Instructions to use hutlim/Qwen3-Reranker-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hutlim/Qwen3-Reranker-0.6B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hutlim/Qwen3-Reranker-0.6B") model = AutoModelForCausalLM.from_pretrained("hutlim/Qwen3-Reranker-0.6B") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +2 -4
handler.py
CHANGED
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@@ -39,20 +39,18 @@ class EndpointHandler:
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)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.
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self.tokenizer = AutoTokenizer.from_pretrained(
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str(model_dir),
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padding_side="left",
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trust_remote_code=True,
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local_files_only=True,
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)
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self.model = AutoModelForCausalLM.from_pretrained(
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str(model_dir),
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-
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trust_remote_code=True,
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local_files_only=True,
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).to(self.device).eval()
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# Safer token lookup for decoder LMs: include leading space variants if needed
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)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.dtype = torch.float16 if self.device == "cuda" else torch.float32
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self.tokenizer = AutoTokenizer.from_pretrained(
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str(model_dir),
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padding_side="left",
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trust_remote_code=True,
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)
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self.model = AutoModelForCausalLM.from_pretrained(
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str(model_dir),
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dtype=self.dtype,
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trust_remote_code=True,
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).to(self.device).eval()
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# Safer token lookup for decoder LMs: include leading space variants if needed
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