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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
class ModelHandler:
def __init__(self):
self.initialized = False
def initialize(self, model_dir: str):
self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
self.model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
self.model.eval()
if torch.cuda.is_available():
self.model.to("cuda")
self.initialized = True
def predict(self, inputs: dict):
if not self.initialized:
raise RuntimeError("Model not initialized")
messages = inputs.get("messages", [])
max_tokens = inputs.get("max_tokens", 512)
temperature = inputs.get("temperature", 0.7)
# Convert OpenAI-style messages into a single prompt
prompt = self._build_prompt(messages)
# Tokenize
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
if torch.cuda.is_available():
input_ids = input_ids.to("cuda")
# Generate
output_ids = self.model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id,
)
response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
# Return just the newly generated portion
generated_text = response[len(prompt):].strip()
return {
"id": "chatcmpl-fakeid",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": generated_text
},
"finish_reason": "stop"
}
],
"model": "your-model-id",
}
def _build_prompt(self, messages):
prompt = ""
for msg in messages:
role = msg["role"]
content = msg["content"]
if role == "user":
prompt += f"User: {content}\n"
elif role == "assistant":
prompt += f"Assistant: {content}\n"
prompt += "Assistant:"
return prompt
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