license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ' TransNormerLLM'
TransNormerLLM3 -- A Faster and Better LLM
Introduction
This official repository unveils the TransNormerLLM3 model along with its open-source weights for every 50 billion tokens processed during pre-training.
TransNormerLLM evolving from TransNormer, 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.
TransNormerLLM3
- 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.
- Titoken tokenizer is used with a total vocabulary size of about 100,000.
- It incorporates Simple GLU for its channel mixer, GLA in the token mixer, and SRMSNorm for normalization.
- In terms of position encoding, the first layer employs LRPE with exponential decay, whereas the subsequent layers continue with exponential decay encoding.
Pre-training Logbook
- Realtime Track: https://api.wandb.ai/links/opennlplab/kip314lq
- Join to dicussion: discord <<<>>> wechat group
startup: WeChat - 预训练启航 <<<>>> Twitter - Pre-training Commences <<<>>> YouTube Recording <<<>>> bilibili 回放
first week review: WeChat - 第一周概览 <<<>>> Twitter - First Week Review
Released Weights
| param | token | Hugging Face | Model Scope | Wisemodel |
|---|---|---|---|---|
| 15B | 50B | 🤗 | 🤖 | 🐯 |
Benchmark Results
The evaluations of all models are conducted using the official settings and the lm-evaluation-harness framework.
| Model | P | T | BoolQ | PIQA | HS | WG | ARC-e | ARC-c | OBQA | MMLU | C-Eval |
|---|---|---|---|---|---|---|---|---|---|---|---|
| TransNormerLLM3-15B | 15 | 0.05 | 62.08 | 72.52 | 55.55 | 57.14 | 62.12 | 31.14 | 32.40 | 27.50 | 26.18 |
| TransNormerLLM3-15B | 15 | 0.10 |
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.
Acknowledgments and Citation
Acknowledgments
Our project is developed based on the following open source projects:
- tiktoken for the tokenizer.
- metaseq for training.
- lm-evaluation-harness for evaluation.
Citation
If you wish to cite our work, please use the following reference:
@article{qin2023scaling,
title={Scaling transnormer to 175 billion parameters},
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},
journal={arXiv preprint arXiv:2307.14995},
year={2023}
}
- OpenNLPLab @2024 -