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1from llmcompressor.transformers import SparseAutoModelForCausalLM
2from transformers import AutoTokenizer
3from llmcompressor.transformers import oneshot
4from llmcompressor.modifiers.quantization import QuantizationModifier
5
6
7MODEL_ID = "google/gemma-2-27b-it"
8
9model = SparseAutoModelForCausalLM.from_pretrained(
10 MODEL_ID, device_map="auto", torch_dtype="auto")
11tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
12
13
14# Configure the simple PTQ quantization
15recipe = QuantizationModifier(
16 targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
17
18# Apply the quantization algorithm.
19oneshot(model=model, recipe=recipe)
20
21# Save the model.
22SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
23model.save_pretrained(SAVE_DIR)
24tokenizer.save_pretrained(SAVE_DIR)