🧠
gemma-7b model by google
⭐ 0.7
🧠

gemma-7b

by google Model ID: hf-model--google--gemma-7b
FNI 0.7
Top 70%
🔗 View Source
Audited 0.7 FNI Score
8.54B Params
4k Context
Hot 52.4K Downloads
8G GPU ~8GB Est. VRAM

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run gemma-7b
🤗 HF Download
huggingface-cli download google/gemma-7b
đŸ“Ļ Install Lib
pip install -U transformers
📊

Engineering Specs

⚡ Hardware

Parameters
8.54B
Architecture
GemmaForCausalLM
Context Length
4K
Model Size
200.1GB

🧠 Lifecycle

Library
-
Precision
float16
Tokenizer
-

🌐 Identity

Source
HuggingFace
License
Open Access
💾

Est. VRAM Benchmark

~7.7GB

Analyze Hardware

* Technical estimation for FP16/Q4 weights. Does not include OS overhead or long-context batching. For Technical Reference Only.

🕸ī¸ Neural Mesh Hub

Interconnecting Research, Data & Ecosystem

đŸ”Ŧ

đŸ”Ŧ Research & Data

📈 Interest Trend

--

* Real-time activity index across HuggingFace, GitHub and Research citations.

🔍 Semantic Keywords

🏷ī¸ transformers 🏷ī¸ safetensors 🏷ī¸ gguf 🏷ī¸ gemma 🏷ī¸ text-generation 🏷ī¸ arxiv:2305.14314 🏷ī¸ arxiv:2312.11805 🏷ī¸ arxiv:2009.03300 🏷ī¸ arxiv:1905.07830 🏷ī¸ arxiv:1911.11641 🏷ī¸ arxiv:1904.09728 🏷ī¸ arxiv:1905.10044 🏷ī¸ arxiv:1907.10641 🏷ī¸ arxiv:1811.00937 🏷ī¸ arxiv:1809.02789 🏷ī¸ arxiv:1911.01547 🏷ī¸ arxiv:1705.03551 🏷ī¸ arxiv:2107.03374 🏷ī¸ arxiv:2108.07732 🏷ī¸ arxiv:2110.14168 🏷ī¸ arxiv:2304.06364 🏷ī¸ arxiv:2206.04615 🏷ī¸ arxiv:1804.06876 🏷ī¸ arxiv:2110.08193 🏷ī¸ arxiv:2009.11462 🏷ī¸ arxiv:2101.11718 🏷ī¸ arxiv:1804.09301 🏷ī¸ arxiv:2109.07958 🏷ī¸ arxiv:2203.09509 🏷ī¸ license:gemma 🏷ī¸ text-generation-inference 🏷ī¸ endpoints_compatible 🏷ī¸ region:us

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đŸ”ŦTechnical Deep Dive

Full Specifications [+]
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🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run gemma-7b
🤗 HF Download
huggingface-cli download google/gemma-7b
đŸ“Ļ Install Lib
pip install -U transformers
đŸ–Ĩī¸

Hardware Compatibility

Multi-Tier Validation Matrix

Live Sync
🎮 Compatible

RTX 3060 / 4060 Ti

Entry 8GB VRAM
🎮 Compatible

RTX 4070 Super

Mid 12GB VRAM
đŸ’ģ Compatible

RTX 4080 / Mac M3

High 16GB VRAM
🚀 Compatible

RTX 3090 / 4090

Pro 24GB VRAM
🏗ī¸ Compatible

RTX 6000 Ada

Workstation 48GB VRAM
🏭 Compatible

A100 / H100

Datacenter 80GB VRAM
ℹī¸

Pro Tip: Compatibility is estimated for 4-bit quantization (Q4). High-precision (FP16) or ultra-long context windows will significantly increase VRAM requirements.

README

Neural Fact Sheet: gemma-7b

[!IMPORTANT] Full Disclosure Protocol Active: Primary source documentation is restricted or gated. The following technical intelligence has been extracted from the R2 Production Node and Zero-Limit Knowledge Mesh.

📊 Core Architecture

  • Parameter Scale: 8.54B
  • Neural Architecture: GemmaForCausalLM
  • Inference Efficiency: 0.7/100 (FNI Logic Score)
  • License Profile: Proprietary/Restricted

⚙ī¸ Technical Capabilities

  • Neural Context Window: 4k tokens
  • Memory Footprint: ~8GB (Q4) estimated VRAM
  • Pipeline Origin: Standard AI
  • Safety Status: Model utilizes developer-defined safety filters.

🚀 Strategic Recommendations

  1. Inference Hub: Recommended for local execution via Ollama or vLLM for private infrastructure.
  2. Context Limits: Optimal performance is maintained within the first 4k tokens of input.
  3. Hardware Alignment: Ideal for hardware with at least ~8GB (Q4) of high-speed video memory.

For full unrestricted documentation, please click "View Source" in the header.

ZEN MODE â€ĸ README

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
  • â€ĸ Source: Unknown
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__google__gemma_7b,
  author = {google},
  title = {undefined Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/google/gemma-7b}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
google. (2026). undefined [Model]. Free2AITools. https://huggingface.co/google/gemma-7b
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id
hf-model--google--gemma-7b
author
google
tags
transformerssafetensorsggufgemmatext-generationarxiv:2305.14314arxiv:2312.11805arxiv:2009.03300arxiv:1905.07830arxiv:1911.11641arxiv:1904.09728arxiv:1905.10044arxiv:1907.10641arxiv:1811.00937arxiv:1809.02789arxiv:1911.01547arxiv:1705.03551arxiv:2107.03374arxiv:2108.07732arxiv:2110.14168arxiv:2304.06364arxiv:2206.04615arxiv:1804.06876arxiv:2110.08193arxiv:2009.11462arxiv:2101.11718arxiv:1804.09301arxiv:2109.07958arxiv:2203.09509license:gemmatext-generation-inferenceendpoints_compatibleregion:us

⚙ī¸ Technical Specs

architecture
GemmaForCausalLM
params billions
8.54
context length
4,096
vram gb
7.7
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

likes
3,244
downloads
52,433

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)