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# Gemma Terms of Use
The terms below apply to Gemma models listed in the Appendix at bottom of this page. For Gemma 4 terms, see the [Gemma 4 license](https://ai.google.dev/gemma/apache_2).
Last modified: April 1, 2026
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
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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](https://ai.google.dev/gemma/terms#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
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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.
(f) "**Output**" means the information content output of Gemma or a Model
Derivative that results from operating or otherwise using Gemma or the Model
Derivative, including via a Hosted Service.
### 1.2
As used in this Agreement, "**including** " means
"**including without limitation**".
## Section 2: ELIGIBILITY AND USAGE
### 2.1 Eligibility
You represent and warrant that you have the legal capacity to enter into this
Agreement (including being of sufficient age of consent). If you are accessing
or using any of the Gemma Services for or on behalf of a legal entity, (a) you
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You may use, reproduce, modify, Distribute, perform or display any of the Gemma
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## Section 3: DISTRIBUTION AND RESTRICTIONS
### 3.1 Distribution and Redistribution
You may reproduce or Distribute copies of Gemma or Model Derivatives if you meet
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1. You must include the use restrictions referenced in Section 3.2 as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Gemma or Model Derivatives and you must provide notice to subsequent users you Distribute to that Gemma or Model Derivatives are subject to the use restrictions in Section 3.2.
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4. All Distributions (other than through a Hosted Service) must be accompanied by a "**Notice** " text file that contains the following notice: "**Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms**".
You may add your own intellectual property statement to your modifications and,
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### 3.2 Use Restrictions
You must not use any of the Gemma Services:
1. for the restricted uses set forth in the Gemma Prohibited Use Policy at [ai.google.dev/gemma/prohibited_use_policy](https://ai.google.dev/gemma/prohibited_use_policy) ("**Prohibited Use Policy**"), which is hereby incorporated by reference into this Agreement; or
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## Section 4: ADDITIONAL PROVISIONS
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Google may update Gemma from time to time.
### 4.2 Trademarks
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## Appendix
- [Gemma 1](https://ai.google.dev/gemma/docs/core/model_card)
- [Gemma 1.1](https://ai.google.dev/gemma/docs/core/model_card)
- [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2)
- [Gemma 3](https://ai.google.dev/gemma/docs/core/model_card_3)
- [Gemma 3n](https://ai.google.dev/gemma/docs/3n)
- [FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma)
- [EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma)
- [PaliGemma](https://ai.google.dev/gemma/docs/paligemma/model-card)
- [PaliGemma 2](https://ai.google.dev/gemma/docs/paligemma/model-card-2)
- [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma/model_card)
- [ShieldGemma 2](https://ai.google.dev/gemma/docs/shieldgemma/model_card_2)
- [CodeGemma](https://ai.google.dev/gemma/docs/codegemma/model_card)
- [CodeGemma 1.1](https://ai.google.dev/gemma/docs/codegemma/model_card)
- [Gemma 2 JPN](https://huggingface.co/google/gemma-2-2b-jpn-it)
- [DataGemma RIG](https://www.kaggle.com/models/google/datagemma-rig)
- [DataGemma RAG](https://www.kaggle.com/models/google/datagemma-rag)
- [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma/model_card)
- [Gemma Scope](https://ai.google.dev/gemma/docs/gemma_scope)
- [Gemma-APS](https://ai.google.dev/gemma/docs/gemma-aps)
- [T5Gemma](https://www.kaggle.com/models/google/t5gemma)
- [VaultGemma](https://www.kaggle.com/models/google/vaultgemma)
- [FunctionGemma](https://www.kaggle.com/models/google/functiongemma)
- [T5Gemma 2](https://www.kaggle.com/models/google/t5gemma-2)
- [TranslateGemma](https://www.kaggle.com/models/google/translategemma)
> [!NOTE]
> **Note:** Previous versions of these Terms are [archived here](https://ai.google.dev/gemma/terms-archive).

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# Modifications
This repository contains a modified derivative of Google's Gemma model.
## Base Model
- google/gemma-3-1b-it
## Training Distribution
- unsloth/gemma-3-1b-it
## Modification Type
Full-parameter supervised fine-tuning (Full FT)
## Purpose
The model was adapted for Retrieval-Augmented Generation (RAG) as part of
the TSFT-RAG research project.
The fine-tuning objective emphasizes:
- context-grounded question answering
- explicit handling of unsupported questions
- structured JSON generation
- topic extraction
- key-message extraction
- argument extraction
- citation-aware responses
## Technical Changes
The following components were modified:
- model weights
The following components remain derived from the original Gemma release:
- tokenizer
- tokenizer configuration
- model architecture
- vocabulary
- chat template
No architectural changes were made to the original Gemma model.
The resulting model remains fully compatible with the Hugging Face
Transformers ecosystem.

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Gemma Notice
This repository contains a modified derivative of Google's Gemma model.
Original base model:
google/gemma-3-1b-it
Training implementation:
unsloth/gemma-3-1b-it
This model was modified through full-parameter supervised fine-tuning as
part of the TSFT-RAG (Task-Specific Full Fine-Tuning for Retrieval-Augmented
Generation) research project.
Use of this model is subject to the Gemma Terms of Use:
https://ai.google.dev/gemma/terms
Copyright © Google LLC for the original Gemma model.
Modifications © 2026 Sascha Frank.

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---
base_model: google/gemma-3-1b-it
base_model_relation: finetune
language:
- de
- en
library_name: transformers
license: gemma
pipeline_tag: text-generation
tags:
- gemma
- gemma-3
- rag
- retrieval-augmented-generation
- task-specific-fine-tuning
- full-fine-tuning
- structured-output
- research
- tsft-rag
- model-card-v1
---
# TSFT-RAG Gemma-3-1B-IT
> **Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation**
Part of the [TSFT-RAG model family](https://huggingface.co/sascha-frank-ai-research) for Retrieval-Augmented Generation across Gemma, Qwen and Llama architectures.
This repository contains **TSFT-RAG Gemma-3-1B-IT**, a full-parameter
fine-tuned derivative of Google's Gemma-3-1B-IT developed within the
**TSFT-RAG** research project. The objective is to investigate how
task-specific full fine-tuning improves Retrieval-Augmented Generation
(RAG), grounded question answering, unsupported-question detection and
structured information extraction.
------------------------------------------------------------------------
# TSFT-RAG Model Series
| Model | Parameters | Status |
|--------|-----------:|--------|
| **Gemma-3-1B-IT** | **1B** | **Released** |
| [Gemma-3-4B-IT](https://huggingface.co/sascha-frank-ai-research/tsft-rag-gemma-3-4b-it) | 4B | Released |
|[Gemma-3-12B-IT](https://huggingface.co/sascha-frank-ai-research/tsft-rag-gemma-3-12b-it) | 12B | Released |
---
## Abstract
TSFT-RAG Gemma-3-1B-IT is a full-parameter fine-tuned derivative of
Google's Gemma-3-1B-IT model developed for Retrieval-Augmented
Generation (RAG). The objective of the TSFT-RAG project is to
investigate whether relatively small language models can be specialized
for reliable context-grounded answering, explicit abstention, structured
output generation and information extraction through full-parameter
supervised fine-tuning.
## Research Background
Retrieval-Augmented Generation requires different optimization targets
than general-purpose conversational assistants. Besides answering
questions from retrieved evidence, practical RAG systems must reliably
refuse unsupported questions, produce structured outputs and follow
deterministic task instructions.
## Model Details
Property | Value
--------------------------| -----------------------
Model family |TSFT-RAG
Base model |google/gemma-3-1b-it
Training implementation |unsloth/gemma-3-1b-it
Architecture |Gemma3ForCausalLM
Parameters |\~1B
Context length |32,768 tokens
Training sequence length |1,024 tokens
Precision |bfloat16
## Training Procedure
Parameter | Value
----------------------- |-------------------
Epochs | 3
Learning rate | 1e-6
Batch size | 1
Gradient accumulation | 16
Optimizer | Paged AdamW 8-bit
Scheduler | Cosine
Warmup ratio | 0.08
Weight decay | 0.01
Max gradient norm | 1.0
Packing | disabled
Seed | 42
Training runtime: **7 h 52 min**
Final training loss: **1.1361**
## Evaluation
The benchmark contains 1,940 held-out German RAG evaluation instances
covering grounded QA, unsupported questions, topic extraction,
key-message extraction, argument extraction, structured JSON generation
and citation-aware responses.
Metric | Base | TSFT-RAG | Difference
-------------------------- |------- |---------- |------------
Aggregate score | 0.328 | 0.605 | +0.278
Hard-negative handling | 0.020 | 0.815 | +0.795
Grounded QA | 0.509 | 0.395 | -0.114
Analysis: main topic | 0.550 | 0.546 | -0.004
Analysis: key message | 0.538 | 0.493 | -0.046
Analysis: main arguments | 0.404 | 0.546 | +0.142
### Discussion
The largest improvement is observed in unsupported-question handling.
The fine-tuned model rejects unsupported questions substantially more
reliably than the original base model while improving argument
extraction. As expected for task-specific specialization, these
improvements are accompanied by moderate reductions in grounded QA and
key-message extraction. This reflects the intended optimization
objective rather than a general capability increase.
## Intended Use
- Local RAG systems
- Enterprise knowledge assistants
- University question answering
- Structured information extraction
- Research on task-specific fine-tuning
## Reproducibility
The model was trained using the Unsloth implementation of Gemma with
full-parameter supervised fine-tuning and the Hugging Face Transformers
ecosystem.
## Limitations
Performance outside the evaluated task formats has not been
systematically studied. The model inherits limitations of the original
Gemma base model and may generate incorrect or unsupported information.
## License
This repository contains a modified derivative of Google's Gemma model.
Use is subject to the Gemma Terms of Use. See LICENSE, NOTICE and
MODIFICATIONS.md.
## Citation
A formal paper citation will be added after publication. Until then,
please cite the accompanying software repository.
# Project
The complete TSFT-RAG research project, including training scripts,
evaluation pipeline, benchmark methodology and documentation, is available at:
**Project repository**
https://github.com/frankmst/rag-task-specific-full-finetuning
**TSFT-RAG model family**
https://huggingface.co/sascha-frank-ai-research
---
# Author
**Sascha Frank**
Independent AI Researcher
**ORCID**
https://orcid.org/0000-0002-0588-0081
**GitHub**
https://github.com/frankmst
**Hugging Face**
https://huggingface.co/sascha-frank-ai-research
## Links
- https://github.com/frankmst/rag-task-specific-full-finetuning
- https://sascha-frank.com

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{{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
{%- if messages[0]['content'] is string -%}
{%- set first_user_prefix = messages[0]['content'] + '
' -%}
{%- else -%}
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
' -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<start_of_turn>' + role + '
' + (first_user_prefix if loop.first else "") }}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'image' -%}
{{ '<start_of_image>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<start_of_turn>model
'}}
{%- endif -%}

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{
"_sliding_window_pattern": 6,
"architectures": [
"Gemma3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": null,
"bos_token_id": 2,
"cache_implementation": "hybrid",
"dtype": "bfloat16",
"eos_token_id": 106,
"final_logit_softcapping": null,
"head_dim": 256,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 1152,
"initializer_range": 0.02,
"intermediate_size": 6912,
"layer_types": [
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention"
],
"max_position_embeddings": 32768,
"model_type": "gemma3_text",
"num_attention_heads": 4,
"num_hidden_layers": 26,
"num_key_value_heads": 1,
"pad_token_id": 0,
"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"full_attention": {
"rope_theta": 1000000,
"rope_type": "default"
},
"sliding_attention": {
"rope_theta": 10000,
"rope_type": "default"
}
},
"sliding_window": 512,
"sliding_window_pattern": 6,
"tie_word_embeddings": true,
"transformers_version": "5.7.0",
"unsloth_fixed": true,
"use_bidirectional_attention": false,
"use_cache": false,
"vocab_size": 262144
}

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{
"bos_token_id": 2,
"cache_implementation": "hybrid",
"do_sample": true,
"eos_token_id": [
1,
106
],
"max_length": 32768,
"pad_token_id": 0,
"top_k": 64,
"top_p": 0.95,
"transformers_version": "5.7.0"
}

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{
"backend": "tokenizers",
"boi_token": "<start_of_image>",
"bos_token": "<bos>",
"clean_up_tokenization_spaces": false,
"eoi_token": "<end_of_image>",
"eos_token": "<end_of_turn>",
"image_token": "<image_soft_token>",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 32768,
"model_specific_special_tokens": {
"boi_token": "<start_of_image>",
"eoi_token": "<end_of_image>",
"image_token": "<image_soft_token>"
},
"pad_token": "<pad>",
"padding_side": "left",
"processor_class": "Gemma3Processor",
"sp_model_kwargs": null,
"spaces_between_special_tokens": false,
"tokenizer_class": "GemmaTokenizer",
"unk_token": "<unk>",
"use_default_system_prompt": false
}

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