gemma-2-text-rewriter-gguf

6
by
Heatw4ve
Language Model
OTHER
9B params
New
6 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
21GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

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 gemma-2-text-rewriter-gguf 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

User Prompt Formatcode
llm = Llama(
  model_path="/path/to/your/model",
  n_ctx=8192,
  n_gpu_layers=16,
  n_threads=4,
  verbose=verbose,
  chat_format="gemma",
)

text='''
William Shakespeare, the master of human insight, gifted us with a timeless observation that cuts to the heart of intellect and humility: "The fool doth think he is wise, but the wise man knows himself to be a fool." Uttered by the character Touchstone in As You Like It, this seemingly paradoxical statement is far more than a witty quip; it's a profound commentary on self-awareness, the nature of true wisdom, and the perpetual quest for knowledge.
'''

messages = [
  {"role": "system", "content": "You are a helpful assistant that rewrites AI-toned text into natural, human-like writing."},
  {"role": "user", "content": f"Rewrite the following text to sound like a real human wrote it:\n\n{text}"}
]

output = llm.create_chat_completion(
  messages=messages,
  max_tokens=512,
  temperature=random.uniform(0.9, 1.9),
  top_p=random.uniform(0.87, 0.96),
  seed=random.randint(2, 2**32),
  stop=["<end_of_turn>", "<eos>", "</s>"]
)

print(output["choices"][0]["message"]["content"].strip())

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