggingFaceFW/finepdfs
- HuggingFaceTB/finemath
- LLM360/MegaMath
- HuggingFaceTB/stack-edu
- HuggingFaceFW/finepdfs-edu
- cis-lmu/Glot500
- ltg/saami-web
language:
- nb
- nn
- 'no'
base_model:
- allenai/OLMo-2-1124-13B
library_name: transformers
tags:
- norwegian
- norsk
- HPLT
---
# NorOLMo
![](https://hplt-project.org/_next/static/media/logo-hplt.8765d2d4.svg)
This is a base (not instruction-tuned) large language model, continually pre-trained on Norwegian data starting from the English [OLMo2-13B](https://huggingface.co/allenai/OLMo-2-1124-13B) model.
The model was trained for 33 000 steps on around 275 billion tokens. Intermediate checkpoints are published here as branches.
## Data Details
### Stage 1 (24 000 steps -- 200B tokens)
Data
- [HPLTv3](https://huggingface.co/datasets/HPLT/HPLT3.0) Bokmål, Nynorsk, Faroese, Icelandic, Danish, Swedish
- FinePDFs Bokmål, Nynorsk, Faroese, Icelandic, Danish, Swedish
- OLMo-Mix
- Northern Sami (cis-lmu/Glot500, ltg/saami-web, SIKOR North Saami corpus)
Data Splits
| Data | Percentage | Unique Tokens | Total Tokens | Number of Documents | Average Document Length |
| ------------------------ | ---------- | ------------- | ------------ | ------------------- | ----------------------- |
| HPLT Bokmål | 39.57 | 39.8B | 79.7B | 36.5M | 1 092 |
| HPLT Nynorsk | 4.95 | 1.2B | 10.0B | 1.5M | 826 |
| HPLT Faroese | 0.46 | 0.2B | 0.9B | 0.3M | 711 |
| HPLT Icelandic | 2.50 | 5.0B | 5.0B | 4.3M | 1 173 |
| HPLT Swedish | 12.09 | 92.1B | 24.4B | 97.7M | 942 |
| HPLT Danish | 12.12 | 50.1B | 24.4B | 52.5M | 954 |
| FinePDFs Bokmål | 8.36 | 8.4B | 16.8B | 1.5M | 5 604 |
| FinePDFs Nynorsk | 1.15 | 0.3B | 2.3B | 92.8K | 3 117 |
| FinePDFs Faroese | 0.17 | 87.1M | 0.3B | 20.8K | 4 196 |
| FinePDFs Icelandic | 1.60 | 3.2B | 3.2B | 0.4M | 8 855 |
| FinePDFs Swedish | 2.48 | 18.9B | 5.0B | 4.1M | 4 574 |
| FinePDFs Danish | 2.45 | 10.1B | 4.9B | 2.4M | 4 190 |
| Northern Sami | 0.18 | 46.4M | 0.4B | 0.2M | 288 |
| Wiki (OLMo-Mix) | 0.02 | 0.2B | 40.3M | 0.3M | 667 |
| Alg. Stack (OLMo-Mix) | 0.04 | 0.6B | 80.5M | 0.1M | 4 201 |
| Open Web Math (OLMo-Mix) | 0.04 | 0.6B | 80.5M | 0.1M | 4 199 |
| ArXiv (OLMo-Mix) | 0.05 | 1.0B | 0.1B | 0.2M | 5 210 |
| PeS2o (OLMo-Mix) | 0.15 | 2.5B | 0.3B | 1.6M | 1 641 |
| DCLM (OLMo-Mix) | 9.50 | 48.3B | 19.1B | 35.1M | 1 377 |
| StarCoder (OLMo-Mix) | 2.10 | 30.5B | 4.2B | 23.6M | 1 293 |
> [!NOTE]
> The number of documents represents the total unique number of documents, not the documents used during training.
> [!NOTE]
> We only took a portion of OLMo-Mix as our unique data.
### Stage 2 (6 000 steps -- 50B tokens)
Data
- [HPLTv3](https://huggingface.co/datasets/HPLT/HPLT3.0) (filtered) Bokmål, Nynorsk, Icelandic, Danish, Swedish
- FinePDFs-Edu Bokmål, Nynorsk, Icelandic, Danish, Swedish, English
- FindePDFs Faroese
- Northern Sami (cis-lmu/Glot500, ltg/saami-web, SIKOR North Saami corpus)
- Stack-Edu
- MegaMath Web-Pro
- FineMath 4+
- InfiWebMath 4+
Data Splits
| Data | Percentage | Unique Tokens | Total Tokens | Number of Documents | Average Document Length |
| ------------------------ | ---------- | ------------- | ------------ | ------------------- | ----------------------- |
| HPLT Bokmål | 45.78 | 23.0B | 23.0B | 19.0M | 1 215 |
| HPLT Nynorsk | 7.84 | 1.0B | 3.9B | 1.0M | 1 003 |
| HPLT Icelandic | 6.87 | 3.5B | 3.5B | 2.7M | 1 268 |
| HPLT Swedish | 4.90 | 2.5B | 2.5B | 3.6M | 3 403 |
| HPLT Danish | 7.73 | 3.9B | 3.9B | 4.1M | 2 950 |
| FinePDFs-Edu Bokmål | 2.24 | 1.1B | 1.1B | 0.2M | 6 897 |
| FinePDFs-Edu Nynorsk | 0.28 | 35.8M | 0.1B | 9.7K | 3 681 |
| FinePDFs Faroese | 0.69 | 87.1M | 0.3B | 20.8K | 4 196 |
| FinePDFs-Edu Icelandic | 0.53 | 0.3B | 0.3B | 40.1K | 6 598 |
| FinePDFs-Edu Swedish | 5.80 | 2.9B | 2.9B | 0.4M | 6 755 |
| FinePDFs-Edu Danish | 2.97 | 1.5B | 1.5B | 0.3M | 5 833 |
| FinePDFs-Edu English | 7.00 | 7.2B | 3.5B | 1.1M | 6 280 |
| Northern Sami | 0.37 | 46.4M | 0.2B | 0.2M | 288 |
| Stack-Edu | 5.00 | 12.8B | 2.5B | 15.0M | 856 |
| MegaMath Web-Pro | 0.84 | 13.7B | 0.4B | 15.0M | 917 |
| FineMath 4+ | 0.62 | 10.1B | 0.3B | 6.7M | 1 512 |
| InfiWebMath 4+ | 0.54 | 8.9B | 0.3B | 6.3M | 1 417 |
### Stage 2-continued (3 000 steps -- 25B tokens)
Same data as for stage 2 but using half the total tokens.
## Training details
### Stage 1
| Hyperparameter | Value |
| ------------------------ | ----------------- |
| Embedding train steps | 1 000 |
| Warmup steps | 2 000 |
| Total train steps | 24 000 |
| Learning rate schedule | Warmup + constant |
| Learning rate | 3e-4 |
| Weight decay | 1e-1 |
| Sequence length | 4 096 |
| Batch size | 2 048 |
| RoPe theta | 500 000 |
| Clip grad | 1.0 |
| Adam epsilon | 1e-8 |
| Adam beta_1 | 0.9 |
| Adam beta_2 | 0.95 |
| RMSNorm epsilon | 1e-6 |
| Z-loss ratio | 1e-5 |
| Diffusion loss ratio | 2e-2 |
### Stage 2
| Hyperparameter | Value |
| ------------------------ | ----------------- |
| Decay steps | 6 000 |
| Total train steps | 6 000 |
| Learning rate schedule | Linear decay |
| Initial learning rate | 3e-4 |
| Final learning rate | 0 |
| Weight decay | 1e-1 |
| Sequence length | 16 384 |
| Batch size | 512 |
| RoPe theta | 2 000 000 |
| Clip grad | 1.0 |
| Adam epsilon | 1e-8 |
| Adam beta_1 | 0.9 |
| Adam beta_2 | 0.95 |
| RMSNorm epsilon | 1e-6 |
| Z-loss ratio | 1e-5 |
| Diffusion loss ratio | 2e-2 |
### Stage 2-continued
| Hyperparameter | Value |
| ------------------------ | --------------------- |
| Warmup steps | 100 |
| Decay steps | 2 900 |
| Total train steps | 3 000 |
| Learning rate schedule | Warmup + Linear decay |
| Max learning rate | 3e-4 |
| Final learning rate | 0 |
| Weight decay | 1e-1 |
| Sequence length | 16 384 |
| Batch size | 512 |
| RoPe theta | 2 000 000 |
| Clip grad | 1.0 |
| Adam epsilon | 1e-8 |
| Adam beta_1 | 0.9 |
| Adam beta_2 | 0.95 |
| RMSNorm epsilon | 1e-6 |
| Z-loss ratio | 1e-5 |
| Diffusion loss ratio | 2e-2 |
## Acknowledgements
Training was conducted as a part of the [HPLT project](https://hplt-project.org/).
_This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10052546]_

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216
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