Instructions to use zai-org/LongAlign-7B-64k-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/LongAlign-7B-64k-base with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/LongAlign-7B-64k-base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/LongAlign-7B-64k-base")
model = AutoModelForCausalLM.from_pretrained("zai-org/LongAlign-7B-64k-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/LongAlign-7B-64k-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/LongAlign-7B-64k-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/LongAlign-7B-64k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zai-org/LongAlign-7B-64k-base

SGLang

How to use zai-org/LongAlign-7B-64k-base 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 "zai-org/LongAlign-7B-64k-base" \
    --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": "zai-org/LongAlign-7B-64k-base",
		"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 "zai-org/LongAlign-7B-64k-base" \
        --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": "zai-org/LongAlign-7B-64k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zai-org/LongAlign-7B-64k-base with Docker Model Runner:
```
docker model run hf.co/zai-org/LongAlign-7B-64k-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LongAlign-7B-64k-base

🤗 [LongAlign Dataset] • 💻 [Github Repo] • 📃 [LongAlign Paper]

LongAlign is the first full recipe for LLM alignment on long context. We propose the LongAlign-10k dataset, containing 10,000 long instruction data of 8k-64k in length. We investigate on trianing strategies, namely packing (with loss weighting) and sorted batching, which are all implemented in our code. For real-world long context evaluation, we introduce LongBench-Chat that evaluate the instruction-following capability on queries of 10k-100k length.

All Models

We open-sourced the following list of models:

Model	Huggingface Repo	Description
LongAlign-6B-64k-base	🤗 Huggingface Repo	ChatGLM3-6B with an extended 64k context window
LongAlign-6B-64k	🤗 Huggingface Repo	Chat model by LongAlign training on LongAlign-6B-64k-base
LongAlign-7B-64k-base	🤗 Huggingface Repo	Llama-2-7B with an extended 64k context window
LongAlign-7B-64k	🤗 Huggingface Repo	Chat model by LongAlign training on LongAlign-7B-64k-base
LongAlign-13B-64k-base	🤗 Huggingface Repo	Llama-2-13B with an extended 64k context window
LongAlign-13B-64k	🤗 Huggingface Repo	Chat model by LongAlign training on LongAlign-13B-64k-base
ChatGLM3-6B-128k	🤗 Huggingface Repo	ChatGLM3-6B with a 128k context window

Model usage

Chat prompt template for LongAlign-6B-64k:

[Round 1]

问：Hi!

答：Hello! What can I assist you today?

[Round 2]

问：What should I do if I can't sleep at night?

答：

Chat prompt template for LongAlign-7B-64k and LongAlign-13B-64k:

[INST]Hi![/INST]Hello! What can I assist you today?

[INST]What should I do if I can't sleep at night?[/INST]

ChatGLM3-6B-128k uses the same prompt template as ChatGLM3-6B.

A simple demo for deployment of the model:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("THUDM/LongAlign-6B-64k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("THUDM/LongAlign-6B-64k", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
model = model.eval()
query = open("assets/paper.txt").read() + "\n\nPlease summarize the paper."
response, history = model.chat(tokenizer, query, history=[], max_new_tokens=512, temperature=1)
print(response)

Citation

If you find our work useful, please consider citing LongAlign:

Downloads last month: 224

Model tree for zai-org/LongAlign-7B-64k-base

Quantizations

2 models

Paper for zai-org/LongAlign-7B-64k-base

LongAlign: A Recipe for Long Context Alignment of Large Language Models

Paper • 2401.18058 • Published Jan 31, 2024 • 25