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Google Gemma 2 27B by SillyTilly

 »  All LLMs  »  SillyTilly  »  Google Gemma 2 27B   URL Share it on

  7   
  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1804.09301   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2009.11462   Arxiv:2101.11718   Arxiv:2103.03874   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Autotrain compatible   Endpoints compatible   Gemma2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/SillyTilly/google-gemma-2-27b 

Google Gemma 2 27B Benchmarks

LLME Score: 0.1626
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Google Gemma 2 27B (SillyTilly/google-gemma-2-27b)
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Google Gemma 2 27B Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
Content Creation and Communication, Research and Education
Primary Use Cases:
Text Generation, Chatbots and Conversational AI, Text Summarization
Limitations:
Biases in training data, Context complexity, Language ambiguity, Factual inaccuracies
Considerations:
Perform continuous monitoring using evaluation metrics and human review, apply de-biasing techniques.
Supported Languages 
English (Full)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
27B model was trained with 13 trillion tokens
Methodology:
Trained with JAX and ML Pathways
Hardware Used:
TPUv5p
Model Architecture:
decoder-only
Safety Evaluation 
Methodologies:
Red-teaming, Human evaluation, Benchmark against relevant academic datasets
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Evaluation Results indicate within acceptable thresholds for meeting internal policies for categories such as child safety, content safety, representational harms, memorization, and large-scale harms.
Responsible Ai Considerations 
Fairness:
LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material.
Transparency:
This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
Accountability:
Developers should monitor for harmful content and biases in outputs.
Mitigation Strategies:
Guidelines for responsible use, content safety mechanisms, adherence to privacy regulations.
Input Output 
Input Format:
Text string, such as a question, a prompt, or a document to be summarized.
Accepted Modalities:
text
Output Format:
Generated English-language text in response to the input.
Performance Tips:
Use appropriate dtype for hardware capabilities, try Flash Attention 2 for performance increases.
LLM NameGoogle Gemma 2 27B
Repository 🤗https://huggingface.co/SillyTilly/google-gemma-2-27b 
Model Size27b
Required VRAM108.3 GB
Updated2025-09-23
MaintainerSillyTilly
Model Typegemma2
Model Files  5.0 GB: 1-of-24   4.5 GB: 2-of-24   4.5 GB: 3-of-24   4.5 GB: 4-of-24   4.5 GB: 5-of-24   4.5 GB: 6-of-24   4.5 GB: 7-of-24   4.5 GB: 8-of-24   4.5 GB: 9-of-24   4.5 GB: 10-of-24   4.5 GB: 11-of-24   4.5 GB: 12-of-24   4.5 GB: 13-of-24   4.5 GB: 14-of-24   4.5 GB: 15-of-24   4.5 GB: 16-of-24   4.5 GB: 17-of-24   4.5 GB: 18-of-24   4.5 GB: 19-of-24   4.5 GB: 20-of-24   4.5 GB: 21-of-24   4.5 GB: 22-of-24   4.5 GB: 23-of-24   4.3 GB: 24-of-24
Model ArchitectureGemma2ForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.42.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than SillyTilly/google-gemma-2-27b.

Rank the Google Gemma 2 27B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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