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---
license: llama2
---
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<div align="center">
<h1>
SlimPLM
</h1>
</div>
<p align="center">
📝 <a href="https://arxiv.org/abs/2402.12052" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/zstanjj/SlimPLM-Retrieval-Necessity-Judgment/" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/plageon/SlimPLM" target="_blank">Github</a>
</p>
<div align="center">
</div>
## ✨ Latest News
- [1/25/2024]: Retrieval Necessity Judgment Model released in [Hugging Face](https://huggingface.co/zstanjj/SlimPLM-Retrieval-Necessity-Judgment/).
- [2/20/2024]: Query Rewriting Model released in [Hugging Face](https://huggingface.co/zstanjj/SlimPLM-Query-Rewriting/).
- [5/19/2024]: Our new work, **[SlimPLM](https://github.com/plageon/SlimPlm)**, has been accepted by **ACL 2024 main** conference.
## 🎬 Get Started
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# construct prompt
question = "Who voices Darth Vader in Star Wars Episodes III-VI, IX Rogue One, and Rebels?"
heuristic_answer = "The voice of Darth Vader in Star Wars is provided by British actor James Earl Jones. He first voiced the character in the 1977 film \"Star Wars: Episode IV - A New Hope\", and his performance has been used in all subsequent Star Wars films, including the prequels and sequels."
prompt = (f"<s>[INST] <<SYS>>\nYou are a helpful assistant. Your task is to parse user input into"
f" structured formats according to the coarse answer. Current datatime is 2023-12-20 9:47:28"
f" <</SYS>>\n Course answer: (({heuristic_answer}))\nQuestion: (({question})) [/INST]")
params_query_rewrite = {"repetition_penalty": 1.05, "temperature": 0.01, "top_k": 1, "top_p": 0.85,
"max_new_tokens": 512, "do_sample": False, "seed": 2023}
# deploy model
model = AutoModelForCausalLM.from_pretrained("zstanjj/SlimPLM-Retrieval-Necessity-Judgment").eval()
if torch.cuda.is_available():
model.cuda()
tokenizer = AutoTokenizer.from_pretrained("zstanjj/SlimPLM-Retrieval-Necessity-Judgment")
# run inference
input_ids = tokenizer.encode(question, return_tensors="pt")
len_input_ids = len(input_ids[0])
if torch.cuda.is_available():
input_ids = input_ids.cuda()
outputs = model.generate(input_ids)
res = tokenizer.decode(outputs[0][len_input_ids:], skip_special_tokens=True)
print(res)
```
## ✏️ Citation
```
@inproceedings{Tan2024SmallMB,
title={Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs},
author={Jiejun Tan and Zhicheng Dou and Yutao Zhu and Peidong Guo and Kun Fang and Ji-Rong Wen},
year={2024},
url={https://arxiv.org/abs/2402.12052}
}
``` |