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")

# 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
```

LongAlign-7B-64k-base / README.md

bys0318

Update README.md

fa28d01 verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

3.31 kB

	---
	language:
	- en
	- zh
	library_name: transformers
	tags:
	- Long Context
	- llama
	license: apache-2.0
	---
	# LongAlign-7B-64k-base

	<p align="center">
	🤗 <a href="https://huggingface.co/datasets/THUDM/LongAlign-10k" target="_blank">[LongAlign Dataset] </a> • 💻 <a href="https://github.com/THUDM/LongAlign" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2401.18058" target="_blank">[LongAlign Paper]</a>
	</p>

	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](https://huggingface.co/THUDM/LongAlign-6B-64k-base) \| ChatGLM3-6B with an extended 64k context window \|
	\|LongAlign-6B-64k\| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-6B-64k) \| Chat model by LongAlign training on LongAlign-6B-64k-base\|
	\|LongAlign-7B-64k-base\| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-7B-64k-base) \| Llama-2-7B with an extended 64k context window \|
	\|LongAlign-7B-64k\| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-7B-64k) \| Chat model by LongAlign training on LongAlign-7B-64k-base\|
	\|LongAlign-13B-64k-base\| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-13B-64k-base) \| Llama-2-13B with an extended 64k context window \|
	\|LongAlign-13B-64k\| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-13B-64k) \| Chat model by LongAlign training on LongAlign-13B-64k-base\|
	\|ChatGLM3-6B-128k\| [🤗 Huggingface Repo](https://huggingface.co/THUDM/chatglm3-6b-128k) \| ChatGLM3-6B with a 128k context window\|

	![](assets/leaderboard.png)

	## Model usage
	Chat prompt template for LongAlign-6B-64k:
	```text
	[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:
	```text
	[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](https://huggingface.co/THUDM/chatglm3-6b).

	A simple demo for deployment of the model:
	```python
	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:

	```

	```