| from typing import Dict, List, Any |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct") |
| model = PeftModel.from_pretrained(model, "Juanxxo/phi3-mini-med-adapter") |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") |
| self.model = model |
| self.tokenizer = tokenizer |
| self.model.eval() |
|
|
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
| """ |
| Process input data and generate predictions using the model. |
| |
| Args: |
| data (Dict[str, Any]): input data containing either and "inputs" key |
| or the input directly in the data dictionary |
| |
| Returns: |
| List[Dict[str, Any]]: Processed model predictions taht will be serialized and returned |
| """ |
| |
| inputs = data.pop("inputs", data) |
| inputs = self.tokenizer(inputs, return_tensors="pt") |
|
|
| print(inputs) |
|
|
| with torch.no_grad(): |
| outputs = self.model.generate(input_ids=input["input_ids"], max_new_token = 10) |
| output = self.tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0] |
| predictions = [{"generated_text": output}] |
|
|
| return predictions |
