ai/gemma3
Google最新推出的AI模型Gemma，虽体型小巧却性能强劲，尤其在聊天交互与内容生成领域表现突出；其轻量化设计不仅便于高效部署，还能在保证响应速度的同时，持续输出高质量内容，为用户带来便捷且智能的使用体验，是一款兼顾小巧体型与强大功能的新一代AI模型。
让 AI 帮你使用轩辕镜像？ · 展开查看说明 · 点击收起说明
如果你使用 DeepSeek、元宝 AI、Copilot、ChatGPT 等 AI 工具，协助配置轩辕镜像、编写 docker pull 命令、修改 Docker Compose 镜像地址、配置镜像加速、排查镜像拉取失败、分析报错日志等问题，请先让 AI 阅读并遵守轩辕镜像的规则文档。
只需在 AI 对话中先发送下面这句话即可：
请先完整阅读并严格遵守以下文档中的全部规则与要求：
https://xuanyuan.cloud/agents.md
在未充分阅读并理解该文档前，不要生成任何命令、配置、修改建议、故障排查方案或技术回答。后续所有输出都必须严格以该文档中的规范为最高优先级执行。查看 agents.md 用法指南与完整示范。国内用户首推 元宝 AI、DeepSeek 的深度思考模式，不推荐豆包 AI；Cursor 等编辑器可在对话 @ 该链接，或加入 User Rules。 若 AI 无法访问外链，可 打开说明文档 复制全文粘贴。文档会随站点更新，复制内容可能过期，建议定期检查。
Gemma 3
GGUF version by Unsloth
!https://github.com/docker/model-cards/raw/refs/heads/main/logos/***
Gemma is a versatile AI model family designed for tasks like question answering, summarization, and reasoning. With open weights and responsible commercial use, it supports image-text input, a 128K token context, and over 140 languages.
Intended uses
Gemma 3 4B model can be used for:
- Text generation: Create poems, scripts, code, marketing copy, and email drafts.
- Chatbots and conversational AI: Enable virtual assistants and customer service bots.
- Text summarization: Produce concise summaries of reports and research papers.
- Image data extraction: Interpret and summarize visual data for text-based communication.
- Language learning tools: Aid in grammar correction and interactive writing practice.
- Knowledge exploration: Assist researchers by generating summaries and answering questions.
Characteristics
| Attribute | Details |
|---|---|
| Provider | Google DeepMind |
| Architecture | Gemma3 |
| Cutoff date | - |
| Languages | 140 languages |
| Tool calling | ❌ |
| Input modalities | Text, Image |
| Output modalities | Text, Code |
| License | Gemma Terms |
Available model variants
| Model variant | Parameters | Quantization | Context window | VRAM¹ | Size |
|---|---|---|---|---|---|
ai/gemma3:4Bai/gemma3:4B-Q4_K_Mai/gemma3:latest | 4B | MOSTLY_Q4_K_M | 131K tokens | 3.83 GiB | 2.31 GB |
ai/gemma3:270M-F16 | 270M | MOSTLY_F16 | 33K tokens | 1.59 GiB | 511.46 MB |
ai/gemma3:270M-UD-IQ2_XXS | 270M | MOSTLY_IQ2_XXS | 33K tokens | 1.26 GiB | 165.54 MB |
ai/gemma3:270M-UD-Q4_K_XL | 270M | MOSTLY_Q4_K_M | 33K tokens | 1.33 GiB | 235.95 MB |
ai/gemma3:4B-F16 | 4B | MOSTLY_BF16 | 131K tokens | 8.75 GiB | 7.23 GB |
¹: VRAM estimated based on model characteristics.
latest→4B
Use this AI model with Docker Model Runner
First, pull the model:
bashdocker model pull ai/gemma3
Then run the model:
bashdocker model run ai/gemma3
For more information on Docker Model Runner, explore the documentation.
Benchmark performance
| Category | Benchmark | Value |
|---|---|---|
| General | MMLU | 59.6 |
| GSM8K | 38.4 | |
| ARC-Challenge | 56.2 | |
| BIG-Bench Hard | 50.9 | |
| DROP | 60.1 | |
| STEM & Code | MATH | 24.2 |
| MBPP | 46.0 | |
| HumanEval | 36.0 | |
| Multilingual | MGSM | 34.7 |
| Global-MMLU-Lite | 57.0 | |
| XQuAD (all) | 68.0 | |
| Multimodal | VQAv2 | 63.9 |
| TextVQA | 58.9 | |
| DocVQA | 72.8 |
Links
- Gemma 3 Model Overview
- Gemma 3 Technical Report
- Unsloth Dynamic 2.0 GGUF
镜像拉取方式
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问 标签列表页面。
DockerHub 原生拉取命令
docker model pull ai/gemma3:<标签>
镜像拉取常见问题
功能
错误码
用户好评
来自真实用户的反馈，见证轩辕镜像的优质服务
oldzhang
运维工程师
Linux服务器
5
"Docker访问体验非常流畅，大镜像也能快速完成下载。"