EXAONE Deep 32B EU AI Act Compliance Profile

EXAONE-Deep-32B

Introduction

We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep 2.4B outperforms other models of comparable size, 2) EXAONE Deep 7.8B outperforms not only open-weight models of comparable scale but also a proprietary reasoning model OpenAI o1-mini, and 3) EXAONE Deep 32B demonstrates competitive performance against leading open-weight models.

For more details, please refer to our documentation, blog and GitHub.

This repository contains the reasoning 32B language model with the following features:

• Number of Parameters (without embeddings): 30.95B
• Number of Layers: 64
• Number of Attention Heads: GQA with 40 Q-heads and 8 KV-heads
• Vocab Size: 102,400
• Context Length: 32,768 tokens

Quickstart

We recommend to use transformers v4.43.1 or later.

Here is the code snippet to run conversational inference with the model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

model_name = "LGAI-EXAONE/EXAONE-Deep-32B"
streaming = True    # choose the streaming option

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Choose your prompt:
#   Math example (AIME 2024)
prompt = r"""Let $x,y$ and $z$ be positive real numbers that satisfy the following system

Your obligations as a EXAONE Deep 32B deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library

Base Model