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 files and versions
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We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.
TokenRhythm/NeoHorse-1-9BInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model TokenRhythm/NeoHorse-1-9BREADME.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 ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('TokenRhythm/NeoHorse-1-9B')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/TokenRhythm/NeoHorse-1-9B.gitFetch 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.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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