--- license: llama2 ---

SlimPLM

📝 Paper • 🤗 Hugging Face • 🧩 Github

## ✨ 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"[INST] <>\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" <>\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} } ```