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Model: norallm/normistral-11b-long
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- nb
+- nn
+- 'no'
+- se
+- sv
+- da
+- en
+- is
+- fo
+base_model:
+- norallm/normistral-11b-warm
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- norwegian
+- sami
+- bokmaal
+- nynorsk
+---
+
+![](https://huggingface.co/norallm/normistral-11b-warm/resolve/main/images/puffin_2.png)
+
+
+**NorMistral-11b-long** is a length-extended version of [NorMistral-11b-warm](https://huggingface.co/norallm/normistral-11b-warm). It has been extended to 32,768 context length by continual training on additional 50 billion subword tokens – using a mix of Scandinavian, Sámi, English and code data (four repetitions of open Norwegian texts). The model follows our earlier paper [Small Languages, Big Models: A Study of Continual Training on Languages of Norway](https://arxiv.org/abs/2412.06484) by Samuel et al. 2025, and forms part of the NORA.LLM family developed by [the Language Technology Group at the University of Oslo (LTG)](https://huggingface.co/ltg).
+
+*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.*
+
+
+## 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.
+
+
+## Pretraining corpus
+
+The model is pretrained on a combination of publicly available data and a custom web crawl for Sámi. The total training corpus consists of 250 billion tokens from the following sources:
+
+1. Norwegian text (Bokmål and Nynorsk); this collection was created by the National Library of Norway and it's a prerelease of an update of NCC (codenamed "Mímir core"). It consists of: a) the public part of [Norwegian Colossal Corpus (NCC)](https://huggingface.co/datasets/NbAiLab/NCC) with permissible licenses (i.e. it doesn't include newspaper texts with the CC BY-NC 2.0 license); b) Bokmål and Nynorsk [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX), and c) Bokmål and Nynorsk [HPLT corpus v1.2](https://hplt-project.org/datasets/v1.2).
+
+2. Northern Sámi texts are sourced from a) [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500); b) [the SIKOR North Saami free corpus](https://repo.clarino.uib.no/xmlui/handle/11509/100); and c) a custom web crawl (seeded from Sámi Wikipedia external links) published separately as [`ltg/saami-web`](https://huggingface.co/datasets/ltg/saami-web).
+
+3. Additional languages for knowledge/language transfer: a) Danish, Swedish, Icelandic, and Faroese from CulturaX and Glot500; b) high-quality English from [FineWeb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu); and c) programming code from [The Stack v2 (the high-quality subset)](https://huggingface.co/datasets/bigcode/the-stack-v2-train-smol-ids).
+
+The corpus is carefully balanced through strategic upsampling to handle the resource disparity between languages. Following data-constrained scaling laws, the corpus data for target languages is repeated multiple times (up to 16x for low-resource languages) to reach the optimal training budget while avoiding overfitting:
+
+![](https://huggingface.co/norallm/normistral-11b-warm/resolve/main/images/corpus.png)
+
+
+
+## Tokenizer
+
+This model uses a new tokenizer, specially trained on the target languages. Therefore it offers substantially faster inference than the original Mistral-Nemo-Base-2407 model. Here are the subword-to-word split ratios across different languages:
+
+| Tokenizer  | # tokens | Bokmål | Nynorsk | Sámi  | Danish | Swedish |
+|:------------|:--------:|:--------:|:---------:|:-------:|:--------:|:---------:|
+| Mistral-Nemo-Base-2407    | 131072 | 1.79   | 1.87    | 2.63  | 1.82   | 2.00    |
+| NorMistral-11b-long | 51200 | 1.22   | 1.28    | 1.82  | 1.33   | 1.39    |
+
+
+## Model details
+
+**Model Developers:** Language Technology Group at the University of Oslo in collaboration with NORA.LLM.
+
+**Architecture:** NorMistral-11B uses the Mistral architecture based on an improved Llama design, featuring:
+- Pre-normalization with RMSNorm
+- SwiGLU activation function
+- Rotary positional embeddings
+- Grouped-query attention
+- 40 transformer layers
+- Hidden dimension: 5,120
+- Intermediate dimension: 14,336
+- 32 query heads and 8 key & value heads (dimension 128)
+- Vocabulary size: 51,200 tokens
+- Total parameters: 11.4 billion
+
+**Training Details:**
+- Training tokens: 250 + 50 billion
+- Batch size: 128 × 32,768 tokens (# sequences × sequence length)
+- Training steps: 12,000
+
+
+**Base Model:** Initialized from NorMistral-11b-warm
+
+**License:** Apache-2.0
+
+
+
+## Example usage
+
+### Basic Causal Language Model Usage
+
+Here's how to use NorMistral-11B as a standard causal language model for translation:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Import the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("norallm/normistral-11b-long")
+model = AutoModelForCausalLM.from_pretrained("norallm/normistral-11b-long").cuda().eval()
+
+# Define zero-shot translation prompt template
+prompt = """Engelsk: {0}
+Bokmål:"""
+
+# Define tokens that should end the generation (any token with a newline)
+eos_token_ids = [
+    token_id
+    for token_id in range(tokenizer.vocab_size)
+    if '\n' in tokenizer.decode([token_id])
+]
+
+# Generation function
+@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=eos_token_ids
+    )
+    return tokenizer.decode(prediction[0, input_ids.size(1):]).strip()
+
+# Example usage
+generate("I'm excited to try this new Norwegian language model!")
+# > Expected output: 'Jeg er spent på å prøve denne nye norske språkmodellen!'
+```
+
+### Memory-Efficient Loading
+
+For systems with limited VRAM, you can load the model in 8-bit or 4-bit quantization:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("norallm/normistral-11b-long")
+
+# Load in 8-bit mode (requires ~12GB VRAM)
+model = AutoModelForCausalLM.from_pretrained(
+    "norallm/normistral-11b-long",
+    device_map='auto',
+    load_in_8bit=True,
+    torch_dtype=torch.bfloat16
+)
+
+# Or load in 4-bit mode (requires ~8GB VRAM)
+model = AutoModelForCausalLM.from_pretrained(
+    "norallm/normistral-11b-long",
+    device_map='auto',
+    load_in_4bit=True,
+    torch_dtype=torch.bfloat16
+)
+```
+
+
+## Citation
+
+```bibtex
+@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",
+}
+```
+
+## Contact
+
+Please write [a community message](https://huggingface.co/norallm/normistral-11b-long/discussions) or contact David Samuel (davisamu@ifi.uio.no) if you have any questions about this model.

config.json

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+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "mask_token_id": 4,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": 3,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.3",
+  "use_cache": true,
+  "vocab_size": 51200
+}

convert_to_safetensors.py

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+import argparse
+import random
+from statistics import mean, stdev
+from typing import List
+import torch
+import torchmetrics
+from datasets import load_dataset
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        default=".",
+        help="Path to the pre-trained model",
+    )
+    args = parser.parse_args()
+
+    return args
+
+
+def load_model(model_path: str):
+    # Load the pre-trained model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path, torch_dtype=torch.bfloat16)
+    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda().eval()
+
+    eos_token_ids = [
+        token_id
+        for token_id in range(tokenizer.vocab_size)
+        if "\n" in tokenizer.decode([token_id])
+    ]
+
+    if hasattr(model.config, "n_positions"):
+        max_length = model.config.n_positions
+    elif hasattr(model.config, "max_position_embeddings"):
+        max_length = model.config.max_position_embeddings
+    elif hasattr(model.config, "max_length"):
+        max_length = model.config.max_length
+    elif hasattr(model.config, "n_ctx"):
+        max_length = model.config.n_ctx
+    else:
+        max_length = 32768  # Default value
+
+    return {
+        "name": model_path.split("/")[-1],
+        "tokenizer": tokenizer,
+        "model": model,
+        "eos_token_ids": eos_token_ids,
+        "max_length": max_length,
+    }
+
+
+def main():
+    args = parse_args()
+
+    model = load_model(args.model_name_or_path)
+    
+    model["model"].save_pretrained(
+        args.model_name_or_path,
+        max_shard_size="5GB"
+    )
+
+
+if __name__ == "__main__":
+    main()

convert_weights.py

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+import argparse
+import random
+from statistics import mean, stdev
+from typing import List
+import torch
+import torchmetrics
+from datasets import load_dataset
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        default=".",
+        help="Path to the pre-trained model",
+    )
+    args = parser.parse_args()
+
+    return args
+
+
+def load_model(model_path: str):
+    # Load the pre-trained model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path, torch_dtype=torch.bfloat16)
+    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda().eval()
+
+    eos_token_ids = [
+        token_id
+        for token_id in range(tokenizer.vocab_size)
+        if "\n" in tokenizer.decode([token_id])
+    ]
+
+    if hasattr(model.config, "n_positions"):
+        max_length = model.config.n_positions
+    elif hasattr(model.config, "max_position_embeddings"):
+        max_length = model.config.max_position_embeddings
+    elif hasattr(model.config, "max_length"):
+        max_length = model.config.max_length
+    elif hasattr(model.config, "n_ctx"):
+        max_length = model.config.n_ctx
+    else:
+        max_length = 32768  # Default value
+
+    return {
+        "name": model_path.split("/")[-1],
+        "tokenizer": tokenizer,
+        "model": model,
+        "eos_token_ids": eos_token_ids,
+        "max_length": max_length,
+    }
+
+
+def main():
+    args = parse_args()
+
+    model = load_model(args.model_name_or_path)
+    
+    model["model"].save_pretrained(
+        args.model_name_or_path,
+        max_shard_size="4.7GB"
+    )
+
+
+if __name__ == "__main__":
+    main()

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 3,
+  "transformers_version": "4.55.3"
+}

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