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- README.md +57 -3
- Results/Account/account.txt +1 -0
- Results/BBH/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-08-01T00-53-06.421267.json +0 -0
- Results/CEVAL-VALID/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T14-03-55.157974.json +0 -0
- Results/CMMLU/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T13-52-48.618780.json +0 -0
- Results/GSM8K/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T11-35-13.650950.json +157 -0
- Results/Law/law.txt +1 -0
- Results/MATHQA/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T12-41-13.601625.json +120 -0
- Results/MMLU/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T13-42-48.339284.json +0 -0
- added_tokens.json +5 -0
- config.json +28 -0
- configuration.json +1 -0
- generation_config.json +6 -0
- latest +1 -0
- merges.txt +0 -0
- model.safetensors.index.json +970 -0
- rng_state_0.pth +3 -0
- rng_state_1.pth +3 -0
- rng_state_10.pth +3 -0
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README.md
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# 灵智大模型 - 垂直领域行业专家
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🌐 [官方网站，欢迎访问](https://ailingzhi.com)
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## ✨ 亮点
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- 从Qwen2-base完美复现了Qwen2-chat，并公开了训练数据；
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- 在垂类领域训练场景下，灵智模型能够在提升垂类领域性能的同时也保持了通用领域的性能；
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- 对多种训练范式（例如直接指令微调，先持续预训练再指令微调等八种范式）做了总结，并针对不同的模型大小采取了最佳的训练范式；
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- 开源了8个灵智模型：`Lingzhi-0.5B-chat`, `Lingzhi-0.8B-chat`, `Lingzhi-1.5B-chat`, `Lingzhi-2.7B-chat`, `Lingzhi-7B-chat`, `Lingzhi-10B-chat`, `Lingzhi-57MOE14B-chat`, `Lingzhi-72B-chat`.
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## 📄 摘要
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在实际应用中，当预训练数据不可用时，进行**持续训练**是很常见的。然而，持续训练往往会在增强领域特定技能的同时导致大语言模型（LLMs）灾难性地遗忘其通用能力。在本文中，我们首先对八种常见的持续训练范式进行了实证研究，然后选择了最佳范式来训练灵智系列模型。实验表明，灵智能够在保持通用能力的同时增强领域特定的性能。我们已经开源了所有模型、训练数据和基准测试，用户可以将它们应用到自己的领域特定区域。
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## 📘 介绍
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大语言模型（LLMs）近年来因其在各种实际下游任务中的出色表现而备受关注。实际上，尽管现有的LLMs在通用领域表现良好，但由于在预训练或指令微调期间缺乏特定领域的专业暴露，它们可能在用户需要的特定领域（如会计、法律、金融）中表现不佳。
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为了提升LLMs在特定领域的表现，我们需要收集相应的数据进行持续学习，如持续预训练（CPT）或有监督微调（SFT）。然而，我们注意到，仅在特定领域进行持续学习可能导致通用能力的灾难性遗忘，如规划、指令执行、数学、编程和自然语言理解等。
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为了同时保持通用和领域特定能力，通常会部署一个未修改的原生模型用于通用任务，而一个微调模型用于专业任务。这将对计算硬件资源（如GPU和内存）提出巨大的需求，从而阻碍商业部署。众所周知，上述现象是业界面临的一个非常棘手的问题。因此，一个值得研究的问题出现了：如何在持续学习过程中提高领域特定的表现，而不损害通用能力？
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为了解决这个问题，我们进行了实证研究，探索了各种持续学习范式并总结了它们的优缺点。最终，在实证研究之后，我们选择了最佳的学习范式和训练数据，基于Qwen2-base进行持续学习，衍生出我们的灵智系列模型。经过大量实验，灵智能够在多个特定领域中表现出色，同时在通用能力方面也表现出与原始Qwen2-chat模型相当的性能。
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## 📊 结果
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> 备注：Baselines中Qwen2的所有结果均是在我们统一的环境下进行评测的。
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| **Base Model** | **General** | | | | | | | | **Domains** | | **Avg.** |
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| :-------------------- | :---------- | :----- | :---------- | :----- | :------- | :----- | :-------- | :----- | :---------- | :----- | :------- |
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| | **English** | | **Chinese** | | **Math** | | **Code** | | | | |
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| | MMLU | BBH | C-Eval | CMMLU | GSM8K | MathQA | HumanEval | MBPP | Account | Law | |
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| ***Baselines*** | | | | | | | | | | | |
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| Qwen2-0\.5B-chat | 43\.30 | 10\.35 | 54\.16 | 53\.57 | 33\.97 | 25\.76 | 20\.73 | 12\.40 | 17\.01 | 25\.00 | 29\.62 |
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| Qwen2-1\.5B-chat | 55\.73 | 9\.55 | 69\.32 | 70\.13 | 54\.21 | 32\.93 | 42\.68 | 20\.60 | 32\.65 | 42\.07 | 42\.99 |
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| Qwen2-7B-chat | 69\.82 | 30\.56 | 81\.58 | 81\.77 | 66\.26 | 44\.09 | 72\.56 | 42\.20 | 55\.10 | 59\.15 | 60\.31 |
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| Qwen2-57MOE14B-chat | | | | | | | | | | | |
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| Qwen2-72B-chat | | | | | | | | | | | |
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| ***Lingzhi Models*** | | | | | | | | | | | |
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| Lingzhi-0\.5B-base | 44\.25 | 25\.65 | 55\.05 | 53\.74 | 29\.34 | 29\.18 | 25\.00 | 22\.40 | 25\.85 | 40\.24 | 35\.07 |
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| Lingzhi-0\.8B-chat | 42\.93 | 27\.77 | 53\.34 | 50\.98 | 21\.00 | 28\.84 | 28\.66 | 18\.00 | 24\.49 | 40\.85 | 33\.69 |
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| Lingzhi-1\.5B-base | 55\.35 | 33\.67 | 69\.47 | 69\.10 | 49\.58 | 35\.31 | 39\.02 | 31\.00 | 37\.41 | 42\.68 | 46\.26 |
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| Lingzhi-2\.7B-chat | 53\.65 | 36\.77 | 67\.09 | 67\.39 | 46\.02 | 34\.51 | 40\.85 | 30\.00 | 38\.10 | 60\.98 | 47\.54 |
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| Lingzhi-7B-base | 69\.06 | 58\.95 | 82\.69 | 83\.05 | 74\.22 | 45\.59 | 56\.10 | 49\.80 | 72\.79 | 89\.02 | 68\.13 |
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| Lingzhi-10B-chat | 69\.37 | 64\.37 | 81\.50 | 82\.27 | 76\.19 | 46\.00 | 60\.98 | 50\.40 | 70\.07 | 82\.93 | 68\.41 |
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| Lingzhi-57MOE14B-chat | | | | | | | | | | | |
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| Lingzhi-72B-chat | | | | | | | | | | | |
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## 📚 引用
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<span style="color:orange;">⚠️ **警告**</span> 如果您用到了我们的模型和数据，请使用以下参考文献。
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```
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@misc{lingzhi,
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title={Lingzhi: Improving Domain-Specific Performance without Compromising General Capabilities},
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author={Daoguang Zan, Lei Yu, Ailun Yu, Zhirong Huang, Zongshuai Ruan, Pengjie Huang},
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year={2024},
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note={All authors contributed equally. The computational power required to train the Lingzhi models (12*8 H800 80G) was provided by Lingzhi AI. Special thanks to them.}
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}
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```
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Results/Account/account.txt
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109/147: 0.7414965986394558
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Results/BBH/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-08-01T00-53-06.421267.json
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Results/CEVAL-VALID/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T14-03-55.157974.json
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Results/CMMLU/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T13-52-48.618780.json
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Results/GSM8K/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T11-35-13.650950.json
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{
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"results": {
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"gsm8k": {
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"alias": "gsm8k",
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"exact_match,strict-match": 0.8946171341925702,
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"exact_match_stderr,strict-match": 0.008457575884041755,
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"exact_match,flexible-extract": 0.8938589840788476,
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"exact_match_stderr,flexible-extract": 0.00848434694843459
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}
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},
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"group_subtasks": {
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"gsm8k": []
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},
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"configs": {
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"gsm8k": {
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"task": "gsm8k",
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"tag": [
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"math_word_problems"
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],
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"dataset_path": "gsm8k",
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"dataset_name": "main",
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"training_split": "train",
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"test_split": "test",
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"fewshot_split": "train",
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"doc_to_text": "Question: {{question}}\nAnswer:",
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"doc_to_target": "{{answer}}",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 5,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true,
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"ignore_case": true,
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"ignore_punctuation": false,
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"regexes_to_ignore": [
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",",
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"(?s).*#### ",
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"\\.$"
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]
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Question:",
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"</s>",
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"<|im_end|>"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"filter_list": [
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{
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"name": "strict-match",
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"filter": [
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{
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"function": "regex",
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"regex_pattern": "#### (\\-?[0-9\\.\\,]+)"
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},
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{
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"function": "take_first"
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}
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]
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},
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{
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"name": "flexible-extract",
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"filter": [
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{
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"function": "regex",
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"regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)"
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},
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{
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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Results/Law/law.txt
ADDED
|
@@ -0,0 +1 @@
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| 1 |
+
145/164: 0.8841463414634146
|
Results/MATHQA/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T12-41-13.601625.json
ADDED
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@@ -0,0 +1,120 @@
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| 91 |
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"pretty_env_info": "PyTorch version: 2.4.0a0+07cecf4168.nv24.05\nIs debug build: False\nCUDA used to build PyTorch: 12.4\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.29.2\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.4.131\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H800\nGPU 1: NVIDIA H800\nGPU 2: NVIDIA H800\nGPU 3: NVIDIA H800\nGPU 4: NVIDIA H800\nGPU 5: NVIDIA H800\nGPU 6: NVIDIA H800\nGPU 7: NVIDIA H800\n\nNvidia driver version: 535.104.12\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8468\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nFrequency boost: enabled\nCPU max MHz: 2101.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear pconfig flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.24.4\n[pip3] onnx==1.16.0\n[pip3] optree==0.11.0\n[pip3] pytorch-quantization==2.1.2\n[pip3] pytorch-triton==3.0.0+989adb9a2\n[pip3] torch==2.4.0a0+07cecf4168.nv24.5\n[pip3] torch-tensorrt==2.4.0a0\n[pip3] torchvision==0.19.0a0\n[conda] Could not collect",
|
| 92 |
+
"transformers_version": "4.41.2",
|
| 93 |
+
"upper_git_hash": null,
|
| 94 |
+
"tokenizer_pad_token": [
|
| 95 |
+
"<|endoftext|>",
|
| 96 |
+
"151643"
|
| 97 |
+
],
|
| 98 |
+
"tokenizer_eos_token": [
|
| 99 |
+
"<|im_end|>",
|
| 100 |
+
"151645"
|
| 101 |
+
],
|
| 102 |
+
"tokenizer_bos_token": [
|
| 103 |
+
null,
|
| 104 |
+
"None"
|
| 105 |
+
],
|
| 106 |
+
"eot_token_id": 151645,
|
| 107 |
+
"max_length": 131072,
|
| 108 |
+
"task_hashes": {},
|
| 109 |
+
"model_source": "hf",
|
| 110 |
+
"model_name": "/gemini/platform/public/trained_models/sft/Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00/checkpoint-300",
|
| 111 |
+
"model_name_sanitized": "__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300",
|
| 112 |
+
"system_instruction": null,
|
| 113 |
+
"system_instruction_sha": null,
|
| 114 |
+
"fewshot_as_multiturn": false,
|
| 115 |
+
"chat_template": null,
|
| 116 |
+
"chat_template_sha": null,
|
| 117 |
+
"start_time": 15616659.029428756,
|
| 118 |
+
"end_time": 15616936.2965863,
|
| 119 |
+
"total_evaluation_time_seconds": "277.2671575434506"
|
| 120 |
+
}
|
Results/MMLU/__gemini__platform__public__trained_models__sft__Qwen2_72B_SFT-2024.07.27-label.lingzhichat+account+law-exp.00__checkpoint-300/results_2024-07-31T13-42-48.339284.json
ADDED
|
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|
added_tokens.json
ADDED
|
@@ -0,0 +1,5 @@
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| 1 |
+
{
|
| 2 |
+
"<|endoftext|>": 151643,
|
| 3 |
+
"<|im_end|>": 151645,
|
| 4 |
+
"<|im_start|>": 151644
|
| 5 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,28 @@
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|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "/gemini/platform/public/llm/huggingface/Qwen/Qwen2-72B",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"Qwen2ForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 151643,
|
| 8 |
+
"eos_token_id": 151643,
|
| 9 |
+
"hidden_act": "silu",
|
| 10 |
+
"hidden_size": 8192,
|
| 11 |
+
"initializer_range": 0.02,
|
| 12 |
+
"intermediate_size": 29568,
|
| 13 |
+
"max_position_embeddings": 131072,
|
| 14 |
+
"max_window_layers": 28,
|
| 15 |
+
"model_type": "qwen2",
|
| 16 |
+
"num_attention_heads": 64,
|
| 17 |
+
"num_hidden_layers": 80,
|
| 18 |
+
"num_key_value_heads": 8,
|
| 19 |
+
"rms_norm_eps": 1e-05,
|
| 20 |
+
"rope_theta": 1000000.0,
|
| 21 |
+
"sliding_window": 131072,
|
| 22 |
+
"tie_word_embeddings": false,
|
| 23 |
+
"torch_dtype": "float16",
|
| 24 |
+
"transformers_version": "4.42.2",
|
| 25 |
+
"use_cache": false,
|
| 26 |
+
"use_sliding_window": false,
|
| 27 |
+
"vocab_size": 152064
|
| 28 |
+
}
|
configuration.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"framework":"Pytorch","task":"text-generation"}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
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| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"eos_token_id": 151643,
|
| 4 |
+
"max_new_tokens": 2048,
|
| 5 |
+
"transformers_version": "4.42.2"
|
| 6 |
+
}
|
latest
ADDED
|
@@ -0,0 +1 @@
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|
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|
|
|
| 1 |
+
global_step300
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,970 @@
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