Overview

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.

Highlights

Frontier-level reasoning and complex problem-solving capabilities
High-performance synthetic data generation for model distillation
Extensive multilingual support for global application deployment
Competitive alternative to closed-source proprietary LLMs
Optimized for large-scale enterprise production environments

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("meta-llama/Llama-3.1-405B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-405B")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download meta-llama/Llama-3.1-405B

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download meta-llama/Llama-3.1-405B config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('meta-llama/Llama-3.1-405B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/meta-llama/Llama-3.1-405B

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/meta-llama/Llama-3.1-405B

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-3.1-405B')
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-3.1-405B')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model LLM-Research/Meta-Llama-3.1-405B

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model LLM-Research/Meta-Llama-3.1-405B README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('LLM-Research/Meta-Llama-3.1-405B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/LLM-Research/Meta-Llama-3.1-405B.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/LLM-Research/Meta-Llama-3.1-405B.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'LLM-Research/Meta-Llama-3.1-405B')