| ## Evaluation | |
| ``` | |
| lm_eval --model vllm-vlm --model_args pretrained=llava-hf/llava-1.5-7b-hf --tasks mmmu_val | |
| | Groups |Version|Filter|n-shot|Metric| |Value | |Stderr| | |
| |--------------------------------|------:|------|------|------|---|-----:|---|-----:| | |
| |mmmu_val | 0|none | |acc |↑ |0.2433|± |0.0141| | |
| | - Art and Design | 0|none | |acc |↑ |0.2250|± |0.0384| | |
| | - Business | 0|none | |acc |↑ |0.2600|± |0.0358| | |
| | - Health and Medicine | 0|none | |acc |↑ |0.3067|± |0.0377| | |
| | - Humanities and Social Science| 0|none | |acc |↑ |0.2667|± |0.0403| | |
| | - Science | 0|none | |acc |↑ |0.1667|± |0.0308| | |
| | - Tech and Engineering | 0|none | |acc |↑ |0.2381|± |0.0284| | |
| lm_eval --model vllm-vlm --model_args pretrained=mgoin/llava-1.5-7b-hf-FP8-Dynamic --tasks mmmu_val | |
| | Groups |Version|Filter|n-shot|Metric| |Value | |Stderr| | |
| |--------------------------------|------:|------|------|------|---|-----:|---|-----:| | |
| |mmmu_val | 0|none | |acc |↑ |0.2433|± |0.0141| | |
| | - Art and Design | 0|none | |acc |↑ |0.2250|± |0.0384| | |
| | - Business | 0|none | |acc |↑ |0.2600|± |0.0358| | |
| | - Health and Medicine | 0|none | |acc |↑ |0.3067|± |0.0377| | |
| | - Humanities and Social Science| 0|none | |acc |↑ |0.2667|± |0.0403| | |
| | - Science | 0|none | |acc |↑ |0.1667|± |0.0308| | |
| | - Tech and Engineering | 0|none | |acc |↑ |0.2381|± |0.0284| | |
| ``` | |
| ## Creation | |
| https://github.com/vllm-project/llm-compressor/pull/185 | |
| ```python | |
| from transformers import AutoProcessor | |
| from llmcompressor.modifiers.quantization import QuantizationModifier | |
| 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) | |
| # 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(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("==========================================") | |
| ``` |