GemmaX2-28-9B-v0.1

1.5K
79
8K
GPT-3 class
9.0B
28 languages
β€”
by
ModelSpace
Language Model
OTHER
9B params
New
2K downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
21GB+ RAM
Mobile
Laptop
Server
Quick Summary

GemmaX2-28-9B-v0.1 is an LLM-based translation model. It has been fintuned on GemmaX2-28-9B-Pretrain, which is a language model developed through continual pret...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
9GB+ RAM

Training Data Analysis

🟑 Average (4.3/10)

Researched training datasets used by GemmaX2-28-9B-v0.1 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

Model Performancepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ModelSpace/GemmaX2-28-9B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Translate this from Chinese to English:\nChinese: ζˆ‘ηˆ±ζœΊε™¨ηΏ»θ―‘\nEnglish:"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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