Removed unexpected file that shouldn't have been added, sorry

examples/basic_inference.py

@@ -1,62 +0,0 @@
-from transformers import AutoTokenizer, pipeline, logging
-from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
-import argparse
-
-parser = argparse.ArgumentParser(description='Simple AutoGPTQ example')
-parser.add_argument('model_name_or_path', type=str, help='Model folder or repo')
-parser.add_argument('--model_basename', type=str, help='Model file basename if model is not named gptq_model-Xb-Ygr')
-parser.add_argument('--use_slow', action="store_true", help='Use slow tokenizer')
-parser.add_argument('--use_safetensors', action="store_true", help='Model file basename if model is not named gptq_model-Xb-Ygr')
-parser.add_argument('--use_triton', action="store_true", help='Use Triton for inference?')
-parser.add_argument('--bits', type=int, default=4, help='Specify GPTQ bits. Only needed if no quantize_config.json is provided')
-parser.add_argument('--group_size', type=int, default=128, help='Specify GPTQ group_size. Only needed if no quantize_config.json is provided')
-parser.add_argument('--desc_act', action="store_true", help='Specify GPTQ desc_act. Only needed if no quantize_config.json is provided')
-
-args = parser.parse_args()
-
-quantized_model_dir = args.model_name_or_path
-
-tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, use_fast=not args.use_slow)
-
-try:
-   quantize_config = BaseQuantizeConfig.from_pretrained(quantized_model_dir)
-except:
-    quantize_config = BaseQuantizeConfig(
-            bits=args.bits,
-            group_size=args.group_size,
-            desc_act=args.desc_act
-        )
-
-model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir,
-        use_safetensors=True,
-        model_basename=args.model_basename,
-        device="cuda:0",
-        use_triton=args.use_triton,
-        quantize_config=quantize_config)
-
-# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
-logging.set_verbosity(logging.CRITICAL)
-
-prompt = "Tell me about AI"
-prompt_template=f'''### Human: {prompt}
-### Assistant:'''
-
-print("*** Pipeline:")
-pipe = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_new_tokens=512,
-    temperature=0.7,
-    top_p=0.95,
-    repetition_penalty=1.15
-)
-
-print(pipe(prompt_template)[0]['generated_text'])
-
-print("\n\n*** Generate:")
-
-input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
-output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
-print(tokenizer.decode(output[0]))
-