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

Update README.md

0226389 over 2 years ago

5.02 kB

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.
TransNormerLLM3-15B is purely intergrated with Lightning Attention-2, 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 <<<>>> wechat group

--23.12.25-- startup: WeChat - 预训练启航 <<<>>> Twitter - Pre-training Commences <<<>>> YouTube Recording <<<>>> bilibili 回放
--24.01.02-- first week review: WeChat - 第一周概览 <<<>>> Twitter - First Week Review
--24.01.09-- second week review: WeChat - 第二周概览 <<<>>> Twitter - Second 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	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 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}
}

@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}
}

- OpenNLPLab @2024 -