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Gemma 7B by alpindale

 »  All LLMs  »  alpindale  »  Gemma 7B   URL Share it on

  98   
  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:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2305.14314   Arxiv:2312.11805   Autotrain compatible   Endpoints compatible   Gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/alpindale/gemma-7b 

Gemma 7B Benchmarks

LLME Score: 0.14141
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 7B (alpindale/gemma-7b)
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Gemma 7B Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
content creation and communication, research and education
Applications:
Text Generation, Chatbots, Text Summarization, NLP Research, Language Learning Tools, Knowledge Exploration
Primary Use Cases:
Generating poems, scripts, code, Powering chatbots, Summarizing documents
Limitations:
context and task complexity, factual accuracy, common sense
Considerations:
Users should be aware of the training data's influence on bias and limitations.
Additional Notes 
The models are designed for Responsible AI development, supporting innovation with open access.
Supported Languages 
English (Full)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Hardware Used:
TPUv5e
Safety Evaluation 
Methodologies:
structured evaluations, red-teaming
Risk Categories:
text-to-text content safety, representational harms, memorization, large-scale harm
Responsible Ai Considerations 
Fairness:
Training data is pre-processed to avoid biases.
Transparency:
The model card provides details on architecture, capabilities, limitations, and evaluation processes.
Accountability:
Google and model developers.
Mitigation Strategies:
Red-teaming, evaluation against benchmarks like BBQ, BOLD.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
English-language text
Performance Tips:
Ensure clear prompts for best results.
LLM NameGemma 7B
Repository 🤗https://huggingface.co/alpindale/gemma-7b 
Model Size7b
Required VRAM17.1 GB
Updated2025-09-22
Maintaineralpindale
Model Typegemma
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   2.1 GB: 4-of-4
Model ArchitectureGemmaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.38.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than alpindale/gemma-7b.

Rank the Gemma 7B 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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