Developed by: Tilde.ai
Funded by: European Commission via EuroHPC JU Large AI Grand Challenge
Model type: A 30B parameter dense decoder-only transformer
Languages: Albanian, Bosnian, Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Hungarian, Icelandic, Irish, Italian, Latgalian, Latvian, Lithuanian, Macedonian, Maltese, Montenegrin, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovene, Spanish, Swedish, Turkish, Ukrainian as well of mathematical proofs, programming code and XML documents containing translation data
License: CC-BY-4.0

Mission statement

TildeOpen LLM is an open-source foundational language model built to serve underrepresented Nordic and Eastern European languages. Developed with European Commission funding and trained on the LUMI supercomputer, this 30B+ parameter model addresses the performance gaps that speakers of 19 focus languages—representing over 165 million people—face with existing AI systems.
The model employs an equitable tokeniser and curriculum-learning approach to ensure fair representation across less-resourced languages, moving beyond the typical English-centric design of most language models. As an open-source project, TildeOpen LLM enables transparent research and community-driven development while maintaining European technological independence.
This foundational model is not yet adapted to follow instructions or aligned with safety features. The next version being built on top of this model will be a specialised translation model, leveraging TildeOpen LLM's multilingual foundation to provide high-quality translation capabilities across the supported European language pairs.

Model training details

We train TildeOpen LLM using the Tilde's branch of EleutherAI's open-source GPT-NeoX framework on LUMI supercomputer's 768 AMD MI250X GPUs. The foundational model training involves 450,000 updates with a constant batch size of 4,718,592 tokens, using a constant learning rate followed by a cooldown phase across 2 trillion tokens. Training consists of three distinct data sampling phases. First, all languages are sampled uniformly to ensure equal representation. Second, languages are sampled according to their natural distribution to ensure that the model sees as much data from languages with larger speaker bases as possible. Finally, we return to uniform sampling across all languages. This three-phase approach ensures TildeOpen LLM develops balanced multilingual capabilities while maintaining strong performance across all target languages, particularly the underrepresented European languages.

Model Hyper-Parameters

Parameter	Value
Sequence Length	8192
Number of Layers	60
Embedding Size	6144
FFN Hidden Size	21504
Number of Heads	48
Number of KV Heads (GQA)	8
Activation Function	SwiGLU
Position Encodings	RoPE
Layer Norm	RMSNorm
Embedding Parameters	8.05E+08
LM Head Parameters	8.05E+08
Non-embedding Parameters	2.91E+10
Total Parameters	3.07E+10

Tokeniser details

We built the TildeOpen LLM tokeniser to ensure equitable language representation across languages. Technically, we trained the tokeniser to represent the same text regardless of the language it is written in, using a similar number of tokens. In practice, TildeOpen LLM will be more efficient and faster than other models for our focus languages, as writing out answers will require fewer steps. For more details on how TildeOpen LLM compares against other models, see TILDE Bench!

Running model using HF transformers

When loading the tokeniser, you must set use_fast=False.

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer + model
tokenizer = AutoTokenizer.from_pretrained("TildeAI/TildeOpen-30b", use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    "TildeAI/TildeOpen-30b",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Tokenize
inputs = tokenizer(user_in, return_tensors="pt").to(model.device)

# Generate (greedy, deterministic)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    repetition_penalty=1.2,
    do_sample=False,
)

Evaluation

Per-Character Perplexity

What is Perplexity? Perplexity measures how well a language model predicts text. A model with low perplexity makes accurate predictions consistently, while a high perplexity means the model is frequently "surprised" by unexpected words or patterns. Lower perplexity indicates the model has learned language patterns more effectively. It's less "surprised" by what it encounters because it better understands how the language works. Perplexity fairly evaluates how well each model handles:

Spelling accuracy across a diverse vocabulary
Grammar rules that span multiple words
Sentence structure and flow
Language-specific patterns (how different languages form plural forms or compound words)

Why Character-Level? Different language models use different internal vocabularies - some break text into whole words, others into word fragments, and some into individual characters. This makes direct comparison difficult. Character-level perplexity creates a standardised comparison by calculating how well each model would theoretically perform if we measured their predictions character-by-character. We're not changing how the models work - instead, we use mathematical conversion to approximate their character-level performance based on their predictions.

Why does this Matter? Models with lower perplexity generally perform better on real-world tasks like text generation, translation, and understanding context. It's a reliable indicator of overall language competency across different applications.

What data did we use? We use WMT24++ as it is a multilingual, language-parallel evaluation set that none of the models have seen during training. WMT24++ is a composite of texts from news, literature, speech, and social media; thus, it is suitable for foundational model benchmarking.

Language	TildeOpen-30B	Gemma-2-27B	EuroLLM-9B	ALIA-40B
Bulgarian	2.1716	2.3541	2.3502	2.2411
Croatian	2.2259	2.6809	2.6780	2.3456
Czech	2.2682	2.4873	2.4808	2.3639
Danish	2.0968	2.2608	2.2586	2.1543
Dutch	2.0136	2.1249	2.1185	2.0629
English	2.1497	2.0342	2.1897	2.1027
Estonian	2.2825	2.7163	2.5652	2.4232
Finnish	2.1687	2.4069	2.3844	2.2774
French	1.9779	2.0195	2.0479	1.9750
German	1.9664	2.0214	2.0499	1.9725
Hungarian	2.1481	2.3308	2.3705	2.2493
Icelandic	2.2011	3.1917	5.3162	4.0978
Italian	2.0431	2.1065	2.1213	2.0604
Latvian	2.2477	2.6701	2.4896	2.4352
Lithuanian	2.2301	2.5495	2.4754	2.4109
Norwegian	2.2445	2.4173	2.5121	2.3152
Polish	2.1214	2.2294	2.2264	2.1847
Portuguese	2.0810	2.1554	2.1561	2.0884
Romanian	2.1266	2.2724	2.2821	2.1974
Russian	2.1502	2.2091	2.2813	2.1889
Serbian	2.3708	2.8053	4.7160	2.5119
Slovak	2.2281	2.4674	2.4588	2.3505
Slovenian	2.2662	2.5798	2.5087	2.3611
Spanish	2.0400	2.0665	2.1186	2.0055
Swedish	2.1471	2.2971	2.2856	2.2039
Turkish	2.2108	2.3665	2.3508	3.0611
Ukrainian	2.2470	2.4000	2.4251	2.3168

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TildeOpen-30b