本站支持搜索的镜像仓库:Docker Hub、gcr.io、ghcr.io、quay.io、k8s.gcr.io、registry.gcr.io、elastic.co、mcr.microsoft.com

!logo
Meta Llama 3.1 is a collection of multilingual large language models (LLMs) available in 8B, 70B and 405B parameter sizes. These models are designed for text-based tasks, including chat and content generation. The instruction-tuned versions available here are optimized for multilingual dialogue use cases and have demonstrated superior performance compared to many open-source and commercial chat models on common industry benchmarks.
Assistant-like chat: Instruction-tuned text-only models are optimized for multilingual dialogue, making them ideal for developing conversational AI assistants.
Natural language generation tasks: Pretrained models can be adapted for various text-based applications, such as content creation, summarization, and translation.
Synthetic data generation: Utilize the outputs of Llama 3.1 to create synthetic datasets, which can aid in training and improving other models.
Model distillation: Leverage Llama 3.1 to enhance smaller models by transferring knowledge, resulting in more efficient and specialized AI systems, or by using it as a base model to fine-tune based on the knowledge of other bigger models (see deepseek-r1-distill-llama as an example)
Research purposes: Employ Llama 3.1 in academic and scientific research to explore advancements in natural language processing and artificial intelligence.
| Attribute | Details |
|---|---|
| Provider | Meta |
| Architecture | llama |
| Cutoff date | December 2023 |
| Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. |
| Tool calling | ✅ |
| Input modalities | Text |
| Output modalities | Text and Code |
| License | Llama 3.1 Community license |
| Model variant | Parameters | Quantization | Context window | VRAM¹ | Size |
|---|---|---|---|---|---|
ai/llama3.1:latestai/llama3.1:8B-Q4_K_M | 8B | IQ2_XXS/Q4_K_M | 131K tokens | 5.33 GiB | 4.58 GB |
ai/llama3.1:8B-Q4_K_M | 8B | IQ2_XXS/Q4_K_M | 131K tokens | 5.33 GiB | 4.58 GB |
ai/llama3.1:8B-F16 | 8B | F16 | 131K tokens | 15.01 GiB | 14.96 GB |
¹: VRAM estimated based on model characteristics.
latest→8B-Q4_K_M
First, pull the model:
docker model pull ai/llama3.1
Then run the model:
docker model run ai/llama3.1
For more information on Docker Model Runner, explore the documentation.
| Category | Benchmark | Llama 3.1 8B |
|---|---|---|
| General | MMLU | 69.4 |
| MMLU (CoT) | 73.0 | |
| MMLU-Pro (CoT) | 48.3 | |
| IFEval | 80.4 | |
| Reasoning | ARC-C | 83.4 |
| GPQA | 30.4 | |
| Code | HumanEval | 72.6 |
| MBPP ++ base version | 72.8 | |
| Multipl-E HumanEval | 50.8 | |
| Multipl-E MBPP | 52.4 | |
| Math | GSM-8K (CoT) | 84.5 |
| MATH (CoT) | 51.9 | |
| Tool Use | API-Bank | 82.6 |
| BFCL | 76.1 | |
| Gorilla Benchmark API Bench | 8.2 | |
| Nexus (0-shot) | 38.5 | |
| Multilingual | Multilingual MGSM (CoT) | 68.9 |
| MMLU (5-shot) - Portuguese | 62.12 | |
| MMLU (5-shot) - Spanish | 62.45 | |
| MMLU (5-shot) - Italian | 61.63 | |
| MMLU (5-shot) - German | 60.59 | |
| MMLU (5-shot) - French | 62.34 | |
| MMLU (5-shot) - Hindi | 50.88 | |
| MMLU (5-shot) - Thai | 50.32 |
免费版仅支持 Docker Hub 加速,不承诺可用性和速度;专业版支持更多镜像源,保证可用性和稳定速度,提供优先客服响应。
免费版仅支持 docker.io;专业版支持 docker.io、gcr.io、ghcr.io、registry.k8s.io、nvcr.io、quay.io、mcr.microsoft.com、docker.elastic.co 等。
当返回 402 Payment Required 错误时,表示流量已耗尽,需要充值流量包以恢复服务。
通常由 Docker 版本过低导致,需要升级到 20.x 或更高版本以支持 V2 协议。
先检查 Docker 版本,版本过低则升级;版本正常则验证镜像信息是否正确。
使用 docker tag 命令为镜像打上新标签,去掉域名前缀,使镜像名称更简洁。
探索更多轩辕镜像的使用方法,找到最适合您系统的配置方式
通过 Docker 登录方式配置轩辕镜像加速服务,包含7个详细步骤
在 Linux 系统上配置轩辕镜像源,支持主流发行版
在 Docker Desktop 中配置轩辕镜像加速,适用于桌面系统
在 Docker Compose 中使用轩辕镜像加速,支持容器编排
在 k8s 中配置 containerd 使用轩辕镜像加速
在宝塔面板中配置轩辕镜像加速,提升服务器管理效率
在 Synology 群晖NAS系统中配置轩辕镜像加速
在飞牛fnOS系统中配置轩辕镜像加速
在极空间NAS中配置轩辕镜像加速
在爱快ikuai系统中配置轩辕镜像加速
在绿联NAS系统中配置轩辕镜像加速
在威联通NAS系统中配置轩辕镜像加速
在 Podman 中配置轩辕镜像加速,支持多系统
配置轩辕镜像加速9大主流镜像仓库,包含详细配置步骤
无需登录即可使用轩辕镜像加速服务,更加便捷高效
需要其他帮助?请查看我们的 常见问题 或 官方QQ群: 13763429