78 lines
3.3 KiB
Markdown
78 lines
3.3 KiB
Markdown
---
|
||
language:
|
||
- en
|
||
- zh
|
||
library_name: transformers
|
||
tags:
|
||
- Long Context
|
||
- llama
|
||
datasets:
|
||
- THUDM/LongAlign-10k
|
||
license: apache-2.0
|
||
---
|
||
# LongAlign-7B-64k
|
||
|
||
<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|
|
||
|
||
|
||
|
||
## 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:
|
||
|
||
```
|
||
|
||
``` |