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License: Apache 2.0 | Authors: Google DeepMind
[!Note] This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. Four versions of the QAT checkpoints are available:
- Unquantized QAT checkpoints (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models.
- GGUF (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.
- Mobile-optimized (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B.
- Compressed Tensors (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B, and 31B.
Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.
Gemma 4 introduces key capability and architectural advancements:
Reasoning – All models in the family are designed as highly capable reasoners, with configurable thinking modes.
Extended Multimodalities – Processes Text, Image with variable aspect ratio and resolution support (all models), Video, and Audio (featured natively on the E2B, E4B, and 12B models).
Diverse & Efficient Architectures – Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.
Optimized for On-Device – Smaller models are specifically designed for efficient local execution on laptops and mobile devices.
Increased Context Window – The small models feature a 128K context window, while the medium models support 256K.
Enhanced Coding & Agentic Capabilities – Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents.
Native System Prompt Support – Gemma 4 introduces native support for the system role, enabling more structured and controllable conversations.
Gemma 4 models are designed to deliver frontier-level performance at each size, targeting deployment scenarios from mobile and edge devices (E2B, E4B) to consumer GPUs and workstations (12B, 26B A4B, 31B). They are well-suited for reasoning, agentic workflows, coding, and multimodal understanding.
The models employ a hybrid attention mechanism that interleaves local sliding window attention with full global attention, ensuring the final layer is always global. This hybrid design delivers the processing speed and low memory footprint of a lightweight model without sacrificing the deep awareness required for complex, long-context tasks. To optimize memory for long contexts, global layers feature unified Keys and Values, and apply Proportional RoPE (p-RoPE).
The "E" in E2B and E4B stands for "effective" parameters. The smaller models incorporate Per-Layer Embeddings (PLE) to maximize parameter efficiency in on-device deployments. Rather than adding more layers or parameters to the model, PLE gives each decoder layer its own small embedding for every token. These embedding tables are large but are only used for quick lookups, which is why the effective parameter count is much smaller than the total.
The "Unified" in Gemma 4 12B Unified refers to its encoder-free architecture. Other Gemma 4 models use dedicated encoders to process multimodal data before passing it to the LLM. Gemma 4 12B eliminates these encoders entirely, projecting raw image patches and audio waveforms directly into the LLM's embedding space through lightweight linear layers. This unified approach means all modalities flow straight into a single decoder-only transformer, reducing multimodal latency and allowing the entire model to be fine-tuned in one pass.
The "A" in 26B A4B stands for "active parameters" in contrast to the total number of parameters the model contains. By only activating a 4B subset of parameters during inference, the Mixture-of-Experts model runs much faster than its 26B total might suggest. This makes it an excellent choice for fast inference compared to the dense 31B model since it runs almost as fast as a 4B-parameter model.
These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation. Evaluation results marked in the table are for instruction-tuned models.
*Excluding Chinese language.
Gemma 4 models handle a broad range of tasks across text, vision, and audio. Key capabilities include:
You can use all Gemma 4 models with the latest version of Transformers. To get started, install the necessary dependencies in your environment:
pip install -U transformers torch accelerate
Once you have everything installed, you can proceed to load the model with the code below:
Once the model is loaded, you can start generating output:
To enable reasoning, set enable_thinking=True and the parse_response function will take care of parsing the thinking output.
Below, you will also find snippets for processing audio (E2B, E4B, 12B only), images, and video alongside text:
Make sure to install the following packages:
pip install -U transformers torch torchvision librosa accelerate
You can then load the model with the code below:
Once the model is loaded, you can start generating output by directly referencing the audio URL in the prompt:
Make sure to install the following packages:
pip install -U transformers torch torchvision accelerate
You can then load the model with the code below:
Once the model is loaded, you can start generating output by directly referencing the image URL in the prompt:
Make sure to install the following packages:
pip install -U transformers torch torchvision librosa accelerate
You can then load the model with the code below:
Once the model is loaded, you can start generating output by directly referencing the video URL in the prompt:
For the best performance, use these configurations and best practices:
Use the following standardized sampling configuration across all use cases:
temperature=1.0top_p=0.95top_k=64Compared to Gemma 3, the models use standard system, assistant, and user roles. To properly manage the thinking process, use the following control tokens:
<|think|> token at the start of the system prompt. To disable thinking, remove the token.<|channel>thought\n[Internal reasoning]<channel|><|channel>thought\n<channel|>[Final answer][!Note] Note that many libraries like Transformers and llama.cpp handle the complexities of the chat template for you.
For optimal performance with multimodal inputs, place:
Aside from variable aspect ratios, Gemma 4 supports variable image resolution through a configurable visual token budget, which controls how many tokens are used to represent an image. A higher token budget preserves more visual detail at the cost of additional compute, while a lower budget enables faster inference for tasks that don't require fine-grained understanding.
Use the following prompt structures for audio processing:
All models support image inputs and can process videos as frames whereas the E2B, E4B, and 12B models also support audio inputs. Audio supports a maximum length of 30 seconds. Video supports a maximum of 60 seconds assuming the images are processed at one frame per second.
Data used for model training and how the data was processed.
Our pre-training dataset is a large-scale, diverse collection of data encompassing a wide range of domains and modalities, which includes web documents, code, images, audio, with a cutoff date of January 2025. Here are the key components:
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.
Here are the key data cleaning and filtering methods applied to the training data:
As open models become central to enterprise infrastructure, provenance and security are paramount. Developed by Google DeepMind, Gemma 4 undergoes the same rigorous safety evaluations as our proprietary Gemini models.
Gemma 4 models were developed in partnership with internal safety and responsible AI teams. A range of automated as well as human evaluations were conducted to help improve model safety. These evaluations align with Google’s AI principles, as well as safety policies, which aim to prevent our generative AI models from generating harmful content, including:
For all areas of safety testing, we saw major improvements in all categories of content safety relative to previous Gemma models. Overall, Gemma 4 models significantly outperform Gemma 3 and 3n models in improving safety, while keeping unjustified refusals low. 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.
These models have certain limitations that users should be aware of.
Multimodal models (capable of processing vision, language, and/or audio) 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.
The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following:
Risks identified and mitigations:
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.
from transformers import AutoProcessor, AutoModelForMultimodalLM
MODEL_ID = "google/gemma-4-12B-it"
# Load modelprocessor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto")# Promptmessages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a short joke about saving RAM."},]
# Process inputinputs = processor.apply_chat_template( messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True, enable_thinking=False).to(model.device)input_len = inputs["input_ids"].shape[-1]
# Generate outputoutputs = model.generate(**inputs, max_new_tokens=1024)response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
# Parse outputprocessor.parse_response(response)from transformers import AutoProcessor, AutoModelForMultimodalLM
MODEL_ID = "google/gemma-4-12B-it"
# Load modelprocessor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto")# Prompt - add audio after textmessages = [ { "role": "user", "content": [ {"type": "text", "text": "Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three."}, {"type": "audio", "audio": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/journal1.wav"}, ] }]
# Process inputinputs = processor.apply_chat_template( messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True,).to(model.device)input_len = inputs["input_ids"].shape[-1]
# Generate outputoutputs = model.generate(**inputs, max_new_tokens=512)response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
# Parse outputprocessor.parse_response(response)from transformers import AutoProcessor, AutoModelForMultimodalLM
MODEL_ID = "google/gemma-4-12B-it"
# Load modelprocessor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto")# Prompt - add image before textmessages = [ { "role": "user", "content": [ {"type": "image", "url": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png"}, {"type": "text", "text": "What is shown in this image?"} ] }]
# Process inputinputs = processor.apply_chat_template( messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True,).to(model.device)input_len = inputs["input_ids"].shape[-1]
# Generate outputoutputs = model.generate(**inputs, max_new_tokens=512)response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
# Parse outputprocessor.parse_response(response)from transformers import AutoProcessor, AutoModelForMultimodalLM
MODEL_ID = "google/gemma-4-12B-it"
# Load modelprocessor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto")# Prompt - add video before textmessages = [ { 'role': 'user', 'content': [ {"type": "video", "video": "https://github.com/bebechien/gemma/raw/refs/heads/main/videos/ForBiggerBlazes.mp4"}, {'type': 'text', 'text': 'Describe this video.'} ] }]
# Process inputinputs = processor.apply_chat_template( messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True,).to(model.device)input_len = inputs["input_ids"].shape[-1]
# Generate outputoutputs = model.generate(**inputs, max_new_tokens=512)response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
# Parse outputprocessor.parse_response(response)Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
Follow these specific instructions for formatting the answer:* Only output the transcription, with no newlines.* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.