Model card
Yi-1.5 34B is a high-performance bilingual model from 01.AI designed to bridge the gap between English and Chinese language processing. For developers, the 34B parameter scale offers a strategic sweet spot: it provides significantly more reasoning depth and nuance than smaller 7B models while maintaining much lower latency and inference costs than massive 70B+ architectures. Unlike many models that treat non-English languages as an afterthought, Yi-1.5 is optimized for high-fidelity cross-lingual tasks, making it ideal for localization pipelines, multilingual chatbots, and complex document summarization involving both languages. Released under the Apache 2.0 license, it is highly accessible for commercial integration. Whether you are fine-tuning for specific domain knowledge or deploying via standard inference engines, this model serves as a robust mid-sized backbone for applications requiring sophisticated linguistic intelligence without the overhead of flagship-scale deployments.
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.
01-ai/Yi-1.5-34BInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model 01-ai/Yi-1.5-34BREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model 01-ai/Yi-1.5-34B README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('01-ai/Yi-1.5-34B')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/01-ai/Yi-1.5-34B.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/01-ai/Yi-1.5-34B.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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