Create README.md

README.md

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+使用Prompt Tuning方法微调
+
+**Usage**
+
+    from peft import PeftModel, PeftConfig
+  
+    peft_model_id = "Laurie/bloomz-560m_PROMPT_TUNING_CAUSAL_LM"
+    
+    config = PeftConfig.from_pretrained(peft_model_id)
+    model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+    model = PeftModel.from_pretrained(model, peft_model_id)
+  
+    # Grab a tweet and tokenize it:
+    inputs = tokenizer(
+    f'{text_column} : {"@nationalgridus I have no water and the bill is current and paid. Can you do something about this?"} Label : ',
+    return_tensors="pt")
+
+    # Put the model on a GPU and generate the predicted label:
+    model.to(device)
+
+    with torch.no_grad():
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        outputs = model.generate(
+            input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], max_new_tokens=10, eos_token_id=3
+        )
+        print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))
+    [
+        "Tweet text : @nationalgridus I have no water and the bill is current and paid. Can you do something about this? Label : complaint"
+    ]