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+ ---
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+ base_model:
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+ - HuggingFaceM4/Idefics3-8B-Llama3
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+ ---
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+ # FP8-Dynamic quant for Idefics2-8B-Llama3, requires about ~12 GB
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+
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+ ## vLLM usage
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+ ```py
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+ vllm serve leon-se/Idefics3-8B-Llama3-FP8-Dynamic --trust-remote-code
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+ ```
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+
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+ ## Code used for quantization
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+ ```py
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+ from transformers import AutoProcessor, AutoModelForVision2Seq
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+ from llmcompressor.modifiers.quantization import QuantizationModifier
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+ from llmcompressor.transformers import oneshot
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+
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+ model_name = "HuggingfaceM4/Idefics3-8B-Llama3"
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+
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+ processor = AutoProcessor.from_pretrained(model_name)
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+ model = AutoModelForVision2Seq.from_pretrained(model_name, device_map="cuda", torch_dtype="auto", trust_remote_code=True)
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+
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+ recipe = QuantizationModifier(
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+ targets="Linear",
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+ scheme="FP8_DYNAMIC",
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+ ignore=["re:.*lm_head", "re:model.vision_model.*", "re:model.connector.*"],
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+ )
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+
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+ SAVE_DIR = model_name.split("/")[1] + "-FP8-Dynamic"
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+ oneshot(model=model, recipe=recipe, output_dir=SAVE_DIR)
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+ processor.save_pretrained(SAVE_DIR)
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+ ```