Seed-OSS-36B-Instruct

Description

Seed-OSS-36B-Instruct is a 36-billion parameter open-source large language model developed by ByteDance's Seed Team. It is designed for powerful long-context, reasoning, agent and general capabilities, and versatile developer-friendly features. The model features flexible control of thinking budget, enhanced reasoning capability, agentic intelligence, and native long context support up to 512K tokens.

This model is ready for 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 link to Non-NVIDIA Seed-OSS-36B-Instruct Model Card.

License and Terms of Use:

GOVERNING TERMS: This trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Community Model License. Additional Information: Apache 2.0.

Deployment Geography: Global

Release Date:

Build.NVIDIA.com [09/05/2025] via link
Huggingface 08/20/2025 via link

Reference(s):

• Seed-OSS Open-Source Models
• Seed-OSS Technical Documentation

Model Architecture:

Architecture Type: Causal language model with RoPE
Network Architecture: Transformer-based decoder-only
Total Parameters: 36B
Active Parameters: 36B
Vocabulary Size: 155K
Base Model: Seed-OSS-36B-Base

Input:

Input Types: Text
Input Formats: Natural language prompts, conversational messages
Input Parameters: [One-Dimensional (1D)]
Other Input Properties: Max Input Tokens: 512K, Support for thinking budget control, tool calling, long context up to 512K tokens
Input Context Length (ISL): 512K tokens

Output:

Output Types: Text
Output Format: Natural language responses, structured tool calls
Output Parameters: [One-Dimensional (1D)]
Other Output Properties: Max Input Tokens: 512K, Chain-of-thought reasoning, thinking budget reflection, direct responses
Output Context Length (OSL): Configurable based on remaining context

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.

Software Integration:

Runtime Engines: Transformers, vLLM (>=0.10.0)
Supported Hardware:

• NVIDIA Ampere
• NVIDIA Ada Lovelace
• NVIDIA Hopper
• NVIDIA Blackwell

Preferred/Supported Operating System: Linux

Model Version(s)

v1.0

Training, Testing, and Evaluation Datasets:

Data Modality: Text

Text Training Data Size: ~12 Trillion Tokens

Training Dataset

Training Data Collection: [Hybrid: Automated, Human]
Training Labeling: [Hybrid: Automated, Human]
Training Properties: Pre-trained over 12 trillion tokens, knowledge cutoff of 07/2024, data from multiple sources including publicly available internet data, purchased data through vendor partnerships, and in-house generated data

Testing Dataset

Testing Data Collection: [Hybrid: Automated, Human]
Testing Labeling: [Hybrid: Automated, Human]
Testing Properties: Regular safety testing and adversarial testing conducted to identify and address safety vulnerabilities

Evaluation Dataset

Evaluation Benchmark Score: MMLU-Pro: 82.7, MMLU: 87.4, GPQA-D: 80.7, BBH: 89.1, AGIEval-en: 75.8, GSM8K: 93.1, MATH: 84.2, MBPP: 82.6, HumanEval: 78.8, RULER(128K): 94.6, AIR-Bench: 75.6
Evaluation Data Collection: [Hybrid: Automated, Human]
Evaluation Labeling: [Hybrid: Automated, Human]
Evaluation Properties: Safety evaluation including training data filtering, safety fine-tuning evaluation, and content safety measures assessment

Inference

Acceleration Engine: Transformers, vLLM
Test Hardware: H100

Additional Details

Key features include:

• Flexible Control of Thinking Budget: Users can adjust reasoning length dynamically
• Enhanced Reasoning Capability: Optimized for reasoning tasks while maintaining general capabilities
• Agentic Intelligence: Exceptional performance in agentic tasks like tool-using and issue resolving
• Native Long Context: Trained with up-to-512K long context natively
• Research-Friendly: Released both with and without synthetic instruction data
• Content Safety Measures: Training data filtering, safety fine-tuning, and regular safety evaluation
• International (i18n) Model: Primarily optimized for English with limited performance in other languages
• Knowledge Cutoff: July 2024

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here