Llama 3.1 EstLLM 70B 0826 Instruct

The model underwent continuous pre-training starting from meta-llama/Llama-3.1-70B on approximately 60B tokens, which resulted in tartuNLP/Llama-3.1-EstLLM-70B-0826, then supervised fine-tuning and direct preference optimization were applied.

Use with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "tartuNLP/Llama-3.1-EstLLM-70B-Instruct-0826"

# this requires over 140GB of VRAM
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype="auto",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Kas sa räägid eesti keelt?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer(text, return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=128,
    do_sample=True,
    temperature=0.4,
    # specify eos token to stop at the end of the assistant response
    eos_token_id=tokenizer.eos_token_id,
)

# generated_ids include the input tokens as well, so we only decode new tokens
response = tokenizer.decode(
    generated_ids[0][model_inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)

print(response)

Model Details

• Developed by: TartuNLP and TalTechNLP research groups
• Funded by: Estonian Ministry of Education and Research, “Estonian Language Technology Program 2018-2027”
• Model type: Causal Language Model, Instruction-following
• Language(s) (NLP): Estonian, English
• License: Llama 3.1 Community License Agreement
• Finetuned from model: tartuNLP/Llama-3.1-EstLLM-70B-0826

Evaluation

Generative

Every benchmark in this category is treated as a generative problem, and thus the evaluation is performed on the model responses obtained with 0 temperature (not logits). The top scores are higlighted with bold. Second best scores are highlighted with italic bold. Rows are sorted in descending order based on the number of parameters of models (not scores). The test set is used for evaluation of each dataset unless noted otherwise.

Note that all models are evaluated with the same prompt template for comparability, meaning that the scores do not necessarily represent each model's best possible performance. This is especially the case for deepseek-ai/DeepSeek-V3-0324 on some of the benchmarks.

Only models of comparable size are evaluated on benchmarks in English.

Instruction-following

Estonian

Instruction level strict accuracy is reported for IFEval-et.

English

Instruction level strict accuracy is reported for IFEval-en.

Multiple Choice

All datasets except Winogrande-et are evaluated in 0-shot mode. Winogrande-et is evaluated in 3-shot mode. Exact match accuracy is reported for every dataset.

Estonian Language Competence

Knowledge and Reasoning (Estonian)

Knowledge and Reasoning (English)

Translation

English to Estonian

Limitations

TODO

Citation

@misc{dorkin2026estllmenhancingestoniancapabilities,
      title={{EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training}}, 
      author={Aleksei Dorkin and Taido Purason and Emil Kalbaliyev and Hele-Andra Kuulmets and Marii Ojastu and Mark Fišel and Tanel Alumäe and Eleri Aedmaa and Krister Kruusmaa and Kairit Sirts},
      year={2026},
      eprint={2603.02041},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.02041}, 
}