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2025 Complete Guide: ByteDance Seed-OSS-36B Open Source LLM In-Depth Analysis

#bytedance #opensource

🎯 Key Takeaways (TL;DR)

  • Breakthrough Release: ByteDance releases Seed-OSS series open-source LLMs under Apache-2.0 license
  • Technical Highlights: 36B parameters, native 512K context, controllable thinking budget, trained with only 12T tokens
  • Exceptional Performance: Achieves open-source SOTA on multiple benchmarks, particularly excelling in reasoning, coding, and agent tasks
  • Practical Value: Provides both base models and instruction-tuned versions for research and commercial applications

Table of Contents

  1. What is Seed-OSS Model
  2. Core Technical Features
  3. Model Architecture Deep Dive
  4. Performance Benchmarks
  5. Controllable Thinking Budget Mechanism
  6. Quick Start Guide
  7. Competitive Analysis
  8. Frequently Asked Questions

What is Seed-OSS Model {#what-is-seed-oss}

Seed-OSS is an open-source large language model series developed by ByteDance's Seed Team, designed for powerful long-context, reasoning, agent, and general capabilities. The series includes three versions:

  • Seed-OSS-36B-Base: Base model (pre-trained version with synthetic instruction data)
  • Seed-OSS-36B-Base-woSyn: Clean base model (without synthetic instruction data)
  • Seed-OSS-36B-Instruct: Instruction-tuned model (suitable for various downstream tasks)

💡 Professional Tip
Seed-OSS is primarily optimized for international (i18n) use cases, showing excellent performance in multilingual support.

Core Technical Features {#key-features}

🎯 Controllable Thinking Budget

  • Users can flexibly adjust reasoning length
  • Supports dynamic thinking budget control to enhance inference efficiency
  • Recommended to use multiples of 512 (512, 1K, 2K, 4K, 8K, 16K)

🧠 Enhanced Reasoning Capability

  • Specifically optimized for reasoning tasks
  • Maintains balanced and excellent general capabilities
  • Achieves 91.7 on AIME24 and 84.7 on AIME25

🤖 Agentic Intelligence

  • Excels in agent tasks such as tool-using and issue resolving
  • TAU1-Retail: 70.4 (open-source SOTA)
  • SWE-Bench Verified: 56 (open-source SOTA)

🔬 Research-Friendly

  • Provides pre-trained models both with and without synthetic instruction data
  • Offers more diverse options for the research community

📚 Native Long Context

  • Supports up to 512K native long context
  • RULER (128K) benchmark: 94.6 score

Model Architecture Deep Dive {#architecture}

Parameter Specification
Parameters 36B
Attention GQA (Grouped Query Attention)
Activation Function SwiGLU
Number of Layers 64
QKV Heads 80 / 8 / 8
Head Dimension 128
Hidden Size 5120
Vocabulary Size 155K
Context Length 512K
RoPE Base Frequency 1e7

⚠️ Note
The 36B parameter model requires approximately 20GB+ VRAM with Q4 quantization. Consider using inference frameworks that support partial offloading.

Performance Benchmarks {#benchmarks}

Base Model Performance Comparison

Benchmark Qwen3-30B-A3B-Base Qwen2.5-32B-Base Seed-OSS-36B-Base Seed-OSS-36B-Base-woSyn
MMLU-Pro 59.8 58.5 65.1 60.4
MMLU 82.7 84.0 84.9 84.8
BBH 81.4 79.1 87.7 87.2
GSM8K 87.0 87.5 90.8 90.3
MATH 61.1 63.5 81.7 61.3
HumanEval 70.7 47.6 76.8 75.6

Instruction-Tuned Model Performance

Task Category Benchmark Qwen3-30B-A3B-Thinking Qwen3-32B Seed-OSS-36B-Instruct
Math Reasoning AIME24 87.7 82.7 91.7
Math Reasoning AIME25 81.3 73.3 84.7
Coding LiveCodeBench v6 60.3 53.4 67.4
Agent TAU1-Retail 58.7 40.9 70.4
Agent SWE-Bench Verified 31.0 23.4 56.0
Long Context RULER (128K) 94.5 77.5 94.6

✅ Best Practice
Recommended to use temperature=1.1 and top_p=0.95 for sampling to achieve optimal performance.

Controllable Thinking Budget Mechanism {#thinking-budget}

How It Works

The unique feature of Seed-OSS is its controllable thinking budget mechanism, allowing users to flexibly specify the model's thinking budget:

<seed:think>
Let me solve this problem step by step...
<seed:cot_budget_reflect>I have used 129 tokens, and there are 383 tokens remaining for use.</seed:cot_budget_reflect>
Using the power rule...
<seed:cot_budget_reflect>I have used 258 tokens, and there are 254 tokens remaining for use.</seed:cot_budget_reflect>
Alternatively, remember that...
<seed:cot_budget_reflect>I have exhausted my token budget, and now I will start answering the question.</seed:cot_budget_reflect>
</seed:think>
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Budget Setting Guidelines

Budget Value Use Case Performance
0 Direct answers needed Fast response, no thinking process
512 Simple questions Basic reasoning, moderate performance
1K-2K Medium complexity Balance efficiency and quality
4K-8K Complex reasoning Deep thinking, high-quality output
16K+ Extremely complex tasks Maximum reasoning capability
Default(-1) Unlimited Auto-adjust thinking length

Quick Start Guide {#quick-start}

Environment Setup 

pip3 install -r requirements.txt
pip install git+ssh://git@github.com/Fazziekey/transformers.git@seed-oss
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Basic Usage 

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name_or_path = "ByteDance-Seed/Seed-OSS-36B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path, 
    device_map="auto"
)

messages = [
    {"role": "user", "content": "How to make pasta?"},
]

tokenized_chat = tokenizer.apply_chat_template(
    messages, 
    tokenize=True, 
    add_generation_prompt=True, 
    return_tensors="pt", 
    thinking_budget=512  # Control thinking budget
)

outputs = model.generate(
    tokenized_chat.to(model.device), 
    max_new_tokens=2048
)

output_text = tokenizer.decode(outputs[0])
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vLLM Deployment 

# Install vLLM version with Seed-OSS support
VLLM_USE_PRECOMPILED=1 VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL=1 \
pip install git+ssh://git@github.com/FoolPlayer/vllm.git@seed-oss

# Start API server
python3 -m vllm.entrypoints.openai.api_server \
    --host localhost \
    --port 4321 \
    --model ./Seed-OSS-36B-Instruct \
    --tensor-parallel-size 8 \
    --dtype bfloat16
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Competitive Analysis {#comparison}

Training Efficiency Comparison

Model Parameters Training Tokens Context Length Special Capabilities
Seed-OSS-36B 36B 12T 512K Controllable thinking budget
Qwen3-30B-A3B 30B 32T 1M (RoPE) Reasoning optimization
Qwen2.5-32B 32B 18T 128K General capabilities
Gemma3-27B 27B Undisclosed 8K Google ecosystem

💡 Professional Tip
Seed-OSS achieves excellent performance with only 12T tokens, demonstrating efficient training strategies and high data quality.

Application Scenario Mapping 

graph TD
    A[Seed-OSS-36B] --> B[Research Use]
    A --> C[Commercial Applications]
    A --> D[Agent Development]

    B --> B1[Base Model Research]
    B --> B2[Fine-tuning Experiments]

    C --> C1[Customer Service Systems]
    C --> C2[Content Generation]

    D --> D1[Code Assistants]
    D --> D2[Tool Calling]
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🤔 Frequently Asked Questions {#faq}

Q: What's the relationship between Seed-OSS and GPT-OSS?

A: Seed-OSS is independently developed by ByteDance and has no direct relationship with OpenAI's GPT-OSS. Both adopt controllable thinking budget design concepts, but differ in architecture and training methods.

Q: How much VRAM does the 36B parameter model require?

A:

  • FP16: ~72GB
  • INT8: ~36GB
  • INT4: ~18-20GB
  • Recommend using inference frameworks that support partial offloading, such as vLLM or llama.cpp

Q: How to choose the appropriate thinking budget?

A: Choose based on task complexity:

  • Simple QA: 512 tokens
  • Math reasoning: 2K-4K tokens
  • Complex programming: 4K-8K tokens
  • Research analysis: 8K+ tokens

Q: What's the difference between Base and woSyn versions?

A:

  • Base version: Pre-trained with synthetic instruction data, better performance
  • woSyn version: Clean base model, suitable for research and custom fine-tuning

Q: Which languages does the model support?

A: Seed-OSS is primarily optimized for international use cases, supporting multiple languages with a score of 78.4 on the MMMLU multilingual benchmark.

Q: Are there restrictions for commercial use?

A: Uses Apache-2.0 license, allowing commercial use, but recommend reading the license terms carefully.

Summary and Recommendations

Seed-OSS-36B represents significant progress in the open-source LLM field. Its unique controllable thinking budget mechanism and exceptional performance make it an ideal choice for research and applications.

🎯 Recommended Use Cases

  1. Research Institutions: Use woSyn version for fundamental research
  2. Enterprise Applications: Deploy Instruct version to build intelligent applications
  3. Developers: Use controllable thinking budget to optimize inference efficiency
  4. Education Sector: Serve as high-quality open-source resource for teaching and learning

📈 Future Outlook

  • Anticipate release of larger-scale versions (such as the rumored 200B MoE model)
  • Continue monitoring community feedback and performance optimizations
  • Explore more innovative reasoning control mechanisms

✅ Take Action Now
Visit Hugging Face to download the model, or check the GitHub repository for latest documentation and example code.

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