Delete handler.py

handler.py

@@ -1,48 +0,0 @@
-from transformers import (
-    AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
-)
-import torch, os
-
-MODEL_ID = "Qwen/Qwen3-32B"   # 换成自己的模型
-
-def get_model():
-    # ① 先试 bfloat16，A100/H100 都原生支持
-    return AutoModelForCausalLM.from_pretrained(
-        MODEL_ID,
-        torch_dtype=torch.bfloat16,
-        device_map="auto",          # TGI 同款逻辑，自动分片 
-        low_cpu_mem_usage=True,     # 先在 CPU 建图，再流式拷到 GPU
-        trust_remote_code=True
-    )
-
-# ---- 如果 bfloat16 仍 OOM，可改成 4-bit 量化 ----
-# bnb_cfg = BitsAndBytesConfig(
-#     load_in_4bit=True,
-#     bnb_4bit_quant_type="nf4",
-#     bnb_4bit_use_double_quant=True,
-# )
-# def get_model():
-#     return AutoModelForCausalLM.from_pretrained(
-#         MODEL_ID,
-#         device_map="auto",
-#         quantization_config=bnb_cfg,
-#         trust_remote_code=True
-#     )
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
-model = get_model()
-generator = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    device_map="auto",
-    torch_dtype=getattr(model, "dtype", torch.bfloat16),
-)
-
-def __init__(self, *args, **kwargs):
-    pass
-
-def __call__(self, data):
-    prompt = data.get("inputs") if isinstance(data, dict) else data
-    outputs = generator(prompt, max_new_tokens=256)
-    return outputs