Instructions to use DAMO-NLP-SG/CLEX-7B-Chat-16K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DAMO-NLP-SG/CLEX-7B-Chat-16K with Transformers:

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

pipe = pipeline("text-generation", model="DAMO-NLP-SG/CLEX-7B-Chat-16K", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/CLEX-7B-Chat-16K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-7B-Chat-16K", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DAMO-NLP-SG/CLEX-7B-Chat-16K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DAMO-NLP-SG/CLEX-7B-Chat-16K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-SG/CLEX-7B-Chat-16K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DAMO-NLP-SG/CLEX-7B-Chat-16K

SGLang

How to use DAMO-NLP-SG/CLEX-7B-Chat-16K 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 "DAMO-NLP-SG/CLEX-7B-Chat-16K" \
    --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": "DAMO-NLP-SG/CLEX-7B-Chat-16K",
		"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 "DAMO-NLP-SG/CLEX-7B-Chat-16K" \
        --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": "DAMO-NLP-SG/CLEX-7B-Chat-16K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DAMO-NLP-SG/CLEX-7B-Chat-16K with Docker Model Runner:
```
docker model run hf.co/DAMO-NLP-SG/CLEX-7B-Chat-16K
```

CLEX-7B-Chat-16K / README.md

Guanzheng

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metadata

license: mit
language:
  - en
metrics:
  - perplexity

CLEX: Continuous Length Extrapolation for Large Language Models

This repo stores the checkpoint of CLEX-7B-Chat-16K

Features and Highlights of CLEX

Simple and Clear: MINIMAL code and architecture changes. Only one up-and-down projection layer introduced, NO recurrent memory caching or sparse attention required.
Train Short, Test Long: NO performance drop on the sequences 4x~8x longer than the training ones (see here).
Continuous Length Extrapolation: Explicitly modeling the continuous dynamics of context window size during length extrapolation.

More details about long-text modeling with our CLEX can be found at the git repo.

Model Zoo

Model Name	Model Type	Starting Point	Train Data	Train Length	MAX Test Length
CLEX-7B-4K	base	LLaMA-2-7B	Redpajama-Book	4K	16K
CLEX-7B-Chat-4K	chat	CLEX-7B-4K	UltraChat	4K	16K
CLEX-7B-16K	base	LLaMA-2-7B	Redpajama-Book	16K	64K
CLEX-7B-Chat-16K (this checkpoint)	chat	CLEX-7B-16K	UltraChat	16K	64K

How to Use

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/CLEX-7B-Chat-16K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-7B-Chat-16K", torch_dtype=torch.bfloat16, trust_remote_code=True)
inputs = tokenizer("What is CLEX?", return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))

Citation

If you find our project useful, hope you can star our repo and cite our paper as follows:

@article{damonlpsg2023clex,
  author = {Chen, Guanzheng and Li, Xin and Meng, Zaiqiao and Liang, Shangsong and Bing, Lidong},
  title = {CLEX: Continuous Length Extrapolation for Large Language Models},
  year = 2023,
  journal = {arXiv preprint arXiv:2310.16450},
  url = {https://arxiv.org/abs/2310.16450}
}