LongAlign-13B-64k
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: