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MODEL Listed

NeoHorse-1-9B

NeoHorse-1-9B is a compact, high-efficiency text generation model designed for developers who need a balance between low latency and reasoning performance. At the 9B parameter scale, it sits in the 'sweet spot' for deployment on consumer-grade hardware or edge devices without requiring massive data center clusters. Unlike larger, cumbersome models, NeoHorse is optimized for streamlined integration into existing RAG pipelines, automated content workflows, and agentic frameworks where quick inference cycles are critical. While it doesn't aim to compete with trillion-parameter behemoths on broad world knowledge, its architecture is tuned for coherent instruction following and structured output generation. For teams working under strict memory constraints or those building specialized microservices, this model offers a highly portable alternative to larger closed-source APIs, providing more control over the deployment environment under the Apache-2.0 license.

TokenRhythmtext generation
01 / MODEL CARD

Model card

NeoHorse-1-9B is a compact, high-efficiency text generation model designed for developers who need a balance between low latency and reasoning performance. At the 9B parameter scale, it sits in the 'sweet spot' for deployment on consumer-grade hardware or edge devices without requiring massive data center clusters. Unlike larger, cumbersome models, NeoHorse is optimized for streamlined integration into existing RAG pipelines, automated content workflows, and agentic frameworks where quick inference cycles are critical. While it doesn't aim to compete with trillion-parameter behemoths on broad world knowledge, its architecture is tuned for coherent instruction following and structured output generation. For teams working under strict memory constraints or those building specialized microservices, this model offers a highly portable alternative to larger closed-source APIs, providing more control over the deployment environment under the Apache-2.0 license.

Model typetext generation
ProviderTokenRhythm
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/TokenRhythm/NeoHorse-1-9B
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: TokenRhythm/NeoHorse-1-9B
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 TokenRhythm/NeoHorse-1-9B
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 TokenRhythm/NeoHorse-1-9B 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('TokenRhythm/NeoHorse-1-9B')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/TokenRhythm/NeoHorse-1-9B.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/TokenRhythm/NeoHorse-1-9B.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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