Model card
Llama 3.1 8B Instruct is a dense decoder-only model designed for high-efficiency deployment without sacrificing complex reasoning capabilities. For developers, the primary draw is its optimized balance between footprint and performance, making it ideal for edge computing, local hosting, or as a fast routing layer in agentic workflows. It excels at structured data extraction, concise summarization, and tool-calling tasks. Compared to its predecessors, it features an expanded context window and improved multilingual support, significantly reducing the need for prompt engineering when handling diverse datasets. Integration is straightforward via standard transformers libraries or vLLM for production-grade throughput, providing a reliable open-weights alternative to proprietary small-language models.
Model files and versions
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.
meta-llama/Llama-3.1-8B-InstructInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model meta-llama/Llama-3.1-8B-InstructREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model meta-llama/Llama-3.1-8B-Instruct README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('meta-llama/Llama-3.1-8B-Instruct')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/meta-llama/Llama-3.1-8B-Instruct.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/meta-llama/Llama-3.1-8B-Instruct.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
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