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@ -1,52 +0,0 @@
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This model is released to foster global open research and empower developers.
|
||||
|
||||
## Model Details
|
||||
- Model Name: `Seed-OSS`
|
||||
- Model Type/Structure: Causal language model
|
||||
- Model Version:
|
||||
- `Seed-OSS-36B-Base`
|
||||
- `Seed-OSS-36B-Base-woSyn`
|
||||
- `Seed-OSS-36B-Instruct`
|
||||
- Context Length: 512K (524,288)
|
||||
- Developed by: ByteDance Seed Team
|
||||
- Release Date: Aug 20, 2025
|
||||
- License: Apache 2.0
|
||||
- Contact: [seed-bytedance@bytedance.com](mailto:seed-bytedance@bytedance.com)
|
||||
|
||||
## Intended Use and Limitations
|
||||
**Intended Uses**:
|
||||
As a general purpose model, Seed-OSS can support multiple use cases, including question answering, summarization, reasoning. It takes text as input and generates text as output. The following list is just some limited examples of the potential use cases:
|
||||
- Underlying technology for chatbots and conversational AI.
|
||||
- Assistance with content creation (e.g., drafting, summarizing, editing).
|
||||
- Auxiliary information retrieval in non-critical scenarios.
|
||||
|
||||
**Prohibited Uses**:
|
||||
- **Professional medical, legal, or financial advice**.
|
||||
- **Automatic decision-making**: Used for high-risk, high-impact automatic decision-making unless it has been rigorously fine-tuned and evaluated by humans for that specific use case.
|
||||
- **Minor Safety**: Abuse, exploit, or harm a minor or individual under the age of consent, including grooming, or child sexual exploitation
|
||||
- **Illegal Activities**: Violating any laws, including fraud, terrorism, or generating Child Sexual Abuse Material (CSAM).
|
||||
- **Hate & Harassment**: Generating discriminatory, hateful, or harassing content.
|
||||
- **Misinformation**: Engaging in disinformation, misinformation, or deceptive activities, including but not limited to passing off or representing AI-generated content as human-generated.
|
||||
- **Military & Surveillance**: Any use for military, weaponry, intelligence gathering, or mass surveillance purposes.
|
||||
- **High-Risk Harm**: Generating sexually explicit material, violating privacy (e.g., PII).
|
||||
|
||||
**Limitations**:
|
||||
- **Languages**: Seed-OSS is an **international (i18n)** model. It is primarily optimized for and evaluated in **English**. Performance in other languages is limited and not robustly tested.
|
||||
- **Training Data**: The training data is predominantly from publicly available sources, the models understanding of specific cultures, values and historical events may be incomplete.
|
||||
- **Hallucination**: Models generate content based on the statistical patterns in their training data. The model may generate information that is incorrect or entirely fictional.
|
||||
- **Harmful Content Generation**: Like any large language model, Seed-OSS may still be capable of producing outputs which are considered harmful, inaccurate or offensive—despite extensive safety training. We encourage developers to conduct their own testing, ensure human oversight and deploy mitigation strategies for their specific use cases.
|
||||
|
||||
For developers who intend to build customized models or applications on top of this model, please be aware that you are fully responsible for the customized model or your application, and ensuring your application is compliant with all applicable laws.
|
||||
|
||||
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
|
||||
|
||||
## Content Safety Measures
|
||||
We designed relevant measures to ensure the model's content safety throughout the entire model training cycle, ranging from training data preparation to model training and evaluation. Prior to launch, Seed-OSS went through numerous safety and security reviews led by a global Safety and Security team based in Singapore. This includes:
|
||||
- **Training Data Filtering**: Data cleaning and content filtering mechanism is designed and executed to ensure that no CSAM or highly toxic content data is included in the training dataset. PII removal is also performed through a combination of algorithmic and manual checks.
|
||||
- **Safety Fine-Tuning**: Safety training is executed during the Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF/PPO) training stages to minimize the likelihood of harmful outputs.
|
||||
- **Evaluation**: Regular safety testing and adversarial testing are conducted to identify and address safety vulnerabilities.
|
||||
|
||||
## Training Data
|
||||
Seed-OSS is trained on text data from multiple sources, which includes publicly available data on the internet, purchased data through the partnership with external vendors and data generated by our in-house teams. The model is pre-trained over 12 trillion tokens. The model has a knowledge cutoff of 07/2024.
|
||||
|
||||
Multiple measures have been taken to do data preprocessing, including data deduplication, data desensitization, quality filtering, CSAM filtering and toxic content filtering.
|
||||
630
README.md
630
README.md
@ -1,630 +0,0 @@
|
||||
---
|
||||
license: apache-2.0
|
||||
pipeline_tag: text-generation
|
||||
library_name: transformers
|
||||
tags:
|
||||
- vllm
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
👋 Hi, everyone!
|
||||
<br>
|
||||
We are <b>ByteDance Seed Team.</b>
|
||||
</div>
|
||||
|
||||
<p align="center">
|
||||
You can get to know us better through the following channels👇
|
||||
<br>
|
||||
<a href="https://seed.bytedance.com/">
|
||||
<img src="https://img.shields.io/badge/Website-%231e37ff?style=for-the-badge&logo=bytedance&logoColor=white"></a>
|
||||
</p>
|
||||
|
||||
|
||||
|
||||
|
||||
# Seed-OSS Open-Source Models
|
||||
<p align="center">
|
||||
<a href="https://github.com/ByteDance-Seed/seed-oss">
|
||||
<img src="https://img.shields.io/badge/Seed-Project Page-yellow"></a>
|
||||
<a href="https://github.com/ByteDance-Seed/seed-oss">
|
||||
<img src="https://img.shields.io/badge/Seed-Tech Report Coming Soon-red"></a>
|
||||
<a href="https://huggingface.co/collections/ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd">
|
||||
<img src="https://img.shields.io/badge/Seed-Hugging Face-orange"></a>
|
||||
<br>
|
||||
<a href="./LICENSE">
|
||||
<img src="https://img.shields.io/badge/License-Apache2.0-blue"></a>
|
||||
</p>
|
||||
|
||||
> [!NOTE]
|
||||
> This model card is dedicated to the `Seed-OSS-36B-Base-Instruct` model.
|
||||
|
||||
## 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.
|
||||
|
||||
<div align="center">
|
||||
|
||||
| | |
|
||||
|:---:|:---:|
|
||||
| | **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 |
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
## 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.
|
||||
|
||||
<div align="center">
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th align="center">Benchmark</th>
|
||||
<th align="center"><sup><a href="https://seed.bytedance.com/en/seed1_6">Seed1.6-Base</a></sup></th>
|
||||
<th align="center"><sup>Qwen3-30B-A3B-Base-2507*</sup></th>
|
||||
<th align="center"><sup>Qwen2.5-32B-Base*</sup></th>
|
||||
<th align="center"><sup>Seed-OSS-36B-Base<br>(<i>w/ syn.</i>)</sup></th>
|
||||
<th align="center"><sup>Seed-OSS-36B-Base-woSyn<br>(<i>w/o syn.</i>)</sup></th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td align="center" colspan=6><strong>Knowledge</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">MMLU-Pro</td>
|
||||
<td align="center">70</td>
|
||||
<td align="center">59.8</td>
|
||||
<td align="center">58.5 (55.1)</td>
|
||||
<td align="center"><b>65.1</b></td>
|
||||
<td align="center">60.4</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">MMLU</td>
|
||||
<td align="center">88.8</td>
|
||||
<td align="center">82.7</td>
|
||||
<td align="center">84 (83.3)</td>
|
||||
<td align="center"><b>84.9</b></td>
|
||||
<td align="center">84.8</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">TriviaQA</td>
|
||||
<td align="center">91</td>
|
||||
<td align="center">76.2</td>
|
||||
<td align="center">76</td>
|
||||
<td align="center"><b>82.1</b></td>
|
||||
<td align="center">81.9</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">GPQA-D</td>
|
||||
<td align="center">43.4</td>
|
||||
<td align="center"><b>37</b></td>
|
||||
<td align="center">29.3</td>
|
||||
<td align="center">31.7</td>
|
||||
<td align="center">35.2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">SimpleQA</td>
|
||||
<td align="center">17.1</td>
|
||||
<td align="center">7.2</td>
|
||||
<td align="center">6.1</td>
|
||||
<td align="center">5.8</td>
|
||||
<td align="center"><b>7.4</b></td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=6><strong>Reasoning</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">BBH</td>
|
||||
<td align="center">92.1</td>
|
||||
<td align="center">81.4</td>
|
||||
<td align="center">79.1 (84.5)</td>
|
||||
<td align="center"><b>87.7</b></td>
|
||||
<td align="center">87.2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">AGIEval-en</td>
|
||||
<td align="center">78</td>
|
||||
<td align="center">66.4</td>
|
||||
<td align="center">65.6</td>
|
||||
<td align="center"><b>70.7</b></td>
|
||||
<td align="center">70.1</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=6><strong>Math</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">GSM8K</td>
|
||||
<td align="center">93.1</td>
|
||||
<td align="center">87</td>
|
||||
<td align="center">87.5 (92.9)</td>
|
||||
<td align="center"><b>90.8</b></td>
|
||||
<td align="center">90.3</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">MATH</td>
|
||||
<td align="center">72.9</td>
|
||||
<td align="center">61.1</td>
|
||||
<td align="center">63.5 (57.7)</td>
|
||||
<td align="center"><b>81.7</b></td>
|
||||
<td align="center">61.3</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=6><strong>Coding</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">MBPP</td>
|
||||
<td align="center">83.6</td>
|
||||
<td align="center">78.8</td>
|
||||
<td align="center">77.8 (84.5)</td>
|
||||
<td align="center"><b>80.6</b></td>
|
||||
<td align="center">74.6</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">HumanEval</td>
|
||||
<td align="center">78</td>
|
||||
<td align="center">70.7</td>
|
||||
<td align="center">47.6 (58.5)</td>
|
||||
<td align="center"><b>76.8</b></td>
|
||||
<td align="center">75.6</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<sup>
|
||||
- <b>Bold</b> denotes open-source SOTA.
|
||||
</sup><br/><sup>
|
||||
- "*" indicates that the results in this column are presented in the format of "reproduced_results (reported_results_if_any)".
|
||||
</sup>
|
||||
|
||||
### Seed-OSS-36B-Instruct
|
||||
|
||||
<div align="center">
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th align="center">Benchmark</th>
|
||||
<th align="center"><sup><a href="https://console.volcengine.com/ark/region:ark+cn-beijing/model/detail?Id=doubao-seed-1-6-thinking">Seed1.6-Thinking-0715</a></sup></th>
|
||||
<th align="center"><sup>OAI-OSS-20B*</sup></th>
|
||||
<th align="center"><sup>Qwen3-30B-A3B-Thinking-2507*</sup></th>
|
||||
<th align="center"><sup>Qwen3-32B*</sup></th>
|
||||
<th align="center"><sup>Gemma3-27B</sup></th>
|
||||
<th align="center"><sup>Seed-OSS-36B-Instruct</sup></th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Knowledge</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">MMLU-Pro</td>
|
||||
<td align="center">86.6</td>
|
||||
<td align="center">76.2</td>
|
||||
<td align="center"><ins>81.9</ins> (80.9)</td>
|
||||
<td align="center">81.8</td>
|
||||
<td align="center">67.5</td>
|
||||
<td align="center"><b>82.7</b></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">MMLU</td>
|
||||
<td align="center">90.6</td>
|
||||
<td align="center">81.7 (85.3)</td>
|
||||
<td align="center"><ins>86.9</ins></td>
|
||||
<td align="center">86.2</td>
|
||||
<td align="center">76.9</td>
|
||||
<td align="center"><b>87.4</b></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">GPQA-D</td>
|
||||
<td align="center">80.7</td>
|
||||
<td align="center"><b>72.2</b> (71.5)</td>
|
||||
<td align="center"><ins>71.4</ins> (73.4)</td>
|
||||
<td align="center">66.7 (68.4)</td>
|
||||
<td align="center">42.4</td>
|
||||
<td align="center"><ins>71.4</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">SuperGPQA</td>
|
||||
<td align="center">63.4</td>
|
||||
<td align="center">50.1</td>
|
||||
<td align="center"><b>57.3</b> (56.8)</td>
|
||||
<td align="center">49.3</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>55.7</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">SimpleQA</td>
|
||||
<td align="center">23.7</td>
|
||||
<td align="center">6.7</td>
|
||||
<td align="center"><b>23.6</b></td>
|
||||
<td align="center">8.6</td>
|
||||
<td align="center"><ins>10</ins></td>
|
||||
<td align="center">9.7</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Math</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">AIME24</td>
|
||||
<td align="center">90.3</td>
|
||||
<td align="center"><b>92.7</b> (92.1)</td>
|
||||
<td align="center">87.7</td>
|
||||
<td align="center">82.7 (81.4)</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>91.7</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">AIME25</td>
|
||||
<td align="center">86</td>
|
||||
<td align="center"><b>90.3</b> (91.7)</td>
|
||||
<td align="center">81.3 (85)</td>
|
||||
<td align="center">73.3 (72.9)</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>84.7</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">BeyondAIME</td>
|
||||
<td align="center">60</td>
|
||||
<td align="center"><b>69</b></td>
|
||||
<td align="center">56</td>
|
||||
<td align="center">29</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>65</ins></td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Reasoning</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">ArcAGI V2</td>
|
||||
<td align="center">1.16</td>
|
||||
<td align="center"><b>1.74</b></td>
|
||||
<td align="center">0.87</td>
|
||||
<td align="center">0</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>1.45</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">KORBench</td>
|
||||
<td align="center">74.8</td>
|
||||
<td align="center"><b>72.3</b></td>
|
||||
<td align="center">70.2</td>
|
||||
<td align="center">65.4</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>70.6</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">HLE</td>
|
||||
<td align="center">13.9</td>
|
||||
<td align="center"><b>12.7</b> (10.9)</td>
|
||||
<td align="center">8.7</td>
|
||||
<td align="center">6.9</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>10.1</ins></td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Coding</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">LiveCodeBench v6<br/><sup>(02/2025-05/2025)</sup></td>
|
||||
<td align="center">66.8</td>
|
||||
<td align="center"><ins>63.8</ins></td>
|
||||
<td align="center">60.3 (66)</td>
|
||||
<td align="center">53.4</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><b>67.4</b></td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Instruction Following</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">IFEval</td>
|
||||
<td align="center">86.3</td>
|
||||
<td align="center"><b>92.8</b></td>
|
||||
<td align="center">88 (88.9)</td>
|
||||
<td align="center">88.4 (85)</td>
|
||||
<td align="center"><ins>90.4</ins></td>
|
||||
<td align="center">85.8</td>
|
||||
</tr>
|
||||
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Agent</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">TAU1-Retail</td>
|
||||
<td align="center">63</td>
|
||||
<td align="center">(54.8)</td>
|
||||
<td align="center"><ins>58.7</ins> (67.8)</td>
|
||||
<td align="center">40.9</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><b>70.4</b></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">TAU1-Airline</td>
|
||||
<td align="center">49</td>
|
||||
<td align="center">(38)</td>
|
||||
<td align="center"><b>47</b> (48)</td>
|
||||
<td align="center">38</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>46</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">SWE-Bench Verified<br/><sup>(OpenHands)</sup></td>
|
||||
<td align="center">41.8</td>
|
||||
<td align="center"><b>(60.7)</b></td>
|
||||
<td align="center">31</td>
|
||||
<td align="center">23.4</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>56</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">SWE-Bench Verified<br/><sup>(AgentLess 4*10)</sup></td>
|
||||
<td align="center">48.4</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">33.5</td>
|
||||
<td align="center"><ins>39.7</ins></td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><b>47</b></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">Multi-SWE-Bench</td>
|
||||
<td align="center">17.7</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>9.5</ins></td>
|
||||
<td align="center">7.7</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><b>17</b></td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Multilingualism</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">MMMLU</td>
|
||||
<td align="center">84.3</td>
|
||||
<td align="center">77.4 (75.7)</td>
|
||||
<td align="center"><b>79</b></td>
|
||||
<td align="center"><b>79</b> (80.6)</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><ins>78.4</ins></td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Long Context</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">RULER<br/><sup>(128K)</sup></td>
|
||||
<td align="center">94.5</td>
|
||||
<td align="center">78.7</td>
|
||||
<td align="center"><ins>94.5</ins></td>
|
||||
<td align="center">77.5</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center"><b>94.6</b></td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td align="center" colspan=7><strong>Safety</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center">AIR-Bench</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">75.6</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<sup>
|
||||
- <b>Bold</b> denotes open-source SOTA. <ins>Underlined</ins> indicates the second place in the open-source model.
|
||||
</sup><br/><sup>
|
||||
- "*" 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.
|
||||
</sup><br/><sup>
|
||||
- The results of Gemma3-27B are sourced directly from its technical report.
|
||||
</sup><br/><sup>
|
||||
- The results of ArcAGI-V2 were measured on the official evaluation set, which was not involved in the training process.
|
||||
</sup><br/><sup>
|
||||
- Generation configs for Seed-OSS-36B-Instruct: temperature=1.1, top_p=0.95. Specifically, for Taubench, temperature=1, top_p=0.7.
|
||||
</sup><br/><sup>
|
||||
</sup>
|
||||
|
||||
> [!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.
|
||||
|
||||
|
||||
|
||||
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.
|
||||
```
|
||||
<seed:think>
|
||||
Got it, let's try to solve this problem step by step. The problem says ... ...
|
||||
<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 used 393 tokens, and there are 119 tokens remaining for use.</seed:cot_budget_reflect>
|
||||
Because if ... ...
|
||||
<seed:cot_budget_reflect>I have exhausted my token budget, and now I will start answering the question.</seed:cot_budget_reflect>
|
||||
</seed:think>
|
||||
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.
|
||||
|
||||
@ -1,171 +0,0 @@
|
||||
{# ----------‑‑‑ special token variables ‑‑‑---------- #}
|
||||
{%- set bos_token = '<seed:bos>' -%}
|
||||
{%- set eos_token = '<seed:eos>' -%}
|
||||
{%- set pad_token = '<seed:pad>' -%}
|
||||
{%- set toolcall_begin_token = '<seed:tool_call>' -%}
|
||||
{%- set toolcall_end_token = '</seed:tool_call>' -%}
|
||||
{%- set think_begin_token = '<seed:think>' -%}
|
||||
{%- set think_end_token = '</seed:think>' -%}
|
||||
{%- set budget_begin_token = '<seed:cot_budget_reflect>'-%}
|
||||
{%- set budget_end_token = '</seed:cot_budget_reflect>'-%}
|
||||
{# -------------- reflection-interval lookup -------------- #}
|
||||
{%- if not thinking_budget is defined %}
|
||||
{%- set thinking_budget = -1 -%}
|
||||
{%- endif -%}
|
||||
{%- set budget_reflections_v05 = {
|
||||
0: 0,
|
||||
512: 128,
|
||||
1024: 256,
|
||||
2048: 512,
|
||||
4096: 512,
|
||||
8192: 1024,
|
||||
16384: 1024
|
||||
} -%}
|
||||
{# Find the first gear that is greater than or equal to the thinking_budget. #}
|
||||
{%- set ns = namespace(interval = None) -%}
|
||||
{%- for k, v in budget_reflections_v05 | dictsort -%}
|
||||
{%- if ns.interval is none and thinking_budget <= k -%}
|
||||
{%- set ns.interval = v -%}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{# If it exceeds the maximum gear, use the value of the last gear #}
|
||||
{%- if ns.interval is none -%}
|
||||
{%- set ns.interval = budget_reflections_v05[16384] -%}
|
||||
{%- endif -%}
|
||||
{# ---------- Preprocess the system message ---------- #}
|
||||
{%- if messages[0]["role"] == "system" %}
|
||||
{%- set system_message = messages[0]["content"] %}
|
||||
{%- set loop_messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{%- set loop_messages = messages %}
|
||||
{%- endif %}
|
||||
{# ---------- Ensure tools exist ---------- #}
|
||||
{%- if not tools is defined or tools is none %}
|
||||
{%- set tools = [] %}
|
||||
{%- endif %}
|
||||
{# tools2doc.jinja #}
|
||||
{%- macro py_type(t) -%}
|
||||
{%- if t == "string" -%}str
|
||||
{%- elif t in ("number", "integer") -%}int
|
||||
{%- elif t == "boolean" -%}bool
|
||||
{%- elif t == "array" -%}list
|
||||
{%- else -%}Any{%- endif -%}
|
||||
{%- endmacro -%}
|
||||
{# ---------- Output the system block ---------- #}
|
||||
{%- if system_message is defined %}
|
||||
{{ bos_token + "system\n" + system_message }}
|
||||
{%- else %}
|
||||
{%- if tools is iterable and tools | length > 0 %}
|
||||
{{ bos_token + "system\nYou are Doubao, a helpful AI assistant. You may call one or more functions to assist with the user query." }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if use_json_tooldef is defined and use_json_tooldef %}
|
||||
|
||||
{{"Tool List:\nYou are authorized to use the following tools (described in JSON Schema format). Before performing any task, you must decide how to call them based on the descriptions and parameters of these tools."}}
|
||||
{{ tools | tojson(ensure_ascii=False) }}
|
||||
{%- else %}
|
||||
{%- for item in tools if item.type == "function" %}
|
||||
|
||||
|
||||
Function:
|
||||
def {{ item.function.name }}(
|
||||
{%- for name, spec in item.function.parameters.properties.items() %}
|
||||
{{- name }}: {{ py_type(spec.type) }}{% if not loop.last %},{% endif %}
|
||||
{%- endfor %}):
|
||||
"""
|
||||
{{ item.function.description | trim }}
|
||||
|
||||
{# ---------- Args ---------- #}
|
||||
{%- if item.function.parameters.properties %}
|
||||
Args:
|
||||
{%- for name, spec in item.function.parameters.properties.items() %}
|
||||
|
||||
- {{ name }} ({{ py_type(spec.type) }})
|
||||
{%- if name in item.function.parameters.required %} [必填]{% else %} [选填]{% endif %}:
|
||||
{{- " " ~ (spec.description or "") }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
|
||||
{# ---------- Returns ---------- #}
|
||||
{%- if item.function.returns is defined
|
||||
and item.function.returns.properties is defined
|
||||
and item.function.returns.properties %}
|
||||
Returns:
|
||||
{%- for name, spec in item.function.returns.properties.items() %}
|
||||
|
||||
- {{ name }} ({{ py_type(spec.type) }}):
|
||||
{{- " " ~ (spec.description or "") }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
|
||||
"""
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{%- if tools is iterable and tools | length > 0 %}
|
||||
|
||||
{{"工具调用请遵循如下格式:\n<seed:tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>value_1</parameter>\n<parameter=example_parameter_2>This is the value for the second parameter\nthat can span\nmultiple lines</parameter>\n</function>\n</seed:tool_call>\n"}}
|
||||
{%- endif %}
|
||||
{# End the system block line #}
|
||||
{%- if system_message is defined or tools is iterable and tools | length > 0 %}
|
||||
{{ eos_token }}
|
||||
{%- endif %}
|
||||
{# ---------- Thinking Budget ---------- #}
|
||||
{%- if thinking_budget is defined %}
|
||||
{%- if thinking_budget == 0 %}
|
||||
{{ bos_token+"system" }}
|
||||
{{ "You are an intelligent assistant that can answer questions in one step without the need for reasoning and thinking, that is, your thinking budget is 0. Next, please skip the thinking process and directly start answering the user's questions." }}
|
||||
{{ eos_token }}
|
||||
{%- elif not thinking_budget == -1 %}
|
||||
{{ bos_token+"system" }}
|
||||
{{ "You are an intelligent assistant with reflective ability. In the process of thinking and reasoning, you need to strictly follow the thinking budget, which is "}}{{thinking_budget}}{{". That is, you need to complete your thinking within "}}{{thinking_budget}}{{" tokens and start answering the user's questions. You will reflect on your thinking process every "}}{{ns.interval}}{{" tokens, stating how many tokens have been used and how many are left."}}
|
||||
{{ eos_token }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{# ---------- List the historical messages one by one ---------- #}
|
||||
{%- for message in loop_messages %}
|
||||
{%- if message.role == "assistant"
|
||||
and message.tool_calls is defined
|
||||
and message.tool_calls is iterable
|
||||
and message.tool_calls | length > 0 %}
|
||||
{{ bos_token + message.role }}
|
||||
{%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
|
||||
{{ "\n" + think_begin_token + message.reasoning_content | trim + think_end_token }}
|
||||
{%- endif %}
|
||||
{%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
|
||||
{{ "\n" + message.content | trim + "\n" }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}{% set tool_call = tool_call.function %}{% endif %}
|
||||
{{ "\n" + toolcall_begin_token + "\n<function=" + tool_call.name + ">\n" }}
|
||||
{%- if tool_call.arguments is defined %}
|
||||
{%- for arg_name, arg_value in tool_call.arguments | items %}
|
||||
{{ "<parameter=" + arg_name + ">" }}
|
||||
{%- set arg_value = arg_value if arg_value is string else arg_value | string %}
|
||||
{{ arg_value+"</parameter>\n" }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{ "</function>\n" + toolcall_end_token }}
|
||||
{%- endfor %}
|
||||
{{ eos_token }}
|
||||
{%- elif message.role in ["user", "system"] %}
|
||||
{{ bos_token + message.role + "\n" + message.content + eos_token }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{ bos_token + message.role }}
|
||||
{%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
|
||||
{{ "\n" + think_begin_token + message.reasoning_content | trim + think_end_token }}
|
||||
{%- endif %}
|
||||
{%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
|
||||
{{ "\n" + message.content | trim + eos_token }}
|
||||
{%- endif %}
|
||||
{# Include the tool role #}
|
||||
{%- else %}
|
||||
{{ bos_token + message.role + "\n" + message.content + eos_token }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{# ---------- Control the model to start continuation ---------- #}
|
||||
{%- if add_generation_prompt %}
|
||||
{{ bos_token+"assistant\n" }}
|
||||
{%- if thinking_budget == 0 %}
|
||||
{{ think_begin_token + "\n" + budget_begin_token + "The current thinking budget is 0, so I will directly start answering the question." + budget_end_token + "\n" + think_end_token }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
33
config.json
33
config.json
@ -1,33 +0,0 @@
|
||||
{
|
||||
"architectures": [
|
||||
"SeedOssForCausalLM"
|
||||
],
|
||||
"attention_bias": true,
|
||||
"attention_dropout": 0.1,
|
||||
"attention_out_bias": false,
|
||||
"bos_token_id": 0,
|
||||
"pad_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 5120,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 27648,
|
||||
"max_position_embeddings": 524288,
|
||||
"mlp_bias": false,
|
||||
"model_type": "seed_oss",
|
||||
"num_attention_heads": 80,
|
||||
"num_hidden_layers": 64,
|
||||
"num_key_value_heads": 8,
|
||||
"residual_dropout": 0.1,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"rope_type": "default"
|
||||
},
|
||||
"rope_theta": 10000000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.55.0",
|
||||
"use_cache": true,
|
||||
"vocab_size": 155136
|
||||
}
|
||||
@ -1 +0,0 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
@ -1,10 +0,0 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 0,
|
||||
"pad_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"transformers_version": "4.55.0",
|
||||
"temperature": 1.1,
|
||||
"top_p": 0.95
|
||||
}
|
||||
|
||||
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"model.layers.9.mlp.up_proj.weight": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00003-of-00015.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00003-of-00015.safetensors",
|
||||
"model.norm.weight": "model-00015-of-00015.safetensors"
|
||||
}
|
||||
}
|
||||
@ -1,23 +0,0 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<seed:bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<seed:eos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<seed:pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
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tokenizer.json
(Stored with Git LFS)
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Reference in New Issue
Block a user