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 DatasetsCode 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())Deploy This Model
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