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---
license: gemma
base_model:
- google/gemma-2-27b-it
pipeline_tag: text-generation
---
FP-8 quantized version of google/gemma-2-27b-it quantized with **compute sponsored by Arrow and Nvidia through Danish Data Science Community**.
Quantized using this script:
```python
from llmcompressor.transformers import SparseAutoModelForCausalLM
from transformers import AutoTokenizer
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
MODEL_ID = "google/gemma-2-27b-it"
model = SparseAutoModelForCausalLM.from_pretrained(
MODEL_ID, device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
# Configure the simple PTQ quantization
recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
# Apply the quantization algorithm.
oneshot(model=model, recipe=recipe)
# Save the model.
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)
``` |