Meta-Llama-3-8B-quantized.w4a16

Creation

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor import oneshot
from llmcompressor.modifiers.awq import AWQModifier
from llmcompressor.utils import dispatch_for_generation


MODEL_ID = "meta-llama/Meta-Llama-3-8B"

model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
tokenizer.chat_template = ("{% for message in messages %}{{ message['role'] }}: {{ message['content'] }}\n{% endfor %}")

DATASET_ID = "HuggingFaceH4/ultrachat_200k"
DATASET_SPLIT = "train_sft"

NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048
ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
ds = ds.shuffle(seed=42)


def preprocess(example):
    return {
        "text": tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
        )
    }


ds = ds.map(preprocess)


def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        max_length=MAX_SEQUENCE_LENGTH,
        truncation=True,
        add_special_tokens=False,
    )


recipe = [
    AWQModifier(ignore=["lm_head"], scheme="W4A16_ASYM", targets=["Linear"]),
]

oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)

print("\n\n")
print("========== SAMPLE GENERATION ==============")
dispatch_for_generation(model)
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(
    model.device
)
output = model.generate(input_ids, max_new_tokens=100)
print(tokenizer.decode(output[0]))
print("==========================================\n\n")

SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-awq-asym"
try:
    model.save_pretrained(SAVE_DIR, save_compressed=True)
except ValueError:
    print("Non-contiguous tensor issue detected, saving with safe_serialization=False")
    model.save_pretrained(SAVE_DIR, safe_serialization=False)
tokenizer.save_pretrained(SAVE_DIR)