Instructions to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints

SGLang

How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Docker Model Runner:
```
docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints
```

TransNormerLLM3-15B-Intermediate-Checkpoints / README.md

OpenNLPLab

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	---
	license: apache-2.0
	language:
	- en
	- zh
	pipeline_tag: text-generation
	tags:
	- ' TransNormerLLM'
	---

	<div align="center">
	<h1>
	TransNormerLLM3 -- A Faster and Better LLM
	</h1>
	</div>

	# Introduction

	This official repository unveils the TransNormerLLM3 model along with its open-source weights for every 50 billion tokens processed during pre-training.

	[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.


	# 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.
	- 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.
	- 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](https://discord.gg/MYQh6BWN) <<<>>> [wechat group](https://github.com/OpenNLPLab/TransnormerLLM/blob/main/images/contact_me_qr.png)

	> --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)
	> --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)
	> --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)


	# 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](https://github.com/EleutherAI/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 \| 63.98 \| 74.70 \| 61.09 \| 61.33 \| 65.95 \| 34.64 \| 35.60 \| 25.38 \| 27.40 \|
	\| TransNormerLLM3-15B \| 15 \| 0.15 \| 60.34 \| 75.08 \| 63.99 \| 62.04 \| 64.56 \| 34.90 \| 35.20 \| 22.64 \| 26.60 \|



	> 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](https://github.com/openai/tiktoken) for the tokenizer.
	- [metaseq](https://github.com/facebookresearch/metaseq) for training.
	- [lm-evaluation-harness](https://github.com/EleutherAI/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}
	}

	@misc{qin2024lightning,
	title={Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models},
	author={Zhen Qin and Weigao Sun and Dong Li and Xuyang Shen and Weixuan Sun and Yiran Zhong},
	year={2024},
	eprint={2401.04658},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	<p align="center">
	- OpenNLPLab @2024 -
	</p>