Update examples to only load required number of samples from dataset

[kylesayrs]

Purpose

• Speed up examples and demonstrate how to load dataset slices, which is especially relevant for oneshot flows

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot

# Select model and load it.
MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# Select calibration dataset.
DATASET_ID = "HuggingFaceH4/ultrachat_200k"
DATASET_SPLIT = "train_sft"

# Select number of samples. 512 samples is a good place to start.
# Increasing the number of samples can improve accuracy.
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048

# Load dataset and preprocess.
ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))

...

Changes

• Limit data loading by NUM_CALIBRATION_SAMPLES in all examples
• Leave select argument for demonstration purposes for those who are new to using the Datasets library

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