--- base_model: microsoft/phi-2 library_name: peft model_name: fol-parser-phi2-lora tags: - base_model:adapter:microsoft/phi-2 - lora - sft - transformers - trl licence: license pipeline_tag: text-generation --- # code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel BASE_MODEL = "microsoft/phi-2" ADAPTER_MODEL = "MinaGabriel/fol-parser-phi2-lora-adapter" # tokenizer tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, torch_dtype=torch.float16, device_map="auto", ) base_model.config.pad_token_id = tokenizer.pad_token_id base_model.generation_config.pad_token_id = tokenizer.pad_token_id # attach the adapter model = PeftModel.from_pretrained( base_model, ADAPTER_MODEL, device_map="auto", ) model.eval() def generate(context: str, question: str, max_new_tokens: int = 300) -> str: prompt = ( "\nYou are a precise logic parser. Output [FOL] then [CONCLUSION_FOL].\n\n" "\n" f"[CONTEXT]\n{context}\n\n" f"[QUESTION]\n{question}\n\n" "Produce the two blocks exactly as specified.\n" "\n" "\n" ) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): output_ids = model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=False, temperature=0.0, eos_token_id=tokenizer.eos_token_id, # explicit pad_token_id=tokenizer.pad_token_id # explicit ) full_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) return full_text.split("\n")[-1].strip() ``` # Usage: ```python print( generate( context="Cats are animal. dogs are animal. human are not animal. animal are awesome", question="dogs awesome?" ) ) ``` # output: ```C++ [FOL] cat(animal) dog(animal) ¬human(animal) ∀x (animal(x) → awesome(x)) [CONCLUSION_FOL] awesome(dog) ```