You can deploy this model and start earning money today!

cjvt/
GaMS3-12B-Instruct

Name: GaMS3-12B-Instruct
Availability: InStock

google/gemma-3-12b-pt

finetune

gemma

Views

No views yet

Model Card for GaMS3-12B-Instruct

GaMS3-12B-Instruct represents the next generation of the GaMS (Generative Model for Slovene) model. The model is based on Google's Gemma 3 family and continually pretrained on Slovene, English, and some portion of Croatian, Serbian, and Bosnian corpora. The supervised fine-tuning phase was done on a combination of Slovene and English datasets.

Acknowledgment

The model was developed within the PoVeJMo research program (Adaptive Natural Language Processing with Large Language Models), particularly within the research project titled SloLLaMai -- Open-access computationally efficient models for Slovenian. The program is funded within the Recovery and Resilience Plan by the Slovenian Research and Innovation Agency (ARIS) and NextGenerationEU. The authors also acknowledge the financial support from the Slovenian Research and Innovation Agency (research core funding No. P6-0411 -- Language Resources and Technologies for Slovene).

This project is also funded by the European Union under Horizon Europe (101186647 – AI4DH).

We thank everyone who contributed to data collection and preparation, which enabled us to train our model. Special thanks go to Nikola Ljubešić, Taja Kuzman, Tjaša Arčon, Jaka Čibej, Simon Krek, Tomaž Erjavec, Iztok Kosem and Tomaž Savodnik.

The model's development was supported by NVIDIA as a part of their Sovereign AI initiative. We are thankful for the access to NVIDIA DGX Cloud Lepton. We are also extremely grateful for all the support and help we received from a group of exceptional people at NVIDIA: Anna Louise Ollerenshaw, Meriem Bendris, Oleg Sudakov, Benedetta Delfino, Rita Fernandes Neves, Andrea Pilzer, Miguel Martinez, Noel Osagie, Adam Henryk Grzywaczewski and Aleks Polak.

Basic information

Developed by: team of researchers at the University of Ljubljana, Faculty for Computer and Information Science. Team members: Domen Vreš, Iztok Lebar Bajec, Tjaša Arčon, Timotej Petrič, Dario Vajda and Marko Robnik-Šikonja.
Languages: Slovene, English (primary), Croatian, Bosnian and Serbian (secondary). The model might also work for other languages supported by Gemma 3, even though it was not continually pretrained on them.
Base model: google/gemma-3-12b-pt
License: Gemma

Usage

Transformers library

The model can be run through pipeline API using the following code:

python

from transformers import pipeline

model_id = "cjvt/GaMS3-12B-Instruct"

model = pipeline(
    "text-generation",
    model=model_id,
    device_map="cuda" # replace with "mps" to run on a Mac device
)

# Example of response generation
message = [{"role": "user", "content": "Kateri je najpomembnejši dogodek v slovenski zgodovini?"}]
response = model(message, max_new_tokens=512)
print("Model's response:", response[0]["generated_text"][-1]["content"])

# Example of conversation chain
new_message = response[0]["generated_text"]
new_message.append({"role": "user", "content": "Lahko bolj podrobno opišeš ta dogodek?"})
response = model(new_message, max_new_tokens=1024)
print("Model's response:", response[0]["generated_text"][-1]["content"])

For multi GPU inference, set the device_map to auto (accelerate library required):

python

from transformers import pipeline

model_id = "cjvt/GaMS3-12B-Instruct"

model = pipeline(
    "text-generation",
    model=model_id,
    device_map="auto"
)

# Example of response generation
message = [{"role": "user", "content": "Kateri je najpomembnejši dogodek v slovenski zgodovini?"}]
response = model(message, max_new_tokens=512)
print("Model's response:", response[0]["generated_text"][-1]["content"])

# Example of conversation chain
new_message = response[0]["generated_text"]
new_message.append({"role": "user", "content": "Lahko bolj podrobno opišeš ta dogodek?"})
response = model(new_message, max_new_tokens=1024)
print("Model's response:", response[0]["generated_text"][-1]["content"])

vLLM library

As Gemma 3 architecture is supported in vLLM, this is also true for our model.

NOTE: We noticed degradation in performance when the Flash Infer attention backend is used. For optimal performance please use Flash Attention backend.

Example vLLM code:

python

from vllm import LLM, SamplingParams

model = LLM("cjvt/GaMS3-12B-Instruct")

sampling_params = SamplingParams(
    n=1,
    temperature=0.6,
    top_p=0.9,
    max_tokens=1024
)

messages = [[{"role": "user", "content": "Kateri je najpomembnejši dogodek v slovenski zgodovini?"}]]
response = model.chat(messages, sampling_params)
print("Model's response:", response[0].outputs[0].text)

Training

The training was performed in 3 CPT and 2 SFT stages.

CPT stages:

Parallel alignment: the model was pretrained on parallel English and Slovene texts using a context window of 65536 tokens;
Base CPT: the model was pretrained on a combination of Slovene, English, Croatian, Bosnian and Serbian corpora with a context window of 65536 tokens;
Long CPT: the model was pretrained on a combination of high-quality Slovene, English, Croatian, Bosnian, and Serbian corpora with a context window of 131072 tokens.

SFT stages:

Base instruction-following SFT: the model was trained on a dataset consisting of various tasks (open/closed question answering, writing, math, code) and topics;
Chat and safety tuning: the model was trained on a combination of chat-oriented examples and a small set of safety prompts.

Infrastructure

The model was trained on the following HPC infrastructure:

EuroHPC supercomputer LEONARDO: We managed to scale the training across 128 nodes on LEONARDO's booster partition. We used approximately 150k GPU hours on LEONARDO for development of this model (including data preparation such as translation and web rewrite, and model training).
Faculty's B200 node: With 8 B200 GPUs, our faculty's node represents a modern infrastructure for AI development. However, as we have only a single such node, the majority of the training was done elsewhere. In total, around 1000 GPU hours were used on the B200 node.
NVIDIA DGX Cloud Lepton: A unified AI platform that connects developers to tens of thousands of GPUs from a global network of cloud providers. It addresses a critical need: accelerating AI developer productivity by providing access to GPU capacity and AI services across the NVIDIA compute ecosystem. It integrates seamlessly with the NVIDIA software stack, enabling developers to build, train, and deploy AI applications quickly and Scale. We spent approximately 40k Lepton GPU hours.

Software

CPT: NVIDIA NeMo Framework 2.0 (container version 25.07);
SFT: Transformers library in combination with DeepSpeed and TRL.

Training hyperparameters

In line with our commitment to transparency, open science, and the sharing of knowledge, we openly disclose all training hyperparameters used in developing this model. All training stages were performed with bfloat16 precision and Adam optimizer.

Stage	Model Parallelism	Data Parallelism	Batch Size	Micro Batch Size	LR Scheduler	Min LR	Max LR	Warmup Steps	Constant Steps	Epochs
Parallel alignment	TP 8	64	128	1	Cosine with warmup	5e-7	5e-6	150	200	1
Base CPT	TP 8	64	Rampup: 128 (961 steps) -> 192 (600 steps) -> 256	1	Cosine with warmup	5e-7	5e-6	1000	1000	1
Long CPT	TP 8	16	64	1	Constant with warmup	/	5e-6	500	/	1
Base instruction-following SFT	DeepSpeed ZeRO Stage 2	8	64	8	Cosine with warmup	1e-6	5e-6	1000	0	3 (checkpoint after epoch 2 was selected)
Chat and safety tuning	DeepSpeed ZeRO Stage 2	8	64	8	Cosine with warmup	1e-6	5e-6	1000	0	3 (checkpoint after epoch 2 was selected)

Data and benchmark information

We provide a mixture of datasets used during each of the training stages. During the CPT stages, 99 % of the data was used as a training set, while the remaining percent was used as a validation set. During the SFT stages train/validation split was 90/10. The stats for CPT stages were computed after the initial documents were tokenized, split into units that fit into the context window, merged together using sequence packing and padded to full context window.

Parallel alignment

Corpus	Number of tokens	Number of documents	Total percentage	Short description
DGT	804847616	12281	6.3 %	English, Slovene and Croatian texts extracted from DGT corpus. Cutoff date: 2025 Vol 5.
MaCoCu	430374912	6567	3.4 %	https://www.clarin.si/repository/xmlui/handle/11356/1813
KAS	31391744	479	0.2 %	https://www.clarin.si/repository/xmlui/handle/11356/1449
Wikipedia	11529093120	175920	90.1 %	English Wikipedia retrieved using wikipedia_markdown. Translated into Slovene using GaMS-9B-Translator to create a parallel corpus.
Total	12795707392	195247

Base CPT

Corpus	Language	Number of tokens	Number of documents	Total percentage	Short description
nemotron_pretraining_code	English	1952120832	29787	1.9 %	Subsample of Nemotron-Pretraining-Code-v1. Downloaded git-repositories from Nemotron-Code-Metadata
nemotron_math_4_plus	English	2526937088	38558	2.5 %	Subsample of 4plus split from Nemotron-CC-Math-v1
nemotron_math_3	English	1210908672	18477	1.2 %	Subsample of 3 split from Nemotron-CC-Math-v1
nemotron_pretraining_sft	English	3718316032	56737	3.7 %	Subsample of Nemotron-SFT-General split from Nemotron-Pretraining-SFT-v1
nemotron_high_quality	English	10479403008	159903	10.4 %	Subsample of High-Quality-Synthetic split from Nemotron-CC-v2. Only the examples generated with Qwen3-30B-A3B were considered for selection.
nemotron_diverse_qa	English	8631353344	131704	8.6 %	Subsample of DiverseQA split from Nemotron-CC-v2.
finepdfs_bos	Bosnian	4815912960	73485	4.8 %	Subsample of Bosnian corpus from FinePDFS.
finepdfs_hrv	Croatian	9541124096	145586	9.5 %	Subsample of Croatian corpus from FinePDFS.
finepdfs_srp	Serbian	8119844864	123899	8.0 %	Subsample of Serbian corpus from FinePDFS.
finepdfs_slv	Slovenian	5925044224	90409	5.9 %	Subsample of Slovene corpus from FinePDFS.
trendi	Slovenian	1737687040	26515	1.7 %	https://www.clarin.si/repository/xmlui/handle/11356/2064, Cutoff date: December 2023
classla	Slovenian	4256432128	64948	4.2 %	https://www.clarin.si/repository/xmlui/handle/11356/1882, 1 million randomly selected documents were rewritten using 27B Gemma 3
sl_legal	Slovenian	1697710080	25905	1.7 %	Combination of various Slovene legal data (Legal-Information system of Slovenia, Court practice, Uradni List RS)
sl_med	Slovenian	1598095360	24385	1.6 %	Combination of crawled data, academic works and journals connected to medicine
metafida	Slovenian	4591910912	70067	4.6 %	https://www.clarin.si/repository/xmlui/handle/11356/1775 The following subcorpora were removed: janes_tweet, janes_forum, janes_news, dgt15_sl, classlawiki_sl and tweet_sl
fineweb2	Slovenian	13890289664	211949	13.8 %	Slovene corpus from FineWeb-2
kas	Slovenian	2726035456	41596	2.7 %	https://www.clarin.si/repository/xmlui/handle/11356/1448
nuk_combined	Slovenian	1213267968	18513	1.2 %	OCR-ed data (Marker, Nanonets, Llama 4 Maverick) from the national library of Slovenia. Mostly old newspapers, some books and scientific journals
nuk_doc	Slovenian	11570774016	176556	11.5 %	OCR-ed data (Marker, Nanonets, Llama 4 Maverick) from the national library of Slovenia. Mostly old newspapers, some books and scientific journals
wikipedia_yugo	Slovenian, Croatian, Bosnian, Serbian	673775616	10281	0.7 %	Combination of Slovene, Bosnian, Croatian and Serbian (converted to Latin) wikipedia. Retrieved using wikipedia_markdown. Cutoff date: January 2025
Total		100876943360	1539260

Long

Corpus	Language	Number of tokens	Number of documents	Total percentage	Short description
nemotron_math_4_plus	English	1087373312	8296	5.4 %	Subsample of 4plus split from Nemotron-CC-Math-v1
nemotron_pretraining_sft	English	1231945728	9399	6.1 %	Subsample of Nemotron-SFT-General split from Nemotron-Pretraining-SFT-v1
nemotron_high_quality	English	2634285056	20098	13.1 %	https://huggingface.co/datasets/nvidia/Nemotron-CC-v2
nemotron_diverse_qa	English	1237975040	9445	6.2 %	https://huggingface.co/datasets/nvidia/Nemotron-CC-v2
finepdfs_bos	Bosnian	1614282752	12316	8.0 %	https://huggingface.co/datasets/HuggingFaceFW/finepdfs
finepdfs_hrv	Croatian	2385248256	18198	11.9 %	https://huggingface.co/datasets/HuggingFaceFW/finepdfs
finepdfs_srp	Serbian	2074345472	15826	10.3 %	https://huggingface.co/datasets/HuggingFaceFW/finepdfs
finepdfs_slv	Slovenian	1969618944	15027	9.8 %	https://huggingface.co/datasets/HuggingFaceFW/finepdfs
trendi	Slovenian	610533376	4658	3.0 %	https://www.clarin.si/repository/xmlui/handle/11356/2064, Time window: January 2024 - July 2025
kas_extension	Slovenian	2256404480	17215	11.2 %	Final theses from the three Slovene Universities for years 2019-2024. The theses were crawled from University repositories and OCR-ed with LLama 4 Maverick.
math_sl	Slovenian	1456078848	11109	7.2 %	Combination of 3 sources: translation of nemotron_math_4_plus (using GaMS-9B-Translator) and LLama 4 Maverick OCRs of 2 Slovene math/physics journals: Presek and Obzornik za matematiko in fiziko
nemotron_pretraining_sft_translated	Slovenian	1553858560	11855	7.7 %	Translations of nemotron_pretraining_sft using GaMS-9B-Translator
Total		20111949824	1539260

Base instruction-following SFT

Dataset	Language	Number of train examples	Number of validation examples	Short description
GaMS-Lex	Slovene	1884	0	A small set of lexicographic questions
GaMS-Instruct-ClosedQA	Slovene	10825	1202	ClosedQA generated examples with GPT-4o and Gemini-2.0-Flash. High-quality pre-training documents were used as starting points.
GaMS-Instruct-OpenQA	Slovene	28704	3189	OpenQA generated examples with GPT-4o and Gemini-2.0-Flash. We defined over 400 micro topics.
GaMS-Instruct-Writing	Slovene	9056	1006	Writing generated examples with GPT-4o and Gemini-2.0-Flash.
GaMS-Instruct-DH-1.0	Slovene	9135	1015	https://www.clarin.si/repository/xmlui/handle/11356/1975
Nemotron-SFT-v2-Math-En	English	9000	1000	Subsample of math split from Nemotron Post-Training Dataset v2
Nemotron-SFT-v2-Math-Sl	Slovene	10000	1000	Gemini-2.5-flash translations of subsample (complementary to English part) of math split from Nemotron Post-Training Dataset v2
Nemotron-SFT-v2-STEM-En	English	9000	1000	Subsample of stem split from Nemotron Post-Training Dataset v2
Nemotron-SFT-v2-STEM-Sl	Slovene	10000	1000	Gemini-2.5-flash translations of subsample (complementary to English part) of math split from Nemotron Post-Training Dataset v2
SlCode	Slovene	10000	1000	Subsample of Slovenian Code Feedback dataset
Total		107604	11412

Chat and safety tuning

Dataset	Language	Number of train examples	Number of validation examples	Short description
GaMS-Safety	Slovene	459	0	A small set of safety prompts and responses
Nemotron-Chat	Slovene, English	88126	9791	Downsampled and deduplicated chat split of Nemotron Post-Training Dataset v1. Around 80 % of examples were translated using GaMS-27B-Instruct.
Total		88585	9791

Evaluation

We provide the evaluation using Language Model Evaluation Harness on Slovenian LLM Eval benchmarks. The subset of used benchmarks was manually inspected and corrected by native Slovene speakers. We mark these benchmarks with * in the tables. We compare GaMS3 to the most successful open-source models of similar size.

Slovenian LLM Eval benchmark comparison

Benchmark	Metric	n-shot	GaMS3-12B-Instruct	Gemma-3-12B-IT	Gemma-3-27B-IT	GaMS-9B-Nemotron	GaMS-27B-Nemotron	Zlatorog-12B-Instruct-Beta	EuroLLM-22B-Instruct-2512	Qwen3-30B-A3B-Instruct-2507	Apertus-8B-Instruct-2507	Bielik-11B-v3.0-Instruct
ARC Challenge †	acc	0	0.5265	0.4514	0.5137	0.5171	0.5444	0.4676	0.5026	0.4386	0.4556	0.4923
ARC Easy †	acc	0	0.7744	0.6936	0.7475	0.7546	0.7875	0.7079	0.7433	0.6427	0.7071	0.7231
BoolQ	acc	0	0.8523	0.8526	0.8630	0.8471	0.8618	0.8321	0.8278	0.8590	0.8330	0.8596
GSM8K	exact_match (strict)	5	0.6892	0.7430	0.8006	0.6277	0.6892	0.5921	0.5148	0.7202	0.4375	0.7339
HellaSwag	acc	0	0.5111	0.4728	0.5235	0.5140	0.5428	0.5373	0.4931	0.4256	0.4888	0.5212
OpenBookQA †	acc	0	0.3940	0.3520	0.3700	0.4080	0.3980	0.4060	0.3880	0.2860	0.3420	0.3780
PIQA	acc	0	0.7149	0.6616	0.7106	0.7062	0.7252	0.7247	0.6904	0.6246	0.6893	0.6937
TruthfulQA MC1	acc	6	0.3807	0.3978	0.4076	0.3415	0.3672	0.3647	0.3537	0.3953	0.3917	0.3794
TruthfulQA MC2	acc	6	0.5396	0.5827	0.5881	0.5307	0.5314	0.5383	0.5229	0.5845	0.5574	0.5517
Winogrande †	acc	0	0.7056	0.6559	0.6717	0.7190	0.7206	0.7017	0.6551	0.6204	0.6496	0.6922

† Manually corrected by native Slovene speakers.

Slovenian LLM Eval Leaderboard

The table below shows the average rank for each model across all 10 benchmarks (the lower is better).

Model	Average rank
GaMS-27B-Nemotron	3.05
gemma-3-27b-it	3.20
GaMS3-12B	4.25
Bielik-11B	5.00
GaMS-9B-Nemotron	5.20
Zlatorog-12B	5.60
gemma-3-12b-it	6.30
Qwen3-30B	7.30
EuroLLM-22B	7.50
Apertus-8B	7.60

Slovenian LLM Arena

Similar to well-known chatbot arenas, we created a Slovene-specific LLM Arena. The leaderboard is available here.

Usage and Limitations

These models have certain limitations that users should be aware of.

Intended Usage

Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.

Content Creation and Communication
- Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus, research papers, or reports.
Research and Education
- Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field.
- Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.

Limitations

Training Data
- The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas the model can handle effectively.
Context and Task Complexity
- LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging.
- A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point).
Language Ambiguity and Nuance
- Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language.
Factual Accuracy
- LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements.
Common Sense
- LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations.

Ethical Considerations and Risks

The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following:

Bias and Fairness
- LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card.
Misinformation and Misuse
- LLMs can be misused to generate text that is false, misleading, or harmful.
- Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit].
Transparency and Accountability:
- This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem.

Risks identified and mitigations:

Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases.
Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases.
Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use].
Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques.