gemma-3-1b-it-ONNX

234
23
FP16
β€”
by
onnx-community
Language Model
OTHER
1B params
New
234 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
3GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Training Data Analysis

🟑 Average (4.3/10)

Researched training datasets used by gemma-3-1b-it-ONNX with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

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

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Transformers.jstext
npm i @huggingface/transformers@next
Transformers.jsjavascriptonnx
import { pipeline } from "@huggingface/transformers";

// Create a text generation pipeline
const generator = await pipeline(
  "text-generation",
  "onnx-community/gemma-3-1b-it-ONNX",
  { dtype: "q4", device: "webgpu" },
);

// Define the list of messages
const messages = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Write me a poem about Machine Learning." },
];

// Generate a response
const output = await generator(messages, { max_new_tokens: 512, do_sample: false });
console.log(output[0].generated_text.at(-1).content);

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