|
--- |
|
license: llama2 |
|
--- |
|
<!-- markdownlint-disable first-line-h1 --> |
|
<!-- markdownlint-disable html --> |
|
|
|
<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-Query-Rewriting/" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/plageon/SlimPLM" target="_blank">Github</a> |
|
</p> |
|
|
|
<div align="center"> |
|
</div> |
|
|
|
🌹 If you use this model, please star our **[GitHub repository](https://github.com/plageon/SlimPlm)** to support us. Your star means a lot! |
|
|
|
## ✨ 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, **[Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs](https://aclanthology.org/2024.acl-long.242/)**, 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-Query-Rewriting").eval() |
|
if torch.cuda.is_available(): |
|
model.cuda() |
|
tokenizer = AutoTokenizer.from_pretrained("zstanjj/SlimPLM-Query-Rewriting") |
|
|
|
# run inference |
|
input_ids = tokenizer.encode(prompt.format(question=question, answer=heuristic_answer), 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} |
|
} |
|
``` |
