| ## Creation | |
| ``` | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| from llmcompressor.modifiers.quantization import QuantizationModifier | |
| from llmcompressor.transformers import oneshot, wrap_hf_model_class | |
| MODEL_ID = "nvidia/NVLM-D-72B" | |
| # Load model. | |
| model_class = wrap_hf_model_class(AutoModelForCausalLM) | |
| model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto", trust_remote_code=True) | |
| processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True) | |
| # 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:mlp1.*", "re:vision_model.*"], | |
| ) | |
| # 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) | |
| processor.save_pretrained(SAVE_DIR) | |
| # Confirm generations of the quantized model look sane. | |
| print("========== SAMPLE GENERATION ==============") | |
| 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("==========================================") | |
| ``` |