foundation-model ·

gemma-4-31B-it

任务类型：image-text-to-text

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. ## **Models Overview** 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). ### Dense Models | Property | E2B | E4B | 12B Unified | 31B Dense | | :---- | :---- | :---- | :---- | :---- | | **Total Parameters** | 2.3B effective
(5.1B with embeddings) | 4.5B effective
(8B with embeddings) | 11.95B | 30.7B | | **Layers** | 35 | 42 | 48 | 60 | | **Sliding Window** | 512 tokens | 512 tokens | 1024 tokens | 1024 tokens | | **Context Length** | 128K tokens | 128K tokens | 256K tokens | 256K tokens | | **Vocabulary Size** | 262K | 262K | 262K | 262K | | **Supported Modalities** | Text, Image, Audio | Text, Image, Audio | Text, Image, Audio | Text, Image | | **Vision Encoder Parameters** | *~150M* | *~150M* | - | *~550M* | | **Audio Encoder Parameters** | *~300M* | *~300M* | - | No Audio | 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. ### Mixture-of-Experts (MoE) Model | Property | 26B A4B MoE | | :---- | :---- | | **Total Parameters** | 25.2B | | **Active Parameters** | 3.8B | | **Layers** | 30 | | **Sliding Window** | 1024 tokens | | **Context Length** | 256K tokens | | **Vocabulary Size** | 262K | | **Expert Count** | 8 active / 128 total and 1 shared | | **Supported Modalities** | Text, Image | | **Vision Encoder Parameters** | *~550M* | 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. ## **Benchmark Results** 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. | | Gemma 4 31B | Gemma 4 26B A4B | Gemma 4 12B Unified | Gemma 4 E4B | Gemma 4 E2B | Gemma 3 27B (no think) | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | MMLU Pro | 85.2% | 82.6% | 77.2% | 69.4% | 60.0% | 67.6% | | AIME 2026 no tools | 89.2% | 88.3% | 77.5% | 42.5% | 37.5% | 20.8% | | LiveCodeBench v6 | 80.0% | 77.1% | 72.0% | 52.0% | 44.0% | 29.1% | | Codeforces ELO | 2150 | 1718 | 1659 | 940 | 633 | 110 | | GPQA Diamond | 84.3% | 82.3% | 78.8% | 58.6% | 43.4% | 42.4% | | Tau2 (average over 3) | 76.9% | 68.2% | 69.0% | 42.2% | 24.5% | 16.2% | | HLE no tools | 19.5% | 8.7% | 5.2% | - | - | - | | HLE with search | 26.5% | 17.2% | - | - | - | - | | BigBench Extra Hard | 74.4% | 64.8% | 53.0% | 33.1% | 21.9% | 19.3% | | MMMLU | 88.4% | 86.3% | 83.4% | 76.6% | 67.4% | 70.7% | | **Vision** | | | | | | | | MMMU Pro | 76.9% | 73.8% | 69.1% | 52.6% | 44.2% | 49.7% | | OmniDocBench 1.5 (average edit distance, lower is better) | 0.131 | 0.149 | 0.164 | 0.181 | 0.290 | 0.365 | | MATH-Vision | 85.6% | 82.4% | 79.7% | 59.5% | 52.4% | 46.0% | | MedXPertQA MM | 61.3% | 58.1% | 48.7% | 28.7% | 23.5% | - | | **Audio** | | | | | | | | CoVoST | - | - | 38.5^* | 35.54 | 33.47 | - | | FLEURS (lower is better) | - | - | 0.069^* | 0.08 | 0.09 | - | | **Long Context** | | | | | | | | MRCR v2 8 needle 128k (average) | 66.4% | 44.1% | 43.4% | 25.4% | 19.1% | 13.5% | ^*Excluding Chinese language. ## **Core Capabilities** Gemma 4 models handle a broad range of tasks across text, vision, and audio. Key capabilities include: * **Thinking** – Built-in reasoning mode that lets the model think step-by-step before answering. * **Long Context** – Context windows of up to 128K tokens (E2B/E4B) and 256K tokens (12B, 26B A4B/31B). * **Image Understanding** – Object detection, Document/PDF parsing, screen and UI understanding, chart comprehension, OCR (including multilingual), handwriting recognition, and pointing. Images can be processed at variable aspect ratios and resolutions. * **Video Understanding** – Analyze video by processing sequences of frames. * **Interleaved Multimodal Input** – Freely mix text and images in any order within a single prompt. * **Function Calling** – Native support for structured tool use, enabling agentic workflows. * **Coding** – Code generation, completion, and correction. * **Multilingual** – Out-of-the-box support for 35+ languages, pre-trained on 140+ languages. * **Audio** (E2B, E4B, and 12B only) – Automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages. ## Getting Started 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: ```python from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "google/gemma-4-31B-it" # Load model processor = AutoProcessor.from_pretrained(MODEL_ID) model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto" ) ``` Once the model is loaded, you can start generating output: ```python # Prompt messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a short joke about saving RAM."}, ] # Process input inputs = 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 output outputs = model.generate(**inputs, max_new_tokens=1024) response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) # Parse output processor.parse_response(response, prefix=inputs["input_ids"]) ``` 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: Code for processing Audio 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: ```python from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "google/gemma-4-E2B-it" # Load model processor = AutoProcessor.from_pretrained(MODEL_ID) model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto" ) ``` Once the model is loaded, you can start generating output by directly referencing the audio URL in the prompt: ```python # Prompt - add audio after text messages = [ { "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 input inputs = 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 output outputs = model.generate(**inputs, max_new_tokens=512) response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) # Parse output processor.parse_response(response, prefix=inputs["input_ids"]) ``` Code for processing Images Make sure to install the following packages: `pip install -U transformers torch torchvision accelerate` You can then load the model with the code below: ```python from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "google/gemma-4-31B-it" # Load model processor = AutoProcessor.from_pretrained(MODEL_ID) model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto" ) ``` Once the model is loaded, you can start generating output by directly referencing the image URL in the prompt: ```python # Prompt - add image before text messages = [ { "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 input inputs = 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 output outputs = model.generate(**inputs, max_new_tokens=512) response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) # Parse output processor.parse_response(response, prefix=inputs["input_ids"]) ``` Code for processing Videos 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: ```python from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "google/gemma-4-31B-it" # Load model processor = AutoProcessor.from_pretrained(MODEL_ID) model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto" ) ``` Once the model is loaded, you can start generating output by directly referencing the video URL in the prompt: ```python # Prompt - add video before text messages = [ { '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 input inputs = 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 output outputs = model.generate(**inputs, max_new_tokens=512) response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) # Parse output processor.parse_response(response, prefix=inputs["input_ids"]) ``` ## **Best Practices** For the best performance, use these configurations and best practices: ### 1. Sampling Parameters Use the following standardized sampling configuration across all use cases: * `temperature=1.0` * `top_p=0.95` * `top_k=64` ### 2. Thinking Mode Configuration Compared to Gemma 3, the models use standard `system`, `assistant`, and `user` roles. To properly manage the thinking process, use the following control tokens: * **Trigger Thinking:** Thinking is enabled by including the `<|think|>` token at the start of the system prompt. To disable thinking, remove the token. * **Standard Generation:** When thinking is enabled, the model will output its internal reasoning followed by the final answer using this structure: `<|channel>thought\n`**[Internal reasoning]**`` * **Disabled Thinking Behavior:** For all models except for the E2B and E4B variants, if thinking is disabled, the model will still generate the tags but with an empty thought block: `<|channel>thought\n`**[Final answer]** > [!Note] > Note that many libraries like Transformers and llama.cpp handle the complexities of the chat template for you. ### 3. Multi-Turn Conversations * **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final response. Thoughts from previous model turns must *not be added* before the next user turn begins, with the exception of tool call turns where thinking content should be preserved. ### 4. Modality order For optimal performance with multimodal inputs, place: * Image content **before** the text in your prompt. * Audio content **after** the text in your prompt. ### 5. Variable Image Resolution 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. * The supported token budgets are: **70**, **140**, **280**, **560**, and **1120**. * Use *lower budgets* for classification, captioning, or video understanding, where faster inference and processing many frames outweigh fine-grained detail. * Use *higher budgets* for tasks like OCR, document parsing, or reading small text. ### 6. Audio Use the following prompt structures for audio processing: * **Audio Speech Recognition (ASR)** ```text 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. ``` * **Automatic Speech Translation (AST)** ```text 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}. ``` ### 7. Audio and Video Length 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. ## **Model Data** Data used for model training and how the data was processed. ### **Training Dataset** 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: * **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 ens

原始来源：https://huggingface.co/google/gemma-4-31B-it/blob/main/README.md