Upload autorev_inf.py

autorev_inf.py

@@ -0,0 +1,53 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+adapter_id = "Maitreya152/AutoRev"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+num_added = tokenizer.add_special_tokens({"pad_token": "<PAD>"})
+tokenizer.padding_side = "right"
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16, 
+    device_map="auto"           
+)
+
+if num_added > 0:
+    base_model.resize_token_embeddings(len(tokenizer))
+base_model.config.pad_token_id = tokenizer.pad_token_id
+
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+passages = """
+"""
+
+prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+Generate a structured feedback for the research paper passages provided below. The feedback should include a summary of the paper, its strengths, weaknesses, and questions for the authors. Consider that the feedback is being given for a paper submitted to the ICLR conference.
+### Research Paper Passages:
+{passages.strip()}
+### Feedback for the paper:
+"""
+
+inputs = tokenizer(
+    prompt, 
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=6000,         
+    do_sample=True,
+    temperature=0.7,            
+    top_p=0.9,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+input_length = inputs.input_ids.shape[1]
+generated_tokens = outputs[0][input_length:]
+response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+print(response)