gemma-4-E2B-it-litert-lm

Try Gemma 4 E2B

Build with Gemma 4 E2B and LiteRT-LM

Gemma 4 E2B Performance on LiteRT-LM

• NPU model is benchmarked with 4096 context length

Gemma 4 E2B on Web

• GPU memory measured by "GPU Process" memory for all of Chrome while running. Was 130MB when inactive, before any model loading took place.
• CPU memory measured for the entire tab while running. Was 55MB when inactive, before any model loading took place.

• 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

Dense Models

Mixture-of-Experts (MoE) Model

Benchmark Results

Core Capabilities

• 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

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 torchvision librosa accelerate

You can then load the model with the code below:

python

1from transformers import AutoProcessor, AutoModelForMultimodalLM 2 3MODEL_ID = "google/gemma-4-E2B-it" 4 5# Load model 6processor = AutoProcessor.from_pretrained(MODEL_ID) 7model = AutoModelForMultimodalLM.from_pretrained( 8 MODEL_ID, 9 dtype="auto", 10 device_map="auto" 11)

Once the model is loaded, you can start generating output by directly referencing the audio URL in the prompt:

python

1# Prompt - add audio before text 2messages = [ 3 { 4 "role": "user", 5 "content": [ 6 {"type": "audio", "audio": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/journal1.wav"}, 7 {"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."}, 8 ] 9 } 10] 11 12# Process input 13inputs = processor.apply_chat_template( 14 messages, 15 tokenize=True, 16 return_dict=True, 17 return_tensors="pt", 18 add_generation_prompt=True, 19).to(model.device) 20input_len = inputs["input_ids"].shape[-1] 21 22# Generate output 23outputs = model.generate(**inputs, max_new_tokens=512) 24response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) 25 26# Parse output 27processor.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

1from transformers import AutoProcessor, AutoModelForMultimodalLM 2 3MODEL_ID = "google/gemma-4-E2B-it" 4 5# Load model 6processor = AutoProcessor.from_pretrained(MODEL_ID) 7model = AutoModelForMultimodalLM.from_pretrained( 8 MODEL_ID, 9 dtype="auto", 10 device_map="auto" 11)

Once the model is loaded, you can start generating output by directly referencing the image URL in the prompt:

python

1# Prompt - add image before text 2messages = [ 3 { 4 "role": "user", "content": [ 5 {"type": "image", "url": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/GoldenGate.png"}, 6 {"type": "text", "text": "What is shown in this image?"} 7 ] 8 } 9] 10 11# Process input 12inputs = processor.apply_chat_template( 13 messages, 14 tokenize=True, 15 return_dict=True, 16 return_tensors="pt", 17 add_generation_prompt=True, 18).to(model.device) 19input_len = inputs["input_ids"].shape[-1] 20 21# Generate output 22outputs = model.generate(**inputs, max_new_tokens=512) 23response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) 24 25# Parse output 26processor.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 librosa accelerate

You can then load the model with the code below:

python

1from transformers import AutoProcessor, AutoModelForMultimodalLM 2 3MODEL_ID = "google/gemma-4-E2B-it" 4 5# Load model 6processor = AutoProcessor.from_pretrained(MODEL_ID) 7model = AutoModelForMultimodalLM.from_pretrained( 8 MODEL_ID, 9 dtype="auto", 10 device_map="auto" 11)

Once the model is loaded, you can start generating output by directly referencing the video URL in the prompt:

python

1# Prompt - add video before text 2messages = [ 3 { 4 'role': 'user', 5 'content': [ 6 {"type": "video", "video": "https://github.com/bebechien/gemma/raw/refs/heads/main/videos/ForBiggerBlazes.mp4"}, 7 {'type': 'text', 'text': 'Describe this video.'} 8 ] 9 } 10] 11 12# Process input 13inputs = processor.apply_chat_template( 14 messages, 15 tokenize=True, 16 return_dict=True, 17 return_tensors="pt", 18 add_generation_prompt=True, 19).to(model.device) 20input_len = inputs["input_ids"].shape[-1] 21 22# Generate output 23outputs = model.generate(**inputs, max_new_tokens=512) 24response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) 25 26# Parse output 27processor.parse_response(response)

Best Practices

1. Sampling Parameters

• temperature=1.0
• top_p=0.95
• top_k=64

2. Thinking Mode Configuration

• 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]<channel|>
• 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<channel|>[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

• 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

• Audio Speech Recognition (ASR)

• Automatic Speech Translation (AST)

7. Audio and Video Length

Model Data

Training Dataset

• 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.

Data Preprocessing

• 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.

Ethics and Safety

Evaluation Approach

• Content related to child sexual abuse material and exploitation
• Dangerous content (e.g., promoting suicide, or instructing in activities that could cause real-world harm)
• Sexually explicit content
• Hate speech (e.g., dehumanizing members of protected groups)
• Harassment (e.g., encouraging violence against people)

Evaluation Results

Usage and Limitations

Intended Usage

• 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.
  • Audio Processing and Interaction: The smaller models (E2B and E4B) can analyze and interpret audio inputs, enabling voice-driven interactions and transcriptions.
• 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 perform well on 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

• Bias and Fairness
  • VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. Gemma 4 models underwent careful scrutiny, input data pre-processing, and post-training evaluations as reported in this card to help mitigate the risk of these biases.
• 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.
• 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.

• 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.
• 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.
• 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.

Benefits