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 ---
-license: Apache License 2.0
-
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
-
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
-
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
-
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
-
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
-
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
+license: cc-by-4.0
+tags:
+- alignment
+- value alignment
+- AI safety
+- safety
+- LLM
+- history
+datasets:
+- PKU-Alignment/ProgressGym-HistText
+- PKU-Alignment/ProgressGym-TimelessQA
+base_model:
+- PKU-Alignment/ProgressGym-HistLlama3-8B-C019-pretrain
+- meta-llama/Meta-Llama-3-8B
 ---
-### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型

-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
-```
-```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('PKU-Alignment/ProgressGym-HistLlama3-8B-C019-instruct-v0.2')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/PKU-Alignment/ProgressGym-HistLlama3-8B-C019-instruct-v0.2.git
+# ProgressGym-HistLlama3-8B-C019-instruct
+
+## Overview
+
+#### The ProgressGym Framework
+
+![Framework Diagram](./readme-assets/main-diagram.png)
+
+**ProgressGym-HistLlama3-8B-C019-instruct** is part of the **ProgressGym** framework for research and experimentation on *progress alignment* - the emulation of moral progress in AI alignment algorithms, as a measure to prevent risks of societal value lock-in. 
+
+To quote the paper [*ProgressGym: Alignment with a Millennium of Moral Progress*](https://arxiv.org/abs/2406.20087):
+
+> Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users. Such influence can reinforce prevailing societal values, potentially contributing to the lock-in of misguided moral beliefs and, consequently, the perpetuation of problematic moral practices on a broad scale. 
+>
+> We introduce *progress alignment* as a technical solution to mitigate this imminent risk. Progress alignment algorithms learn to emulate the mechanics of human moral progress, thereby addressing the susceptibility of existing alignment methods to contemporary moral blindspots.
+
+#### ProgressGym-HistLlama3-8B-C019-instruct
+
+ProgressGym-HistLlama3-8B-C019-instruct is one of the **36 historical language models** in the ProgressGym framework. 
+
+**ProgressGym-HistLlama3-8B-C019-instruct is under continual iteration.** Improving upon the current version, new versions of the model are currently being trained to reflect historical moral tendencies in ever more comprehensive ways.
+
+**ProgressGym-HistLlama3-8B-C019-instruct is a 19th-century historical language model.** Based on [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B), It is continued-pretrained on the 19th-century text data from [ProgressGym-HistText](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-HistText), using the following hyperparameters:
+
+- learning_rate: 1.5e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 64
+- total_eval_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: polynomial
+- lr_scheduler_warmup_steps: 20
+- num_epochs: 4.0
+- mixed_precision_training: Native AMP
+
+... with the following training results:
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 2.3809        | 0.1923 | 200  | 2.4207          |
+| 2.3057        | 0.3846 | 400  | 2.3750          |
+| 2.35          | 0.5769 | 600  | 2.3477          |
+| 2.3291        | 0.7692 | 800  | 2.3324          |
+| 2.2998        | 0.9615 | 1000 | 2.3237          |
+| 2.1248        | 1.1538 | 1200 | 2.3361          |
+| 2.1239        | 1.3462 | 1400 | 2.3344          |
+| 2.1521        | 1.5385 | 1600 | 2.3338          |
+| 2.1359        | 1.7308 | 1800 | 2.3336          |
+| 2.0531        | 1.9231 | 2000 | 2.3332          |
+| 2.0783        | 2.1154 | 2200 | 2.3357          |
+| 2.0952        | 2.3077 | 2400 | 2.3360          |
+| 2.1009        | 2.5    | 2600 | 2.3361          |
+| 2.125         | 2.6923 | 2800 | 2.3360          |
+| 2.1206        | 2.8846 | 3000 | 2.3360          |
+| 2.0593        | 3.0769 | 3200 | 2.3363          |
+| 2.0927        | 3.2692 | 3400 | 2.3365          |
+| 2.093         | 3.4615 | 3600 | 2.3368          |
+| 2.066         | 3.6538 | 3800 | 2.3363          |
+| 2.1086        | 3.8462 | 4000 | 2.3362          |
+
+Note that the training data volume for the continued pretraining stage is capped at 3GB. When the corresponding century's corpus exceeds this volume, the training data is randomly sampled to fit the volume.
+
+**ProgressGym-HistLlama3-8B-C019-instruct is an instruction-tuned language model.** It is tuned on [ProgressGym-TimelessQA](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-TimelessQA), using the following hyperparameters. Note, however, that the snapshot at training step 10 is used for the final model, to minimize erosion of the value tendencies learned during continued pretraining; we qualitatively observe that this snapshot still possesses strong instruction-following capabilities.
+- learning_rate: 1.5e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 64
+- total_eval_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: polynomial
+- lr_scheduler_warmup_steps: 20
+- num_epochs: 4.0
+- mixed_precision_training: Native AMP
+
+... with the following training results:
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.8164        | 0.4167 | 20   | 0.8431          |
+| 0.7962        | 0.8333 | 40   | 0.8462          |
+| 0.4335        | 1.25   | 60   | 0.8650          |
+| 0.4578        | 1.6667 | 80   | 0.8533          |
+| 0.3944        | 2.0833 | 100  | 0.8484          |
+| 0.3997        | 2.5    | 120  | 0.8528          |
+| 0.3752        | 2.9167 | 140  | 0.8573          |
+| 0.3697        | 3.3333 | 160  | 0.8608          |
+| 0.3636        | 3.75   | 180  | 0.8634          |
+
+
+## Links
+
+- **[Paper Preprint]**  [ProgressGym: Alignment with a Millennium of Moral Progress](https://arxiv.org/abs/2406.20087)
+- **[Leaderboard & Interactive Playground]** [PKU-Alignment/ProgressGym-LeaderBoard](https://huggingface.co/spaces/PKU-Alignment/ProgressGym-LeaderBoard)
+- **[Huggingface Data & Model Collection]** [PKU-Alignment/ProgressGym](https://huggingface.co/collections/PKU-Alignment/progressgym-666735fcf3e4efa276226eaa)
+- **[Github Codebase]** [PKU-Alignment/ProgressGym](https://github.com/PKU-Alignment/ProgressGym)
+- **[Documentation]** [ProgressGym Documentation](https://pku-alignment.github.io/ProgressGym/)
+- **[PyPI Package]** *(coming soon - [stay tuned](https://forms.gle/1TWFLL4ZCLeYTD5N6)!)*
+
+## Citation
+
+If the datasets, models, or framework of ProgressGym help you in your project, please cite ProgressGym using the bibtex entry below.
+
+```text
+@article{progressgym,
+  title={ProgressGym: Alignment with a Millennium of Moral Progress},
+  author={Tianyi Qiu and Yang Zhang and Xuchuan Huang and Jasmine Xinze Li and Jiaming Ji and Yaodong Yang},
+  journal={arXiv preprint arXiv:2406.20087},
+  eprint={2406.20087},
+  eprinttype = {arXiv},
+  year={2024}
+}
 ```

-<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+## Ethics Statement
+
+- **Copyright information of historical text data sources**:
+  - Project Gutenberg, one among our four source of our historical text data, consists only of texts in the public domain.
+  - For the text that we draw from Internet Archive, we only include those that uploaded by *Library of Congress*, which are texts freely released online by the U.S. Library of Congress for research and public use.
+  - The text data from Early English Books Online are, according to their publisher, "freely available to the public" and "available for access, distribution, use, or reuse by anyone".
+  - The last remaining source of our historical text data, the Pile of Law dataset, is released under a Creative Commons license, which we adhere to in our use.
+- **Reproducibility**: To ensure reproducibility, we open-source all the code involved in the production of our main results (including the entire pipeline starting from data collection and model training), as well as the supporting infrastructure (the ProgressGym framework), making replication as easy as running a few simple script files.
+- **Misuse Prevention**: In order to prevent potential misuse of progress alignment algorithms, we have carefully formulated progress alignment as strictly value-neutral, without *a priori* assumptions on the direction of progress. In the event of potential misuse of our dataset, we condemn any misuse attempt to the strongest degree possible, and will work with the research community on whistleblowing for such attempts. 
+- **Open-Sourcing**: We confirm that our code, data, and models are to be open-sourced under a CC-BY 4.0 license. We will continue to maintain and update our open-source repositories and models.