g

396
license:gpl-3.0
by
ataeff
Language Model
OTHER
New
396 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🟡 Average (4.7/10)

Researched training datasets used by g with quality assessment

Specialized For

general
science
multilingual

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...
webtext
🔵 6.5/10
general
Key Strengths
  • •Quality Signal: Human curation through Reddit upvotes
  • •Effective: Produced high-performing GPT-2 model
  • •Influential: Established importance of careful dataset curation
Considerations
  • •Proprietary: Original dataset not publicly available
  • •Limited Size: 40GB relatively small by modern standards
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 ...

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Languages verified workingpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

tokenizer = AutoTokenizer.from_pretrained("ataeff/g")
base = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-270m-it", dtype=torch.bfloat16)
model = PeftModel.from_pretrained(base, "ataeff/g")

prompt = "<start_of_turn>user\nWhat is the meaning of life?<end_of_turn>\n<start_of_turn>model\n"
ids = tokenizer(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=200, temperature=0.7, do_sample=True)
print(tokenizer.decode(out[0], skip_special_tokens=True))

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