NVIDIA NIM for GPU accelerated Stockmark-2-100B-Instruct inference through OpenAI compatible APIs
Stockmark-2-100B-Instruct Overview
Description:
This container houses the Stockmark-2-100B-Instruct, which is a 100-billion-parameter large language model built from scratch, with a particular focus on Japanese. It was pre-trained on approximately 2.0 trillion tokens of data, consisting of 60% English, 30% Japanese, and 10% code. Following pretraining, the model underwent post-training (SFT and DPO) with synthetic data in Japanese to enhance its ability to follow instructions. This version improves instruction-following ability and adds support for long-context (32k), compared to the previous version.
The container components are ready for commercial/non-commercial use.
Third-Party Community Consideration
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see Stockmark-2-100B-Instruct · Hugging Face.
Deployment Geography:
Global
Release Date:
Build.NVIDIA.com via link
Huggingface via link
Stockmark-2-100B-Instruct
Stockmark-2-100B-Instruct Container includes the following model:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Stockmark-2-100B-Instruct | 100-billion-parameter large language model built from scratch, with a particular focus on Japanese. | Automatic |
Deployment Details:
Visit the NIM Container LLM page for release documentation, deployment guides, and more.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Container Version(s):
Stockmark-2-100B-Instruct-1.12.0
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NVIDIA NIM Documentation
Visit the NIM Container LLM page for release documentation, deployment guides and more.
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