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--- |
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license: llama2 |
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--- |
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<!-- markdownlint-disable first-line-h1 --> |
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<!-- markdownlint-disable html --> |
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<div align="center"> |
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<h1> |
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SlimPLM |
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</h1> |
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</div> |
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<p align="center"> |
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📝 <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> |
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</p> |
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<div align="center"> |
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</div> |
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🌹 If you use this model, please star our **[GitHub repository](https://github.com/plageon/SlimPlm)** to support us. Your star means a lot! |
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## ✨ Latest News |
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- [1/25/2024]: Retrieval Necessity Judgment Model released in [Hugging Face](https://huggingface.co/zstanjj/SlimPLM-Retrieval-Necessity-Judgment/). |
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- [2/20/2024]: Query Rewriting Model released in [Hugging Face](https://huggingface.co/zstanjj/SlimPLM-Query-Rewriting/). |
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- [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. |
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## 🎬 Get Started |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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# construct prompt |
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question = "Who voices Darth Vader in Star Wars Episodes III-VI, IX Rogue One, and Rebels?" |
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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." |
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prompt = (f"<s>[INST] <<SYS>>\nYou are a helpful assistant. Your task is to parse user input into" |
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f" structured formats according to the coarse answer. Current datatime is 2023-12-20 9:47:28" |
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f" <</SYS>>\n Course answer: (({heuristic_answer}))\nQuestion: (({question})) [/INST]") |
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params_query_rewrite = {"repetition_penalty": 1.05, "temperature": 0.01, "top_k": 1, "top_p": 0.85, |
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"max_new_tokens": 512, "do_sample": False, "seed": 2023} |
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# deploy model |
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model = AutoModelForCausalLM.from_pretrained("zstanjj/SlimPLM-Retrieval-Necessity-Judgment").eval() |
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if torch.cuda.is_available(): |
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model.cuda() |
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tokenizer = AutoTokenizer.from_pretrained("zstanjj/SlimPLM-Retrieval-Necessity-Judgment") |
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# run inference |
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input_ids = tokenizer.encode(prompt.format(question=question, answer=heuristic_answer), return_tensors="pt") |
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len_input_ids = len(input_ids[0]) |
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if torch.cuda.is_available(): |
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input_ids = input_ids.cuda() |
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outputs = model.generate(input_ids) |
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res = tokenizer.decode(outputs[0][len_input_ids:], skip_special_tokens=True) |
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print(res) |
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``` |
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## ✏️ Citation |
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``` |
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@inproceedings{Tan2024SmallMB, |
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title={Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs}, |
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author={Jiejun Tan and Zhicheng Dou and Yutao Zhu and Peidong Guo and Kun Fang and Ji-Rong Wen}, |
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year={2024}, |
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url={https://arxiv.org/abs/2402.12052} |
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} |
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``` |
