---
title: Hectron Web
emoji: 👀
colorFrom: yellow
colorTo: indigo
sdk: static
pinned: true
license: llama3.1
short_description: Hectron Web
---
library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: image-text-to-text
---
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License: Apache 2.0 | Authors: Google DeepMind
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 small models) 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 four distinct sizes: **E2B**, **E4B**, **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 and E4B 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 (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 | 31B Dense |
| :---- | :---- | :---- | :---- |
| **Total Parameters** | 2.3B effective (5.1B with embeddings) | 4.5B effective (8B with embeddings) | 30.7B |
| **Layers** | 35 | 42 | 60 |
| **Sliding Window** | 512 tokens | 512 tokens | 1024 tokens |
| **Context Length** | 128K tokens | 128K tokens | 256K tokens |
| **Vocabulary Size** | 262K | 262K | 262K |
| **Supported Modalities** | 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.
### 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 E4B | Gemma 4 E2B | Gemma 3 27B (no think) |
| :---- | :---- | :---- | :---- | :---- | :---- |
| MMLU Pro | 85.2% | 82.6% | 69.4% | 60.0% | 67.6% |
| AIME 2026 no tools | 89.2% | 88.3% | 42.5% | 37.5% | 20.8% |
| LiveCodeBench v6 | 80.0% | 77.1% | 52.0% | 44.0% | 29.1% |
| Codeforces ELO | 2150 | 1718 | 940 | 633 | 110 |
| GPQA Diamond | 84.3% | 82.3% | 58.6% | 43.4% | 42.4% |
| Tau2 (average over 3) | 76.9% | 68.2% | 42.2% | 24.5% | 16.2% |
| HLE no tools | 19.5% | 8.7% | - | - | - |
| HLE with search | 26.5% | 17.2% | - | - | - |
| BigBench Extra Hard | 74.4% | 64.8% | 33.1% | 21.9% | 19.3% |
| MMMLU | 88.4% | 86.3% | 76.6% | 67.4% | 70.7% |
| **Vision** | | | | | |
| MMMU Pro | 76.9% | 73.8% | 52.6% | 44.2% | 49.7% |
| OmniDocBench 1.5 (average edit distance, lower is better) | 0.131 | 0.149 | 0.181 | 0.290 | 0.365 |
| MATH-Vision | 85.6% | 82.4% | 59.5% | 52.4% | 46.0% |
| MedXPertQA MM | 61.3% | 58.1% | 28.7% | 23.5% | - |
| **Audio** | | | | | |
| CoVoST | - | - | 35.54 | 33.47 | - |
| FLEURS (lower is better) | - | - | 0.08 | 0.09 | - |
| **Long Context** | | | | | |
| MRCR v2 8 needle 128k (average) | 66.4% | 44.1% | 25.4% | 19.1% | 13.5% |
## **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 (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 and E4B 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, AutoModelForCausalLM
MODEL_ID = "google/gemma-4-31B-it"
# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.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
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
inputs = processor(text=text, return_tensors="pt").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)
```
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 and E4B only), images, and video alongside text:
Code for processing Audio
Instead of using `AutoModelForCausalLM`, you can use `AutoModelForMultimodalLM` to process audio. To use it, make sure to install the following packages:
`pip install -U transformers torch 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 before text
messages = [
{
"role": "user",
"content": [
{"type": "audio", "audio": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/journal1.wav"},
{"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."},
]
}
]
# 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)
```
Code for processing Images
Instead of using `AutoModelForCausalLM`, you can use `AutoModelForMultimodalLM` to process images. To use it, 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/Demos/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)
```
Code for processing Videos
Instead of using `AutoModelForCausalLM`, you can use `AutoModelForMultimodalLM` to process videos. To use it, make sure to install the following packages:
`pip install -U transformers torch torchvision torchcodec 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)
```
## **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.
### 4. Modality order
* For optimal performance with multimodal inputs, place image and/or audio content **before** 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 and E4B 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 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](https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf).
## **Ethics and Safety**
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.
### **Evaluation Approach**
Gemma-Hectron4 models were developed
