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https://github.com/ByteDance-Seed/seed-oss


https://github.com/ByteDance-Seed/seed-oss

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README 

          


 👋 Hi, everyone!

    


    We are ByteDance Seed Team.






  You can get to know us better through the following channels👇

  


  

    




![seed logo](https://github.com/user-attachments/assets/c42e675e-497c-4508-8bb9-093ad4d1f216)



# Seed-OSS Open-Source Models



  

    

  

    

  

    

  


  

    




## News

- [2025/08/20]🔥We release `Seed-OSS-36B-Base` (both with and without synthetic data versions) and `Seed-OSS-36B-Instruct`.



## Introduction

Seed-OSS is a series of open-source large language models developed by ByteDance's Seed Team, designed for powerful long-context, reasoning, agent and general capabilities, and versatile developer-friendly features. Although trained with only 12T tokens, Seed-OSS achieves excellent performance on several popular open benchmarks.



We release this series of models to the open-source community under the Apache-2.0 license.



> [!NOTE]

> Seed-OSS is primarily optimized for international (i18n) use cases.



### Key Features

- **Flexible Control of Thinking Budget**: Allowing users to flexibly adjust the reasoning length as needed. This capability of dynamically controlling the reasoning length enhances inference efficiency in practical application scenarios.

- **Enhanced Reasoning Capability**: Specifically optimized for reasoning tasks while maintaining balanced and excellent general capabilities.

- **Agentic Intelligence**: Performs exceptionally well in agentic tasks such as tool-using and issue resolving.

- **Research-Friendly**: Given that the inclusion of synthetic instruction data in pre-training may affect the post-training research, we released pre-trained models both with and without instruction data, providing the research community with more diverse options.

- **Native Long Context**: Trained with up-to-512K long context natively.



### Model Summary



Seed-OSS adopts the popular causal language model architecture with RoPE, GQA attention, RMSNorm and SwiGLU activation.






| | |

|:---:|:---:|

| | **Seed-OSS-36B** |

| **Parameters** | 36B |

| **Attention** | GQA |

| **Activation Function** | SwiGLU |

| **Number of Layers** | 64 |

| **Number of QKV Heads** | 80 / 8 / 8 |

| **Head Size** | 128 |

| **Hidden Size** | 5120 |

| **Vocabulary Size** | 155K |

| **Context Length** | 512K |

| **RoPE Base Frequency** | 1e7 |






## Evaluation Results



### Seed-OSS-36B-Base



Incorporating synthetic instruction data into pretraining leads to improved performance on most benchmarks. We adopt the version augmented with synthetic instruction data (i.e., *w/ syn.*) as `Seed-OSS-36B-Base`. We also release `Seed-OSS-36B-Base-woSyn` trained without such data (i.e., *w/o syn.*), offering the community a high-performance foundation model unaffected by synthetic instruction data.






Benchmark

Seed1.6-Base

Qwen3-30B-A3B-Base-2507*

Qwen2.5-32B-Base*

Seed-OSS-36B-Base
(w/ syn.)

Seed-OSS-36B-Base-woSyn
(w/o syn.)



Knowledge



MMLU-Pro

70

59.8

58.5 (55.1)

65.1

60.4



MMLU

88.8

82.7

84 (83.3)

84.9

84.8



TriviaQA

91

76.2

76

82.1

81.9



GPQA-D

43.4

37

29.3

31.7

35.2



SimpleQA

17.1

7.2

6.1

5.8

7.4



Reasoning



BBH

92.1

81.4

79.1 (84.5)

87.7

87.2



AGIEval-en

78

66.4

65.6

70.7

70.1



Math



GSM8K

93.1

87

87.5 (92.9)

90.8

90.3



MATH

72.9

61.1

63.5 (57.7)

81.7

61.3



Coding



MBPP

83.6

78.8

77.8 (84.5)

80.6

74.6



HumanEval

78

70.7

47.6 (58.5)

76.8

75.6








- Bold denotes open-source SOTA.




- "*" indicates that the results in this column are presented in the format of "reproduced_results (reported_results_if_any)".




### Seed-OSS-36B-Instruct






Benchmark

Seed1.6-Thinking-0715

OAI-OSS-20B*

Qwen3-30B-A3B-Thinking-2507*

Qwen3-32B*

Gemma3-27B

Seed-OSS-36B-Instruct



Knowledge



MMLU-Pro

86.6

76.2

81.9 (80.9)

81.8

67.5

82.7



MMLU

90.6

81.7 (85.3)

86.9

86.2

76.9

87.4



GPQA-D

80.7

72.2 (71.5)

71.4 (73.4)

66.7 (68.4)

42.4

71.4



SuperGPQA

63.4

50.1

57.3 (56.8)

49.3

-

55.7



SimpleQA

23.7

6.7

23.6

8.6

10

9.7



Math



AIME24

90.3

92.7 (92.1)

87.7

82.7 (81.4)

-

91.7



AIME25

86

90.3 (91.7)

81.3 (85)

73.3 (72.9)

-

84.7



BeyondAIME

60

69

56

29

-

65



Reasoning



ArcAGI V2

1.16

1.74

0.87

0

-

1.45



KORBench

74.8

72.3

70.2

65.4

-

70.6



HLE

13.9

12.7 (10.9)

8.7

6.9

-

10.1



Coding



LiveCodeBench v6
(02/2025-05/2025)

66.8

63.8

60.3 (66)

53.4

-

67.4



Instruction Following



IFEval

86.3

92.8

88 (88.9)

88.4 (85)

90.4

85.8



Agent



TAU1-Retail

63

(54.8)

58.7 (67.8)

40.9

-

70.4



TAU1-Airline

49

(38)

47 (48)

38

-

46



SWE-Bench Verified
(OpenHands)

41.8

(60.7)

31

23.4

-

56



SWE-Bench Verified
(AgentLess 4*10)

48.4

-

33.5

39.7

-

47



Multi-SWE-Bench

17.7

-

9.5

7.7

-

17



Multilingualism



MMMLU

84.3

77.4 (75.7)

79

79 (80.6)

-

78.4



Long Context



RULER
(128K)

94.5

78.7

94.5

77.5

-

94.6



Safety



AIR-Bench

-

-

-

-

-

75.6








- Bold denotes open-source SOTA. Underlined indicates the second place in the open-source model.




- "*" indicates that the results in this column are presented in the format of "reproduced_results (reported_results_if_any)". Some results have been omitted due to the failure of the evaluation run.




- The results of Gemma3-27B are sourced directly from its technical report.




- The results of ArcAGI-V2 were measured on the official evaluation set, which was not involved in the training process.




- Generation configs for Seed-OSS-36B-Instruct: temperature=1.1, top_p=0.95. Specifically, for Taubench, temperature=1, top_p=0.7.







> [!NOTE]

> We recommend sampling with `temperature=1.1` and `top_p=0.95`.



### Thinking Budget



Users can flexibly specify the model's thinking budget. The figure below shows the performance curves across different tasks as the thinking budget varies. For simpler tasks (such as IFEval), the model's chain of thought (CoT) is shorter, and the score exhibits fluctuations as the thinking budget increases. For more challenging tasks (such as AIME and LiveCodeBench), the model's CoT is longer, and the score improves with an increase in the thinking budget.



![thinking_budget](./figures/thinking_budget.png)



Here is an example with a thinking budget set to 512: during the reasoning process, the model periodically triggers self-reflection to estimate the consumed and remaining budget, and delivers the final response once the budget is exhausted or the reasoning concludes.

```



Got it, let's try to solve this problem step by step. The problem says ... ...

I have used 129 tokens, and there are 383 tokens remaining for use.

Using the power rule, ... ...

I have used 258 tokens, and there are 254 tokens remaining for use.

Alternatively, remember that ... ...

I have used 393 tokens, and there are 119 tokens remaining for use.

Because if ... ...

I have exhausted my token budget, and now I will start answering the question.



To solve the problem, we start by using the properties of logarithms to simplify the given equations: (full answer omitted).

```



If no thinking budget is set (default mode), Seed-OSS will initiate thinking with unlimited length. If a thinking budget is specified, users are advised to prioritize values that are integer multiples of 512 (e.g., 512, 1K, 2K, 4K, 8K, or 16K), as the model has been extensively trained on these intervals. Models are instructed to output a direct response when the thinking budget is 0, and we recommend setting any budget below 512 to this value.



## Quick Start

```shell

pip install git+https://github.com/huggingface/transformers.git@56d68c6706ee052b445e1e476056ed92ac5eb383

```



```python

from transformers import AutoModelForCausalLM, AutoTokenizer

import os

import re



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")  # You may want to use bfloat16 and/or move to GPU here

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 the thinking budget

)



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



output_text = tokenizer.decode(outputs[0])

```



## Inference



### Download Model



Download Seed-OSS checkpoint to `./Seed-OSS-36B-Instruct`



### Transformers

The `generate.py` script provides a simple interface for model inference with configurable options.



#### Basic Usage

```shell

cd inference

python3 generate.py --model_path /path/to/model

```



#### Key Parameters

| Parameter | Description |

|-----------|-------------|

| `--model_path` | Path to the pretrained model directory (required) |

| `--prompts` | Input prompts (default: sample cooking/code questions) |

| `--max_new_tokens` | Maximum tokens to generate (default: 4096) |

| `--attn_implementation` | Attention mechanism: `flash_attention_2` (default) or `eager` |

| `--load_in_4bit/8bit` | Enable 4-bit/8-bit quantization (reduces memory usage) |

| `--thinking_budget` | Thinking budget in tokens (default: -1 for unlimited budget) |



#### Quantization Examples

```shell

# 8-bit quantization

python3 generate.py --model_path /path/to/model --load_in_8bit True



# 4-bit quantization

python3 generate.py --model_path /path/to/model --load_in_4bit True

```



#### Custom Prompts

```shell

python3 generate.py --model_path /path/to/model --prompts "['What is machine learning?', 'Explain quantum computing']"

```



### vLLM

Use vllm >= 0.10.0 or higher for inference.



- First install vLLM with Seed-OSS support version:

```shell

VLLM_USE_PRECOMPILED=1 VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL=1 pip install git+https://github.com/vllm-project/vllm.git

```



- Start vLLM API server:

```shell

python3 -m vllm.entrypoints.openai.api_server \

    --host localhost \

    --port 4321 \

    --enable-auto-tool-choice \

    --tool-call-parser seed_oss \

    --trust-remote-code \

    --model ./Seed-OSS-36B-Instruct \

    --chat-template ./Seed-OSS-36B-Instruct/chat_template.jinja \

    --tensor-parallel-size 8 \

    --dtype bfloat16 \

    --served-model-name seed_oss

```



- Test with OpenAI client:



Chat



```shell

# no stream

python3 inference/vllm_chat.py --max_new_tokens 4096 --thinking_budget -1

# stream

python3 inference/vllm_chat.py --max_new_tokens 4096 --thinking_budget -1 --stream

```



Tool Call

```shell

# no stream

python3 inference/vllm_tool_call.py --max_new_tokens 4096 --thinking_budget -1

# stream

python3 inference/vllm_tool_call.py --max_new_tokens 4096 --thinking_budget -1 --stream

```



## Model Card

See [MODEL_CARD](./MODEL_CARD.md).



## License

This project is licensed under Apache-2.0. See the [LICENSE](./LICENSE) flie for details.



## Citation



```bibtex

@misc{seed2025seed-oss,

  author={ByteDance Seed Team},

  title={Seed-OSS Open-Source Models},

  year={2025},

  howpublished={\url{https://github.com/ByteDance-Seed/seed-oss}}

}

```



## About [ByteDance Seed Team](https://seed.bytedance.com/)



Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.