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@@ -11,7 +11,7 @@ license: llama2
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  </div>
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  <p align="center">
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- πŸ“ <a href="https://arxiv.org/abs/2402.12052" target="_blank">Paper</a> β€’ πŸ€— <a href="https://huggingface.co/zstanjj/SlimPLM-Query-Rewriting/" target="_blank">Hugging Face</a> β€’ 🧩 <a href="https://github.com/plageon/SlimPLM" target="_blank">Github</a>
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  </p>
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  <div align="center">
@@ -38,10 +38,10 @@ params_query_rewrite = {"repetition_penalty": 1.05, "temperature": 0.01, "top_k"
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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-Search-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-Search-Necessity-Judgment")
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  # run inference
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  input_ids = tokenizer.encode(question, return_tensors="pt")
 
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  </div>
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  <p align="center">
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+ πŸ“ <a href="https://arxiv.org/abs/2402.12052" target="_blank">Paper</a> β€’ πŸ€— <a href="https://huggingface.co/zstanjj/SlimPLM-Retrieval-Necessity-Judgment/" target="_blank">Hugging Face</a> β€’ 🧩 <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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  "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(question, return_tensors="pt")