Instructions to use DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K with Transformers:

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

pipe = pipeline("text-generation", model="DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K 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-Mixtral-8x7B-Chat-32K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K

SGLang

How to use DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K 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-Mixtral-8x7B-Chat-32K" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Mixtral-8x7B-Chat-32K" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K with Docker Model Runner:
```
docker model run hf.co/DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K
```

CLEX: Continuous Length Extrapolation for Large Language Models

This repo stores the checkpoint of CLEX-Mixtral-8x7B-Chat-32K.

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.

If you have any questions, feel free to contact us. (Emails: guanzzh.chen@gmail.com, lixin4ever@gmail.com)

Model Zoo

Model Name	Model Type	Starting Point	Train Data	Train Length	MAX Test Length	HF Repo
CLEX-LLaMA-2-7B-16K	base	LLaMA-2-7B	Redpajama-Book	16K	64K	link
CLEX-LLaMA-2-7B-Chat-16K	chat	CLEX-7B-16K	UltraChat	16K	64K	link
CLEX-LLaMA-2-7B-64K	base	LLaMA-2-7B	Redpajama-Book	64k	256K	link
CLEX-Phi-2-32K	base	Phi-2-2.7B	LongCorpus-2.5B	32k	128K	link
CLEX-Mixtral-8x7B-32K	base	Mixtral-8x7B-v0.1	LongCorpus-2.5B	32k	>128K	link
CLEX-Mixtral-8x7B-Chat-32k (this checkpoint)	chat	CLEX-Mixtral-8x7B-32K	Ultrachat 200k	32k	>128K	link

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", 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]))

Evaluation

InfiniteBench

We also evaluate CLEX-Mixtral-8x7B-Chat-32k on InfiniteBench, which is a 128k-length benchmark covering various tasks. We compare our CLEX-Mixtral-8x7B-Chat-32k with GPT-4, Claude, KimiChat, and vanilla Mixtral-8x7B.

Task Name	GPT-4	YaRN-Mistral-7B	Kimi-Chat	Claude 2	CLEX-Mixtral-8x7B-Chat-32k	Mixtral-8x7B-Instruct-v0.1
Retrieve.PassKey	100%	92.71%	98.14%	97.80%	99.72%	96.78%
Retrieve.Number	100%	56.61%	95.42%	98.14%	76.10%	76.61%
Retrieve.KV	89.00%	< 5%	53.60%	65.40%	<5%	<5%
En.Sum	14.73%	9.09%	17.93%	14.45%	15.48%	14.3%
En.QA	22.22%	9.55%	16.52%	11.97%	15.52%	16.81%
En.MC	67.25%	27.95%	72.49%	62.88%	58.96%	56.77%
En.Dia	8.50%	7.50%	11.50%	46.50%	9%	<5%
Code.Debug	39.59%	< 5%	18.02%	< 5%	21.32%	<5%
Code.Run	23.25%	< 5%	< 5%	< 5%	< 5%	<5%
Math.Calc	< 5%	< 5%	< 5%	< 5%	< 5%	<5%
Math.Find	60.00%	17.14%	12.57%	32.29%	28%	26.57%

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}
}

Downloads last month: 10

Safetensors

Model size

47B params

Tensor type

F32

BF16

Dataset used to train DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K

Paper for DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K

CLEX: Continuous Length Extrapolation for Large Language Models

Paper • 2310.16450 • Published Oct 25, 2023 • 10