gemma-3n-E4B-it-FP8-dynamic

572
3
4.0B
31 languages
—
by
RedHatAI
Language Model
OTHER
4B params
New
572 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
9GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
4GB+ RAM

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by gemma-3n-E4B-it-FP8-dynamic with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Creationpythontransformers
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
from transformers import AutoProcessor, Gemma3nForConditionalGeneration

# Load model.
model_id = "google/gemma-3n-E4B-it"
model = Gemma3nForConditionalGeneration.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Recipe
recipe = [
    QuantizationModifier(
        targets="Linear",
        scheme="FP8_DYNAMIC",
        ignore=[
            "re:.*embed_audio.*",
            "re:.*embed_vision.*",
            "re:.*audio_tower.*",
            "re:.*vision_tower.*",
            "re:.*altup.*",
            "re:.*lm_head.*",
            "re:.*laurel.*",
            "re:model\.language_model\.layers\.\d+\.per_layer_input_gate",
            "re:model\.language_model\.layers\.\d+\.per_layer_projection",
            "model.language_model.per_layer_model_projection",
        ],
    ),
]

SAVE_DIR = f"{model_id.split('/')[1]}-{recipe[0].scheme}"

# Perform oneshot
oneshot(
    model=model,
    tokenizer=model_id,
    recipe=recipe,
    trust_remote_code_model=True,
    tie_word_embeddings=True,
    output_dir=SAVE_DIR,
)

# Save to disk compressed.
model.save_pretrained(SAVE_DIR, save_compressed=True)
processor.save_pretrained(SAVE_DIR)

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.