Meta-Llama-3-8B-Instruct-FP8

Llama-3-8B-Instruct-FP8

This project demonstrates how to apply FP8 dynamic quantization to Llama-3.1-8B-Instruct using llm_compressor. It provides a pipeline to reduce memory usage and improve inference efficiency while maintaining strong performance.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

# Define the model ID for the model you want to quantize
MODEL_ID = "meta-llama/Llama-3.1-8B-Instruct"

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, device_map="auto", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# Configure the quantization recipe
recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

# Apply the quantization algorithm
oneshot(model=model, recipe=recipe)

# Define the directory to save the quantized model
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"

# Save the quantized model and tokenizer
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)

print(f"Quantized model saved to (SAVE_DIR)")