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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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pipeline_tag: text-generation |
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tags: |
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- ' TransNormerLLM' |
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--- |
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<div align="center"> |
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<h1> |
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TransNormerLLM3 -- A Faster and Better LLM |
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</h1> |
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</div> |
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# Introduction |
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This official repository unveils the TransNormerLLM3 model along with its open-source weights for every 50 billion tokens processed during pre-training. |
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[TransNormerLLM](https://arxiv.org/abs/2307.14995) evolving from [TransNormer](https://arxiv.org/abs/2210.10340), standing out as the first LLM within the linear transformer architecture. Additionally, it distinguishes itself by being the first non-Transformer LLM to exceed both traditional Transformer and other efficient Transformer models (such as, RetNet and Mamba) in terms of speed and performance. |
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# TransNormerLLM3 |
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- **TransNormerLLM3-15B** features **14.83 billion** parameters. It is structured with **42 layers**, includes **40 attention heads**, and has a total **embedding size of 5120**. |
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- **TransNormerLLM3-15B** is purely intergrated with **[Lightning Attention-2](http://arxiv.org/abs/2401.04658)**, which can maintain a **stable TGS** during training of **unlimited sequence lengths**, up until encountering firm limitations like GPU memory constraints. |
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- **Titoken** tokenizer is used with a total **vocabulary size** of about **100,000**. |
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<p align="center"> |
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<img src="./images/TransNormer3.jpg" width="65%" /> |
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</p> |
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### Pre-training Logbook |
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* Realtime Track: https://api.wandb.ai/links/opennlplab/kip314lq |
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* Join to dicussion: [discord](https://discord.gg/JEU3nTcWKC) <<<>>> [wechat group](https://github.com/OpenNLPLab/TransnormerLLM/blob/main/images/contact_me_qr.png) |
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> --23.12.25-- startup: [WeChat - 预训练启航](https://mp.weixin.qq.com/s/YjUY-uy89WkF75_-rBTuKw) <<<>>> [Twitter - Pre-training Commences ](https://twitter.com/opennlplab/status/1739568669502611825) <<<>>> [YouTube Recording](https://t.co/wk7svS4o5r) <<<>>> [bilibili 回放](https://www.bilibili.com/video/BV11j411J7Dy) |
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> --24.01.02-- first week review: [WeChat - 第一周概览](https://mp.weixin.qq.com/s/zwGnZZI3itNPoxzzXkuU2w) <<<>>> [Twitter - First Week Review](https://twitter.com/opennlplab/status/1742187694078501038) |
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> --24.01.09-- second week review: [WeChat - 第二周概览](https://mp.weixin.qq.com/s/6D0qi-0aBier05OKuHfPEA) <<<>>> [Twitter - Second Week Review](https://twitter.com/opennlplab/status/1744720007299523063) |
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> --24.01.15-- third week review: [WeChat - 第三周概览](https://mp.weixin.qq.com/s/EQg8evZ2cNtAk4HruwCXPA) <<<>>> [Twitter - Third Week Review](https://twitter.com/opennlplab/status/1746920293069910190) |
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# Released Weights |
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| param | token | Hugging Face | Model Scope | Wisemodel | |
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| :-----: | :---: | :--------------------------------------------------------------------------------------------------------------------: | :---------: | :-------: | |
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| **15B** | 50B | 🤗[step13000](https://huggingface.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints/tree/step13000-50Btokens) | 🤖 | 🐯 | |
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| **15B** | 100B | 🤗[step26000](https://huggingface.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints/tree/step26000-100Btokens) | 🤖 | 🐯 | |
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| **15B** | 150B | 🤗[step39000](https://huggingface.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints/tree/step39000-150Btokens) | 🤖 | 🐯 | |
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| **15B** | 200B | 🤗[step52000](https://huggingface.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints/tree/step52000-200Btokens) | 🤖 | 🐯 | |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", revision='step26000-100Btokens', trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", torch_dtype=torch.bfloat16, revision='step26000-100Btokens', device_map="auto", trust_remote_code=True) |
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``` |
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# Benchmark Results |
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The evaluations of all models are conducted using the official settings and the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) framework. |
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| Model | P | T | BoolQ | PIQA | HS | WG | ARC-e | ARC-c | OBQA | C-Eval | MMLU | |
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| ----------------------- | --- | ---- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ------ | ----- | |
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| **TransNormerLLM3-15B** | 15 | 0.05 | 62.08 | 72.52 | 55.55 | 57.14 | 62.12 | 31.14 | 32.40 | 26.18 | 27.50 | |
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| **TransNormerLLM3-15B** | 15 | 0.10 | 63.98 | 74.70 | 61.09 | 61.33 | 65.95 | 34.64 | 35.60 | 25.38 | 27.40 | |
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| **TransNormerLLM3-15B** | 15 | 0.15 | 60.34 | 75.08 | 63.99 | 62.04 | 64.56 | 34.90 | 35.20 | 22.64 | 26.60 | |
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| **TransNormerLLM3-15B** | 15 | 0.20 | 52.05 | 74.48 | 64.72 | 62.75 | 66.16 | 35.15 | 36.80 | 27.25 | 30.80 | |
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| **TransNormerLLM3-15B** | 15 | 0.25 | 66.70 | 76.50 | 66.51 | 64.80 | 66.84 | 36.18 | 39.40 | 30.87 | 36.10 | |
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| **TransNormerLLM3-15B** | 15 | 0.30 | 67.00 | 76.50 | 67.17 | 64.40 | 66.29 | 36.77 | 38.80 | 33.99 | 37.60 | |
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> **P**: parameter size (billion). **T**: tokens (trillion). **BoolQ**: acc. **PIQA**: acc. **HellaSwag**: acc_norm. **WinoGrande**: acc. **ARC-easy**: acc. **ARC-challenge**: acc_norm. **OpenBookQA**: acc_norm. **MMLU**: 5-shot acc. **C-Eval**: 5-shot acc. |
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```bash |
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# Please configure the following settings when do evaluation |
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export do_eval=True |
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export use_triton=False |
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``` |
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# Acknowledgments and Citation |
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## Acknowledgments |
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Our project is developed based on the following open source projects: |
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- [tiktoken](https://github.com/openai/tiktoken) for the tokenizer. |
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- [metaseq](https://github.com/facebookresearch/metaseq) for training. |
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- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) for evaluation. |
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## Citation |
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If you wish to cite our work, please use the following reference: |
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``` |
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@article{qin2023scaling, |
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title={Scaling transnormer to 175 billion parameters}, |
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author={Qin, Zhen and Li, Dong and Sun, Weigao and Sun, Weixuan and Shen, Xuyang and Han, Xiaodong and Wei, Yunshen and Lv, Baohong and Yuan, Fei and Luo, Xiao and others}, |
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journal={arXiv preprint arXiv:2307.14995}, |
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year={2023} |
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} |
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@misc{qin2024lightning, |
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title={Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models}, |
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author={Zhen Qin and Weigao Sun and Dong Li and Xuyang Shen and Weixuan Sun and Yiran Zhong}, |
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year={2024}, |
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eprint={2401.04658}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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<p align="center"> |
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<img src="./images/lightning3-leopard.jpg" width="50%" /> |
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- OpenNLPLab @2024 - |
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</p> |
