Update README.md

README.md

@@ -1,7 +1,7 @@
 https://github.com/vllm-project/llm-compressor/pull/185
 
 ```python
-from transformers import AutoTokenizer
+from transformers import AutoProcessor
 
 from llmcompressor.modifiers.quantization import QuantizationModifier
 from llmcompressor.transformers import oneshot
@@ -10,20 +10,20 @@ from llmcompressor.transformers.sparsification import create_sparse_auto_model_c
 MODEL_ID = "llava-hf/llava-1.5-7b-hf"
 model_class = create_sparse_auto_model_class("LlavaForConditionalGeneration")
 model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto")
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+processor = AutoProcessor.from_pretrained(MODEL_ID)
 
 recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
 oneshot(model=model, recipe=recipe)
 
 # Confirm generations of the quantized model look sane.
 print("========== SAMPLE GENERATION ==============")
-input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+input_ids = processor("Hello my name is", return_tensors="pt").input_ids.to("cuda")
 output = model.generate(input_ids, max_new_tokens=20)
-print(tokenizer.decode(output[0]))
+print(processor.decode(output[0]))
 print("==========================================")
 
 # Save to disk in compressed-tensors format.
 SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
 model.save_pretrained(SAVE_DIR)
-tokenizer.save_pretrained(SAVE_DIR)
+processor.save_pretrained(SAVE_DIR)
 ```