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MODEL Listed

Yi-1.5 34B

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.

01.AItext generation
01 / MODEL CARD

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 typetext generation
Provider01.AI
LicenseApache 2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/01-ai/Yi-1.5-34B
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: 01-ai/Yi-1.5-34B
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 01-ai/Yi-1.5-34B
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 01-ai/Yi-1.5-34B 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('01-ai/Yi-1.5-34B')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/01-ai/Yi-1.5-34B.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/01-ai/Yi-1.5-34B.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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