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import triton_python_backend_utils as pb_utils
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import numpy as np
import json
import os
class TritonPythonModel:
def initialize(self, args):
"""
모델이 로드될 때 딱 한 번만 호출됩니다.
`initialize` 함수를 구현하는 것은 선택 사항입니다. 이 함수를 통해 모델은
이 모델과 관련된 모든 상태를 초기화할 수 있습니다.
"""
self.logger = pb_utils.Logger
current_file_path = os.path.abspath(__file__)
self.logger.log_info(f"current_file_path: {current_file_path}")
self.model_name = args["model_name"]
model_repository = args["model_repository"]
model_path = f"{model_repository}/{self.model_name}"
#model_path = "/cheetah/input/model/gemma-3-1b-it/gemma-3-1b-it"
input_model_path = model_path
if os.path.exists(input_model_path):
file_list = os.listdir(input_model_path)
self.logger.log_info(f"'{input_model_path}' 디렉토리의 파일 목록:")
for file_name in file_list:
self.logger.log_info(file_name)
else:
self.logger.log_info(f"'{input_model_path}' 디렉토리가 존재하지 않습니다.")
self.logger.log_info(f"model_repository: {model_repository}")
self.logger.log_info(f"model_path: {model_path}")
self.model_config = json.loads(args["model_config"])
# Hugging Face Transformers 라이브러리에서 사전 학습된 토크나이저를 로드합니다.
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
self.supports_chat_template = self._check_chat_template_support()
# Hugging Face Transformers 라이브러리에서 사전 학습된 언어 모델을 로드합니다.
self.model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_path,
local_files_only=True,
device_map="auto"
)
self.enable_inference_trace = self._get_inference_trace_setting()
self.logger.log_info(f"'{self.model_name}' 모델 초기화 완료")
def execute(self, requests):
"""
Triton이 각 추론 요청에 대해 호출하는 실행 함수입니다.
"""
responses = []
# 각 추론 요청을 순회하며 처리합니다.
for request in requests:
# Triton 입력 파싱
input_text = self._get_input_value(request, "text_input")
text = ""
conversation = ""
input_token_length = 0 # 입력 토큰 길이를 저장할 변수
# 입력 텍스트가 JSON 형식의 대화 기록인지 확인합니다.
try:
conversation = json.loads(input_text)
is_chat = True
self.logger.log_info(f"입력 conversation 출력:\n{conversation}")
except:
# JSON 파싱에 실패하면 일반 텍스트로 처리합니다.
text = input_text
is_chat = False
self.logger.log_info(f"입력 text 출력:\n{text}")
# 입력 텍스트를 토큰화합니다.
if self.supports_chat_template and is_chat:
self.logger.log_info(f"Chat 템플릿을 적용하여 토큰화합니다.")
inputs = self.tokenizer.apply_chat_template(
conversation,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True
).to(device=self.model.device)
else:
self.logger.log_info(f"입력 텍스트를 토큰화합니다.")
inputs = self.tokenizer(
text,
return_tensors="pt").to(device=self.model.device)
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
input_token_length = inputs["input_ids"].shape[-1]
# 언어 모델을 사용하여 텍스트를 생성합니다.
gened = self.model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
generation_config=self._process_generation_config(request),
pad_token_id=self.tokenizer.pad_token_id,
)
# 생성된 토큰 시퀀스를 텍스트로 디코딩하고 입력 텍스트는 제외합니다.
generated_tokens = gened[0][input_token_length:] # 입력 토큰 이후부터 슬라이싱
gened_text = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
self.logger.log_info(f"모델이 생성한 토큰 시퀀스 (입력 텍스트 제외):\n{gened_text}")
output = gened_text.strip()
# 생성된 텍스트를 Triton 출력 텐서로 변환합니다.
output_tensor = pb_utils.Tensor("text_output", np.array(output.encode('utf-8'), dtype=np.bytes_))
# 응답 객체를 생성하고 출력 텐서를 추가합니다.
responses.append(pb_utils.InferenceResponse(output_tensors=[output_tensor]))
return responses
def _process_generation_config(self, request):
"""
추론 요청에서 생성 설정 관련 파라미터들을 추출하여 GenerationConfig 객체를 생성합니다.
Args:
request (pb_utils.InferenceRequest): Triton 추론 요청 객체.
Returns:
transformers.GenerationConfig: GenerationConfig 객체.
"""
max_length = self._get_input_value(request, "max_length", default=20)
max_new_tokens = self._get_input_value(request, "max_new_tokens")
temperature = self._get_input_value(request, "temperature")
do_sample = self._get_input_value(request, "do_sample")
top_k = self._get_input_value(request, "top_k")
top_p = self._get_input_value(request, "top_p")
repetition_penalty = self._get_input_value(request, "repetition_penalty")
stream = self._get_input_value(request, "stream")
generation_config = GenerationConfig(
max_length=max_length,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=do_sample,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
stream=stream,
)
self.logger.log_info(f"추론 요청 GenerationConfig:\n{generation_config}")
return generation_config
def _get_inference_trace_setting(self):
"""
모델 설정(config.pbxt)에서 'enable_inference_trace' 값을 추출하여 반환합니다.
'enable_inference_trace' 설정이 없거나, 올바른 형식이 아닌 경우 기본적으로 False를 반환합니다.
Returns:
bool: 추론 추적 활성화 여부 (True 또는 False).
"""
parameters = self.model_config.get('parameters', {})
trace_config = parameters.get('enable_inference_trace')
if isinstance(trace_config, dict) and 'string_value' in trace_config:
return trace_config['string_value'].lower() == 'true' # 문자열 값을 bool로 변환하여 반환
return False
def _check_chat_template_support(self):
"""
주어진 허깅페이스 Transformer 모델이 Chat 템플릿을 지원하는지 확인하고 결과를 출력합니다.
Returns:
bool: Chat 템플릿 지원 여부 (True 또는 False).
"""
try:
if hasattr(self.tokenizer, "chat_template") and self.tokenizer.chat_template is not None:
self.logger.log_info(f"'{self.model_name}' 모델의 토크나이저는 Chat 템플릿을 지원합니다.")
self.logger.log_info("Chat 템플릿 내용:")
self.logger.log_info(self.tokenizer.chat_template)
return True
else:
self.logger.log_info(f"'{self.model_name}' 모델의 토크나이저는 Chat 템플릿을 직접적으로 지원하지 않거나, Chat 템플릿 정보가 없습니다.")
return False
except Exception as e:
self.logger.log_info(f"'{self.model_name}' 모델의 토크나이저를 로드하는 동안 오류가 발생했습니다: {e}")
return False
def _get_input_value(self, request, input_name: str, default=None):
"""
Triton 추론 요청에서 특정 이름의 입력 텐서 값을 가져옵니다.
Args:
request (pb_utils.InferenceRequest): Triton 추론 요청 객체.
input_name (str): 가져올 입력 텐서의 이름.
default (any, optional): 입력 텐서가 없을 경우 반환할 기본값. Defaults to None.
Returns:
any: 디코딩된 입력 텐서의 값. 텐서가 없으면 기본값을 반환합니다.
"""
tensor_value = pb_utils.get_input_tensor_by_name(request, input_name)
if tensor_value is None:
return default
return self._np_decoder(tensor_value.as_numpy()[0])
def _np_decoder(self, obj):
"""
NumPy 객체의 데이터 타입을 확인하고 Python 기본 타입으로 변환합니다.
Args:
obj (numpy.ndarray element): 변환할 NumPy 배열의 요소.
Returns:
any: 해당 NumPy 요소에 대응하는 Python 기본 타입 (str, int, float, bool).
bytes 타입인 경우 UTF-8로 디코딩합니다.
"""
if isinstance(obj, bytes):
return obj.decode('utf-8')
if np.issubdtype(obj, np.integer):
return int(obj)
if np.issubdtype(obj, np.floating):
return round(float(obj), 3)
if isinstance(obj, np.bool_):
return bool(obj)
def finalize(self):
"""
모델 실행이 완료된 후 Triton 서버가 종료될 때 한 번 호출되는 함수입니다.
`finalize` 함수를 구현하는 것은 선택 사항입니다. 이 함수를 통해 모델은
종료 전에 필요한 모든 정리 작업을 수행할 수 있습니다.
"""
pass

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config.pbtxt
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# Triton backend to use
name: "gemma-3-1b-it"
backend: "python"
max_batch_size: 0
# Triton should expect as input a single string
# input of variable length named 'text_input'
input [
{
name: "text_input"
data_type: TYPE_STRING
dims: [ -1 ]
},
{
name: "max_length"
data_type: TYPE_INT32
dims: [ 1 ]
optional: true
},
{
name: "max_new_tokens"
data_type: TYPE_INT32
dims: [ 1 ]
optional: true
},
{
name: "do_sample"
data_type: TYPE_BOOL
dims: [ 1 ]
optional: true
},
{
name: "top_k"
data_type: TYPE_INT32
dims: [ 1 ]
optional: true
},
{
name: "top_p"
data_type: TYPE_FP32
dims: [ 1 ]
optional: true
},
{
name: "temperature"
data_type: TYPE_FP32
dims: [ 1 ]
optional: true
},
{
name: "repetition_penalty"
data_type: TYPE_FP32
dims: [ 1 ]
optional: true
},
{
name: "stream"
data_type: TYPE_BOOL
dims: [ 1 ]
optional: true
}
]
# Triton should expect to respond with a single string
# output of variable length named 'text_output'
output [
{
name: "text_output"
data_type: TYPE_STRING
dims: [ -1 ]
}
]
parameters: [
{
key: "enable_inference_trace",
value: {string_value: "False"}
}
]
instance_group [
{
kind: KIND_AUTO,
count: 1
}
]

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gemma-3-1b-it/README.md
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---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-1b-pt
---
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
- **Output:**
- Generated text in response to the input, such as an answer to a
question, analysis of image content, or a summary of a document
- Total output context of 8192 tokens
### Usage
Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0.
```sh
$ pip install -U transformers
```
Then, copy the snippet from the section that is relevant for your use case.
#### Running with the `pipeline` API
With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.
```python
from transformers import pipeline
import torch
pipe = pipeline("text-generation", model="google/gemma-3-1b-it", device="cuda", torch_dtype=torch.bfloat16)
messages = [
[
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."},]
},
{
"role": "user",
"content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
},
],
]
output = pipe(messages, max_new_tokens=50)
```
#### Running the model on a single / multi GPU
```python
from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM
import torch
model_id = "google/gemma-3-1b-it"
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
model = Gemma3ForCausalLM.from_pretrained(
model_id, quantization_config=quantization_config
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
[
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."},]
},
{
"role": "user",
"content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
},
],
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device).to(torch.bfloat16)
with torch.inference_mode():
outputs = model.generate(**inputs, max_new_tokens=64)
outputs = tokenizer.batch_decode(outputs)
```
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Reasoning and factuality
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
#### STEM and code
| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
#### Multilingual
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
#### Multimodal
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
- **Child Safety**: Evaluation of text-to-text and image to text prompts
covering child safety policies, including child sexual abuse and
exploitation.
- **Content Safety:** Evaluation of text-to-text and image to text prompts
covering safety policies including, harassment, violence and gore, and hate
speech.
- **Representational Harms**: Evaluation of text-to-text and image to text
prompts covering safety policies including bias, stereotyping, and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance
evaluations" which are our 'arms-length' internal evaluations for responsibility
governance decision making. They are conducted separately from the model
development team, to inform decision making about release. High level findings
are fed back to the model team, but prompt sets are held-out to prevent
overfitting and preserve the results' ability to inform decision making.
Assurance evaluation results are reported to our Responsibility & Safety Council
as part of release review.
### Evaluation Results
For all areas of safety testing, we saw major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models 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.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and 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
- Models 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. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models 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
- Models 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 vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image 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
- VLMs 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 VLM 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
VLMs. 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 certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://goo.gle/Gemma3Report
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805

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