Update README.md

README.md

@@ -8,22 +8,30 @@ from llmcompressor.transformers import oneshot
 from llmcompressor.transformers.sparsification import create_sparse_auto_model_class
 
 MODEL_ID = "llava-hf/llava-1.5-7b-hf"
+
+# Load model.
 model_class = create_sparse_auto_model_class("LlavaForConditionalGeneration")
 model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto")
 processor = AutoProcessor.from_pretrained(MODEL_ID)
 
-recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
-oneshot(model=model, recipe=recipe)
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per channel via ptq
+#   * quantize the activations to fp8 with dynamic per token
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="FP8_DYNAMIC",
+    ignore=["re:.*lm_head", "re:multi_modal_projector.*", "re:vision_tower.*"],
+)
+
+# Apply quantization and save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
+oneshot(model=model, recipe=recipe, output_dir=SAVE_DIR)
 
 # Confirm generations of the quantized model look sane.
 print("========== SAMPLE GENERATION ==============")
-input_ids = processor("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+input_ids = processor(text="Hello my name is", return_tensors="pt").input_ids.to("cuda")
 output = model.generate(input_ids, max_new_tokens=20)
 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)
-processor.save_pretrained(SAVE_DIR)
 ```