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--- |
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language: |
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- en |
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- zh |
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library_name: transformers |
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tags: |
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- Long Context |
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- chatglm |
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- llama |
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datasets: |
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- THUDM/LongAlign-10k |
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- THUDM/LongBench |
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--- |
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# LongAlign-13B-64k |
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<p align="center"> |
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🤗 <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/" target="_blank">[LongAlign Paper]</a> |
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</p> |
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**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 **Chat-LongBench** that evaluate the instruction-following capability on queries of 10k-100k length. |
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## All Models |
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We open-sourced the following list of models: |
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|Model|Huggingface Repo|Description| |
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|---|---|---| |
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|**LongAlign-6B-64k-base**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-6B-64k-base) | **ChatGLM3-6B** with an extended 64k context window | |
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|**LongAlign-6B-64k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-6B-64k) | Chat model by LongAlign training on LongAlign-6B-64k-base| |
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|**LongAlign-7B-64k-base**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-7B-64k-base) | **Llama-2-7B** with an extended 64k context window | |
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|**LongAlign-7B-64k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-7B-64k) | Chat model by LongAlign training on LongAlign-7B-64k-base| |
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|**LongAlign-13B-64k-base**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-13B-64k-base) | **Llama-2-13B** with an extended 64k context window | |
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|**LongAlign-13B-64k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-13B-64k) | Chat model by LongAlign training on LongAlign-13B-64k-base| |
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|**ChatGLM3-6B-128k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/chatglm3-6b-128k) | **ChatGLM3-6B** with a 128k context window| |
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## Model usage |
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Chat prompt template for LongAlign-6B-64k: |
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```text |
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[Round 1] |
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问：Hi! |
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答：Hello! What can I assist you today? |
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[Round 2] |
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问：What should I do if I can't sleep at night? |
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答： |
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``` |
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Chat prompt template for LongAlign-7B-64k and LongAlign-13B-64k: |
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```text |
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[INST]Hi![/INST]Hello! What can I assist you today? |
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[INST]What should I do if I can't sleep at night?[/INST] |
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``` |
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ChatGLM3-6B-128k uses the same prompt template as [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b). |
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A simple demo for deployment of the model: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("THUDM/LongAlign-6B-64k", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("THUDM/LongAlign-6B-64k", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") |
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model = model.eval() |
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query = open("assets/paper.txt").read() + "\n\nPlease summarize the paper." |
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response, history = model.chat(tokenizer, query, history=[], max_new_tokens=512, temperature=1) |
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print(response) |
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``` |
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## Citation |
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If you find our work useful, please consider citing LongAlign: |
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``` |
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``` |
