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Gemma Terms of Use
Last modified: March 24, 2025
By using, reproducing, modifying, distributing, performing or displaying any portion or element of Gemma, Model Derivatives including via any Hosted Service, (each as defined below) (collectively, the "Gemma Services") or otherwise accepting the terms of this Agreement, you agree to be bound by this Agreement.
Section 1: DEFINITIONS
1.1 Definitions
(a) "Agreement" or "Gemma Terms of Use" means these terms and conditions that govern the use, reproduction, Distribution or modification of the Gemma Services and any terms and conditions incorporated by reference.
(b) "Distribution" or "Distribute" means any transmission, publication, or other sharing of Gemma or Model Derivatives to a third party, including by providing or making Gemma or its functionality available as a hosted service via API, web access, or any other electronic or remote means ("Hosted Service").
(c) "Gemma" means the set of machine learning language models, trained model weights and parameters identified in the Appendix, regardless of the source that you obtained it from.
(d) "Google" means Google LLC.
(e) "Model Derivatives" means all (i) modifications to Gemma, (ii) works based on Gemma, or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Gemma, to that model in order to cause that model to perform similarly to Gemma, including distillation methods that use intermediate data representations or methods based on the generation of synthetic data Outputs by Gemma for training that model. For clarity, Outputs are not deemed Model Derivatives.
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1.2
As used in this Agreement, "including" means "including without limitation".
Section 2: ELIGIBILITY AND USAGE
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Appendix
Gemma 1
Gemma 1.1
Gemma 2
Gemma 3
Gemma 3n
EmbeddingGemma
PaliGemma
PaliGemma 2
ShieldGemma
ShieldGemma 2
CodeGemma
CodeGemma 1.1
Gemma 2 JPN
DataGemma RIG
DataGemma RAG
RecurrentGemma
Gemma Scope
Gemma-APS
T5Gemma
VaultGemma

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"Ahma-2-4B-Instruct is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms"

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---
language:
- fi
license: apache-2.0
tags:
- finnish
- gemma
inference: false
pipeline_tag: text-generation
---
* **Base Model:** [Gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt)
* **Language:** Finnish (fi)
* **Training Methodology:**
* Step 1: Continued Pretraining (CP) Mix of English, Finnish and Code-switching data
* Step 2: Supervised Fine-Tuning (SFT) Mostly Finnish
* Step 3: Direct Preference Optimization (DPO) Mostly Finnish
## Running this model
More info coming later
## Pretraining
More info coming later
## Finetuning
More info coming later
## Evaluation results
### MTBench Finnish
This Ahma-Gemma-3-4B-Instruct-v1.0 model was primarily evaluated using [MTBench Finnish by LumiOpen](https://github.com/LumiOpen/FastChat/tree/main/fastchat/llm_judge)
Single-turn results:
| Benchmark | Ahma 3B base (instruct prompt format) | Ahma 7B Instruct (instruct prompt format) | Ahma-Gemma-3-4B-Instruct-v1.0 |
|:--------------------|:--------------------------------------|:------------------------------------------|:--------------------------------------|
| Coding | 1.00 | 1.00 | 4.2
| Extraction | 1.30 | 3.00 | 7.3
| Humanities | 6.20 | 8.00 | 8.9
| Math | 3.20 | 2.90 | 6.1
| Reasoning | 4.60 | 5.70 | 4.8
| Roleplay | 6.50 | 7.20 | 7.7
| STEM | 5.95 | 7.30 | 9.9
| Writing | 9.00 | 8.80 | 9.2
| **Overall Average** | **4.72** | **5.50** | **7.26**
Multi-turn results:
| Benchmark | Ahma 3B Instruct (instruct prompt format) | Ahma 7B Instruct (instruct prompt format) | Ahma-Gemma-3-4B-Instruct-v1.0 | Poro 34B Chat | Poro-2-8B-Instruct|
|:--------------------|:------------------------------------------|:------------------------------------------|:------------------------------|---------------|-------------------|
| Coding | 1.00 | 1.05 | 4.35 | 3.70 | ? |
| Extraction | 1.15 | 2.65 | 6.55 | 6.37 | ? |
| Humanities | 6.20 | 7.85 | 6.55 | 9.25 | ? |
| Math | 2.70 | 2.40 | 4.80 | 1.20 | ? |
| Reasoning | 3.50 | 4.50 | 4.40 | 4.35 | ? |
| Roleplay | 6.40 | 6.60 | 7.26 | 7.35 | ? |
| STEM | 4.78 | 5.40 | 8.80 | 7.80 | ? |
| Writing | 6.65 | 6.25 | 7.6 | 8.50 | ? |
| **Overall Average** | **4.05** | **4.59** | **6.57** | **6.06** | **6.75** |
As we can see, the Ahma-Gemma-3-4B-Instruct-v1.0 model improves upon our previous model generation. We have already started to work on the datasets and methods to improve this model/scale to bigger models
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
Datacrunch/Verda for sponsoring us some compute for Finetuning:
HF Org (https://huggingface.co/datacrunch)
Website: (https://verda.com/)
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Initial parts in pretraining in our continued pretraining journey
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
- Pretraining this model, post-training this model, gathering datasets, running evaluations
## Other notable supporters on this journey
- Ari Kouhia, [Hugging Face profile](https://huggingface.co/concur-means-risotto) for helpful comments on our WA group and helping in synthetic data generation
- Heikki Saxén, [Hugging Face profile](https://huggingface.co/ducklingcodehouse) for helpful comments on our WA group and also for finetuning DentalQA models on top of this model
- Mikko Hällfors, [Hugging Face profile](https://huggingface.co/Avokid) for helpful comments on our WA group and helping in synthetic data generation
Feel free to contact us for more details 🤗
![Ahma](ahma.jpg)

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{{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
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' -%}
{%- else -%}
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- if (message['role'] == 'assistant') -%}
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{%- elif message['content'] is iterable -%}
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{%- else -%}
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{{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{ '<start_of_turn>model
' }}
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