Llama-3.2-1B-Instruct-FP8

Llama-3.2-1B-Instruct-FP8-Dynamic

FP8 dynamic quantization pipeline for Llama-3.2-1B-Instruct using llm_compressor.

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Overview

• This repository demonstrates how to apply FP8 dynamic quantization to the Llama-3.2-1B-Instruct model.
• The goal is to reduce memory usage and improve inference efficiency while maintaining strong performance for text generation and instruction-following tasks.

⚠️ This is a quantization pipeline, not a pre-quantized checkpoint.

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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-3.2-1B-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)")