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MODEL Listed

Llama 3.1 405B

Llama 3.1 405B represents a significant shift in the open-weights landscape, offering frontier-level performance that rivals top-tier proprietary models. For developers, its primary value lies in its massive scale, which enables complex reasoning, sophisticated multilingual support, and high-fidelity code generation. Unlike smaller models, the 405B variant is designed for heavy-duty production workloads where precision is non-negotiable. It is particularly effective as a 'teacher model' for synthetic data generation to distill knowledge into smaller, more efficient models. Integration is streamlined via standard inference frameworks, though its footprint requires substantial VRAM or distributed deployment across multiple GPUs. It provides a viable alternative for teams needing full control over their weights without sacrificing the capabilities of a state-of-the-art LLM.

Metatext-generation
01 / MODEL CARD

Model card

Llama 3.1 405B represents a significant shift in the open-weights landscape, offering frontier-level performance that rivals top-tier proprietary models. For developers, its primary value lies in its massive scale, which enables complex reasoning, sophisticated multilingual support, and high-fidelity code generation. Unlike smaller models, the 405B variant is designed for heavy-duty production workloads where precision is non-negotiable. It is particularly effective as a 'teacher model' for synthetic data generation to distill knowledge into smaller, more efficient models. Integration is streamlined via standard inference frameworks, though its footprint requires substantial VRAM or distributed deployment across multiple GPUs. It provides a viable alternative for teams needing full control over their weights without sacrificing the capabilities of a state-of-the-art LLM.

Model typetext-generation
ProviderMeta
LicenseLlama 3.1
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/meta-llama/Llama-3.1-405B
View model source
Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: meta-llama/Llama-3.1-405B
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model meta-llama/Llama-3.1-405B
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model meta-llama/Llama-3.1-405B README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('meta-llama/Llama-3.1-405B')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/meta-llama/Llama-3.1-405B.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/meta-llama/Llama-3.1-405B.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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