Gemma 7B by google

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  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   Gguf   Quantized   Region:us   Safetensors   Sharded   Tensorflow

Gemma 7B Benchmarks

Gemma 7B Parameters and Internals

Model Type
text-to-text, decoder-only, large language models
Use Cases
Areas:
Content Creation and Communication, Research and Education
Applications:
Text Generation, Chatbots and Conversational AI, Text Summarization, Natural Language Processing Research, Language Learning Tools, Knowledge Exploration
Primary Use Cases:
Question answering, Summarization, Reasoning
Limitations:
Biases from training data, Complex task handling limitations, Figurative language and nuances issues, Factual inaccuracies
Considerations:Continuous monitoring, content safety guidelines, and education around privacy.
Supported Languages
English (full)
Training Details
Data Sources:
Web Documents, Code, Mathematics
Data Volume:6 trillion tokens
Context Length:8192
Hardware Used:
TPUv5e
Model Architecture:text-to-text, decoder-only large language model
Safety Evaluation
Methodologies:
structured evaluations, internal red-teaming
Findings:
acceptable thresholds for internal policies
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:Bias and Fairness, Misinformation, Transparency
Responsible Ai Considerations
Fairness:The model underwent input data pre-processing and evaluations to assess socio-cultural biases.
Transparency:Summarizes details on architecture, capabilities, and limitations.
Accountability:Open model development aims to share innovation with developers and researchers.
Mitigation Strategies:De-biasing techniques, content safety mechanisms, and end-user education.
Input Output
Input Format:Text string
Accepted Modalities:
text
Output Format:Generated English-language text
LLM NameGemma 7B
Repository ๐Ÿค—https://huggingface.co/google/gemma-7b 
Base Model(s)  Gemma Ei Oc Structured Train   Holmeister/Gemma_ei_oc_structured_train
Model Size7b
Required VRAM17.1 GB
Updated2024-11-09
Maintainergoogle
Model Typegemma
Model Files  34.2 GB   5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   2.1 GB: 4-of-4
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureGemmaForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.38.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16
Gemma 7B (google/gemma-7b)

Quantized Models of the Gemma 7B

Model
Likes
Downloads
VRAM
Gemma 7B It113746717317 GB
Train060499 GB
CNCF0245 GB
Gemma 7B GGUF103203 GB

Best Alternatives to Gemma 7B

Best Alternatives
Context / RAM
Downloads
Likes
Gemma 7B It8K / 17.1 GB4671731137
Gemma 1.1 7B It GGUF8K / 5.3 GB631
Train068K / 9.1 GB490
Llama2 Kazakh 7B GGUF8K / 4.1 GB180
Gemma 7B8K / 17.1 GB70
Gemma 7B Translator 0.48K / 17.1 GB260
Gemma 7B Translator 0.38K / 17.1 GB120
Gemma 7B It GGUF8K / 3.1 GB44311
Gemma7B Konosuba8K / 17.1 GB80
Gemma 7B It GGUF8K / 5.3 GB481
Note: green Score (e.g. "73.2") means that the model is better than google/gemma-7b.

Rank the Gemma 7B Capabilities

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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 v20241110