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Gemma 1.1 7B It by OpenModels4all

 »  All LLMs  »  OpenModels4all  »  Gemma 1.1 7B It   URL Share it on

  7434   
  Arxiv:1705.03551   Arxiv:1804.06876   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:2107.03374   Arxiv:2108.07732   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2312.11805   Autotrain compatible   Conversational   Endpoints compatible   Gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/OpenModels4all/gemma-1.1-7b-it 

Gemma 1.1 7B It Benchmarks

ARC: 59.98 vs 96.7 (so35)-38%
HellaSwag: 76.21 vs 95.3 (gpt4)-20%
MMLU: 60.39 vs 88.3 (so35)-31.6%
TruthfulQA: 50.4 vs 59 (gpt4)-14.6%
WinoGrande: 69.93 vs 87.5 (gpt4)-20.1%
GSM8K: 41.77 vs 96.4 (so35)-56.7%
LLME Score: 0.25888
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 1.1 7B It (OpenModels4all/gemma-1.1-7b-it)

Gemma 1.1 7B It 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 and Conversational AI, Text Summarization, Natural Language Processing (NLP) Research, Language Learning Tools, Knowledge Exploration
Primary Use Cases:
Content Creation, Communication, Research
Limitations:
Training Data Quality, Context and Task Complexity, Language Ambiguity and Nuance, Factual Accuracy, Common Sense
Considerations:
Careful consideration for potential biases and misinformation. Follow guidelines for responsible use.
Additional Notes 
Prohibited uses outlined in the Gemma Prohibited Use Policy.
Supported Languages 
English (fully supported)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
Training using a novel RLHF method
Hardware Used:
TPUv5e
Model Architecture:
Text-to-text, decoder-only
Safety Evaluation 
Methodologies:
Red-teaming, structured evaluations
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Evaluation within acceptable thresholds for meeting internal policies for categories such as child safety, content safety, representational harms, memorization, 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:
Model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
Accountability:
Transparency and outlining measures for responsible usage.
Mitigation Strategies:
Perpetuation of biases: Continuous monitoring, evaluation metrics, human review, and exploration of de-biasing techniques.
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.
Performance Tips:
Provide longer context for better outputs, up to a certain point.
Release Notes 
Version:
Gemma 1.1 7B IT
Date:
unknown
Notes:
Trained using a novel RLHF method, substantial gains in quality and capabilities, bug fixes in multi-turn conversations.
LLM NameGemma 1.1 7B It
Repository 🤗https://huggingface.co/OpenModels4all/gemma-1.1-7b-it 
Model Size7b
Required VRAM17.1 GB
Updated2024-12-21
MaintainerOpenModels4all
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
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.38.1
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 OpenModels4all/gemma-1.1-7b-it.

Rank the Gemma 1.1 7B It 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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Release v20241217
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