app.py
CHANGED
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@@ -13,7 +13,7 @@ print("PEFT Base Model:", peft_config.base_model_name_or_path)
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# 2. Load the tokenizer & base model
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tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
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base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, revision="4831ee1375be5b4ff5a4abf7984e13628db44e35", trust_remote_code=True)
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# 3. Load your LoRA adapter weights onto the base model
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model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
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# 2. Load the tokenizer & base model
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tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
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+
base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, revision="4831ee1375be5b4ff5a4abf7984e13628db44e35", ignore_mismatched_sizes=True, trust_remote_code=True)
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# 3. Load your LoRA adapter weights onto the base model
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model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
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