| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| import torch |
| import json |
| import os |
|
|
| os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" |
|
|
| class EndpointHandler: |
| def __init__(self, path=""): |
| """ |
| Initialize the handler with the model from the given path |
| """ |
| model_name = "meta-llama/Llama-3.3-70B-Instruct" |
|
|
| self.tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
| base_model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype=torch.float16, |
| device_map="auto", |
| trust_remote_code=True, |
| load_in_8bit=True, |
| low_cpu_mem_usage=True |
| ) |
|
|
| try: |
| self.model = PeftModel.from_pretrained( |
| base_model, |
| path, |
| is_trainable=False |
| ) |
| print("Successfully loaded adapter with base model") |
| except Exception as e: |
| print(f"Error loading adapter: {e}") |
| print("Falling back to base model without adapter") |
| self.model = base_model |
| |
| try: |
| with open(f"{path}/chat_template.jinja", "r") as f: |
| self.chat_template = f.read() |
| except: |
| self.chat_template = None |
|
|
| def __call__(self, data): |
| """ |
| Process the input data and return the model's response |
| """ |
| inputs = data.get("inputs", "") |
| parameters = data.get("parameters", {}) |
| |
| default_prompt = "Break this text into WhatsApp messages like a real person would send them. Split where you'd naturally pause: after greetings, before/after questions, between different thoughts, when changing topics. Preserve exact wording - just divide where someone would actually hit 'send' and start a new message. Output JSON array." |
|
|
| custom_prompt = parameters.get("prompt", default_prompt) |
|
|
| messages = [ |
| {"role": "system", "content": custom_prompt}, |
| {"role": "user", "content": inputs} |
| ] |
| |
| if self.chat_template: |
| text = self.tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| else: |
| text = f"{custom_prompt}\nUser: {inputs}\nAssistant:" |
| |
| |
| model_inputs = self.tokenizer(text, return_tensors="pt").to(self.model.device) |
| |
| |
| with torch.no_grad(): |
| outputs = self.model.generate( |
| **model_inputs, |
| max_new_tokens=parameters.get("max_new_tokens", 100), |
| temperature=parameters.get("temperature", 0.3), |
| top_p=parameters.get("top_p", 0.9), |
| do_sample=True, |
| pad_token_id=self.tokenizer.eos_token_id, |
| repetition_penalty=1.1 |
| ) |
| |
| |
| response = self.tokenizer.decode( |
| outputs[0][model_inputs.input_ids.shape[-1]:], |
| skip_special_tokens=True |
| ).strip() |
|
|
| try: |
| |
| if response.startswith('[') and response.endswith(']'): |
| parsed = json.loads(response) |
| if isinstance(parsed, list): |
| formatted_response = response |
| else: |
| formatted_response = json.dumps([response]) |
| else: |
|
|
| formatted_response = json.dumps([response]) |
| except: |
| formatted_response = json.dumps([inputs]) |
|
|
| return [{"content": formatted_response}] |
