File size: 3,025 Bytes
6157583 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | import torch
from typing import Dict, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
class EndpointHandler:
def __init__(self, model_dir: str, **kwargs: Any) -> None:
"""Load base model + LoRA adapter for Atlas-Chat-9B"""
print(f"Loading model from {model_dir}")
# Load tokenizer from base model
base_model_name = "MBZUAI-Paris/Atlas-Chat-9B"
self.tokenizer = AutoTokenizer.from_pretrained(
base_model_name,
trust_remote_code=True
)
# Set padding token
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
# Load base model in half precision
self.model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
low_cpu_mem_usage=True
)
# Load LoRA adapter
self.model = PeftModel.from_pretrained(self.model, model_dir)
self.model.eval()
print("Model loaded successfully!")
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Generate response"""
# Get input
inputs = data.get("inputs", "")
parameters = data.get("parameters", {})
max_new_tokens = parameters.get("max_new_tokens", 300)
temperature = parameters.get("temperature", 0.7)
# Format message with chat template
messages = [
{
"role": "system",
"content": "أنت مساعد تجارة إلكترونية جزائري يتحدث الدارجة الجزائرية."
},
{"role": "user", "content": inputs}
]
# Use the model's chat template
prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Tokenize
tokenized = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
prompt_length = tokenized["input_ids"].shape[1]
# Generate
with torch.no_grad():
outputs = self.model.generate(
**tokenized,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=temperature > 0,
top_p=0.95,
repetition_penalty=1.1,
pad_token_id=self.tokenizer.eos_token_id,
eos_token_id=self.tokenizer.eos_token_id
)
# Decode only the new tokens
generated_tokens = outputs[0][prompt_length:]
response = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
# Clean up response
response = response.strip()
return {"generated_text": response}
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