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# code/inference.py
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch, json
def model_fn(model_dir, *_):
# Load with remote code support for Qwen3
tokenizer = AutoTokenizer.from_pretrained(
model_dir, trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
model_dir,
trust_remote_code=True,
torch_dtype=torch.float16,
device_map="auto"
)
return {"model": model, "tokenizer": tokenizer}
def input_fn(serialized_input, content_type, *_):
# Accept JSON {"inputs": "..."} or raw text
if content_type == "application/json":
return json.loads(serialized_input).get("inputs", "")
return serialized_input
def predict_fn(prompt, model_bundle, *_):
tok = model_bundle["tokenizer"]
mdl = model_bundle["model"]
inputs = tok(prompt, return_tensors="pt").to(mdl.device)
outputs = mdl.generate(**inputs, max_new_tokens=128)
return tok.decode(outputs[0], skip_special_tokens=True)
def output_fn(prediction, accept, *_):
# Return JSON if requested
if accept == "application/json":
return json.dumps({"generated_text": prediction})
return prediction
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