| | --- |
| | language: |
| | - en |
| | - zh |
| | library_name: transformers |
| | tags: |
| | - Long Context |
| | - chatglm |
| | pipeline_tag: text-generation |
| | license: apache-2.0 |
| | --- |
| | # LongAlign-6B-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| |
| |
|
| |  |
| |
|
| | ## 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: |
| |
|
| | ``` |
| | |
| | ``` |
