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Update inference.py
Browse files- inference.py +6 -2
inference.py
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@@ -1,15 +1,19 @@
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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BASE_MODEL = "deepseek-ai/deepseek-coder-1.3b-base"
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LORA_REPO = "VaibhavHD/deepseek-lora-monthly"
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tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
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base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, trust_remote_code=True)
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model = PeftModel.from_pretrained(base, LORA_REPO)
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def generate_response(prompt:str)->str:
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inputs = tokenizer(prompt, return_tensors="pt")
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out = model.generate(**inputs, max_new_tokens=200)
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return tokenizer.decode(out[0], skip_special_tokens=True)
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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import torch
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import transformers.training_args
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# ✅ Fix: allow this class for safe loading in PyTorch 2.6+
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torch.serialization.add_safe_globals([transformers.training_args.TrainingArguments])
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BASE_MODEL = "deepseek-ai/deepseek-coder-1.3b-base"
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LORA_REPO = "VaibhavHD/deepseek-lora-monthly"
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tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
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base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, trust_remote_code=True)
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model = PeftModel.from_pretrained(base, LORA_REPO)
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def generate_response(prompt: str) -> str:
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inputs = tokenizer(prompt, return_tensors="pt")
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out = model.generate(**inputs, max_new_tokens=200)
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return tokenizer.decode(out[0], skip_special_tokens=True)
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