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Gemma 7B It by alpindale

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

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Gemma 7B It is an open-source language model by alpindale. Features: 7b LLM, VRAM: 17.1GB, Context: 8K, LLM Explorer Score: 0.12.

  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:2312.11805   Conversational   Endpoints compatible   Gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/alpindale/gemma-7b-it 

Gemma 7B It Benchmarks

LLME Score: 0.12083
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 7B It (alpindale/gemma-7b-it)
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Gemma 7B It Parameters and Internals

Model Type 
text generation, decoder-only
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
Limitations:
Training Data biases, Context and Task complexity, Language Ambiguity, Factual Accuracy, Common Sense
Supported Languages 
English (Full proficiency)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Hardware Used:
TPUv5e
Model Architecture:
decoder-only large language model
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
The results are within acceptable thresholds for internal policies in various categories such as child safety, content safety, representational harms, memorization, and large-scale harms.
Responsible Ai Considerations 
Fairness:
Models underwent careful scrutiny for socio-cultural biases.
Transparency:
Model card summarizes model's architecture, capabilities, limitations, and evaluation processes.
Accountability:
This responsibility lies with Google and developers using the model.
Mitigation Strategies:
Monitoring, de-biasing techniques, content safety mechanisms, developer education.
Input Output 
Input Format:
Text string, such as a question, a prompt, or a document to be summarized.
Output Format:
Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.
LLM NameGemma 7B It
Repository 🤗https://huggingface.co/alpindale/gemma-7b-it 
Model Size7b
Required VRAM17.1 GB
Updated2026-03-29
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

Quantized Models of the Gemma 7B It

Model
Likes
Downloads
VRAM
Gemma 7B 8bit179 GB

Best Alternatives to Gemma 7B It

Best Alternatives
Context / RAM
Downloads
Likes
Kaggle Math Model Gemma V112K / 17.1 GB50
Gemma 1.1 7B It8K / 17.1 GB20065275
Gemma 1.1 7B It8K / 17.1 GB284
SeaLLM 7B V2.58K / 17.1 GB1241050
Gemma 7B It8K / 17.1 GB59010
Zephyr 7B Gemma DPO Avg8K / 17.1 GB150
Zephyr 7B Gemma Rpo Avg8K / 17.1 GB60
... Codegemma 2 7B It Alpaca V1.38K / 17.1 GB91
... 7B Finetuned Sft Navarasa 2.08K / 34 GB31423
Zephyr 7B Gemma V0.18K / 17.1 GB302124
Note: green Score (e.g. "73.2") means that the model is better than alpindale/gemma-7b-it.

Rank the Gemma 7B It 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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