What are the hardware requirements for Gemma 1.1 7B It?

Gemma 1.1 7B It requires approximately 17.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 1.1 7B It and how large is it?

Gemma 1.1 7B It is developed by google, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gemma 1.1 7B It perform on standard benchmarks?

Gemma 1.1 7B It has the following published scores: MMLU 60.92, LMSYS ELO 1099. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gemma 1.1 7B It by google — VRAM 17.1GB, 8K context

Name: Gemma 1.1 7B It
Rating: 3.5 (12 reviews)
Author: google

Gemma 1.1 7B It is an open-source language model by google. Features: 7b LLM, VRAM: 17.1GB, Context: 8K, License: gemma, HF Score: 60.1, LLM Explorer Score: 0.36, ELO: 1099, Arc: 60.1, HellaSwag: 76.1, MMLU: 60.9, TruthfulQA: 50.7, WinoGrande: 69.7, GSM8K: 43.

Model Card on HF 🤗: https://huggingface.co/google/gemma-1.1-7b-it

Gemma 1.1 7B It Benchmarks

LMSys ELO: 1099 vs 1272 (so35)^-13.6%

IFEval: 50.39 vs 88 (so35)^-42.7%

ARC: 60.07 vs 96.7 (so35)^-37.9%

HellaSwag: 76.14 vs 95.3 (gpt4)^-20.1%

MMLU: 60.92 vs 88.3 (so35)^-31%

TruthfulQA: 50.74 vs 59 (gpt4)^-14%

WinoGrande: 69.69 vs 87.5 (gpt4)^-20.4%

GSM8K: 42.99 vs 96.4 (so35)^-55.4%

MATH Lvl 5: 4.91

LLME Score: 0.36415

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Gemma 1.1 7B It (google/gemma-1.1-7b-it)

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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

Primary Use Cases:

NLP Research, Language Learning Tools, Knowledge Exploration

Limitations:

Factual Accuracy, Common Sense

Considerations:

Developers are encouraged to exercise caution and implement appropriate content safety safeguards.

Additional Notes

Training hardware and Tensor Processing Units (TPUs) highlighted along with sustainability focus.

Supported Languages

English (primary language)

Training Details

Data Sources:

Web Documents, Code, Mathematics

Data Volume:

6 trillion tokens

Methodology:

novel RLHF method

Hardware Used:

TPUs

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:

Bias and Fairness, Misinformation and Misuse, Transparency and Accountability

Responsible Ai Considerations

Fairness:

Models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported.

Transparency:

Model card provides architectural, capabilities, limitations, and evaluation details.

Accountability:

Google is leading the model development, dissemination, and documenting processes.

Mitigation Strategies:

Continuous monitoring, content safety safeguards, developer and end-user education.

Input Output

Input Format:

Text string input like a question or document for summarization

Accepted Modalities:

text

Output Format:

Generated English-language text

Performance Tips:

Longer context generally enhances model performance.

Release Notes

Version:

Gemma 1.1 7B (IT)

Notes:

Improved model interaction, upgraded conversational capabilities, bug fixes.

LLM Name	Gemma 1.1 7B It
Repository 🤗	https://huggingface.co/google/gemma-1.1-7b-it
Model Size	7b
Required VRAM	17.1 GB
Updated	2026-04-11
Maintainer	google
Model Type	gemma
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 Architecture	GemmaForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.38.1
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Best Alternatives to Gemma 1.1 7B It

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Kaggle Math Model Gemma V1	12K / 17.1 GB	5	0
Gemma 1.1 7B It	8K / 17.1 GB	28	4
Gemma 7B It	8K / 17.1 GB	1029	10
SeaLLM 7B V2.5	8K / 17.1 GB	12410	50
Zephyr 7B Gemma DPO Avg	8K / 17.1 GB	15	0
Zephyr 7B Gemma Rpo Avg	8K / 17.1 GB	7	0
... Codegemma 2 7B It Alpaca V1.3	8K / 17.1 GB	9	1
Zephyr 7B Gemma V0.1	8K / 17.1 GB	302	124
... 7B Finetuned Sft Navarasa 2.0	8K / 34 GB	314	23
Codegemma 7B It	8K / 17.1 GB	3605	251

Rank the Gemma 1.1 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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Gemma 1.1 7B It by google

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Gemma 1.1 7B It Benchmarks

Gemma 1.1 7B It Parameters and Internals

Best Alternatives to Gemma 1.1 7B It

Rank the Gemma 1.1 7B It Capabilities

What open-source LLMs or SLMs are you in search of? 53254 in total.