LGAI-EXAONE/EXAONE-Deep-7.8B

EXAONE-Deep-7.8B

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 7.8B language model with the following features:

• Number of Parameters (without embeddings): 6.98B
• Number of Layers: 32
• Number of Attention Heads: GQA with 32 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-7.8B"
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 of equations:
\[\log_2\left({x \over yz}\right) = {1 \over 2}\]\[\log_2\left({y \over xz}\right) = {1 \over 3}\]\[\log_2\left({z \over xy}\right) = {1 \over 4}\]
Then the value of $\left|\log_2(x^4y^3z^2)\right|$ is $\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$.

Please reason step by step, and put your final answer within \boxed{}."""
# Korean MCQA example (CSAT Math 2025)
prompt = r"""Question : $a_1 = 2$인 수열 $\{a_n\}$과 $b_1 = 2$인 등차수열 $\{b_n\}$이 모든 자연수 $n$에 대하여\[\sum_{k=1}^{n} \frac{a_k}{b_{k+1}} = \frac{1}{2} n^2\]을 만족시킬 때, $\sum_{k=1}^{5} a_k$의 값을 구하여라.

Options :
A) 120
B) 125
C) 130
D) 135
E) 140

Please reason step by step, and you should write the correct option alphabet (A, B, C, D or E) within \\boxed{}."""

messages = [
{"role": "user", "content": prompt}
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)

if streaming:
streamer = TextIteratorStreamer(tokenizer)
thread = Thread(target=model.generate, kwargs=dict(
input_ids=input_ids.to("cuda"),
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=32768,
do_sample=True,
temperature=0.6,
top_p=0.95,
streamer=streamer
))
thread.start()

for text in streamer:
print(text, end="", flush=True)
else:
output = model.generate(
input_ids.to("cuda"),
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=32768,
do_sample=True,
temperature=0.6,
top_p=0.95,
)
print(tokenizer.decode(output[0]))

> ### Note > The EXAONE Deep models are trained with an optimized configuration, > so we recommend following the [Usage Guideline](#usage-guideline) section to achieve optimal performance.

Evaluation

The following table shows the evaluation results of reasoning tasks such as math and coding. The full evaluation results can be found in the documentation.

Models MATH-500 (pass@1) AIME 2024 (pass@1 / cons@64) AIME 2025 (pass@1 / cons@64) CSAT Math 2025 (pass@1) GPQA Diamond (pass@1) Live Code Bench (pass@1)

EXAONE Deep 32B 95.7 72.1 / 90.0 65.8 / 80.0 94.5 66.1 59.5

DeepSeek-R1-Distill-Qwen-32B 94.3 72.6 / 83.3 55.2 / 73.3 84.1 62.1 57.2

QwQ-32B 95.5 79.5 / 86.7 67.1 / 76.7 94.4 63.3 63.4

DeepSeek-R1-Distill-Llama-70B 94.5 70.0 / 86.7 53.9 / 66.7 88.8 65.2 57.5

DeepSeek-R1 (671B) 97.3 79.8 / 86.7 66.8 / 80.0 89.9 71.5 65.9

EXAONE Deep 7.8B 94.8 70.0 / 83.3 59.6 / 76.7 89.9 62.6 55.2

DeepSeek-R1-Distill-Qwen-7B 92.8 55.5 / 83.3 38.5 / 56.7 79.7 49.1 37.6

DeepSeek-R1-Distill-Llama-8B 89.1 50.4 / 80.0 33.6 / 53.3 74.1 49.0 39.6

OpenAI o1-mini 90.0 63.6 / 80.0 54.8 / 66.7 84.4 60.0 53.8

EXAONE Deep 2.4B 92.3 52.5 / 76.7 47.9 / 73.3 79.2 54.3 46.6

DeepSeek-R1-Distill-Qwen-1.5B 83.9 28.9 / 52.7 23.9 / 36.7 65.6 33.8 16.9

Deployment

EXAONE Deep models can be inferred in the various frameworks, such as:

• TensorRT-LLM
• vLLM
• SGLang
• llama.cpp
• Ollama
• LM-Studio

Please refer to our EXAONE Deep GitHub for more details about the inference frameworks.

Quantization

We provide the pre-quantized EXAONE Deep models with AWQ and several quantization types in GGUF format. Please refer to our EXAONE Deep collection to find corresponding quantized models.

Usage Guideline

To achieve the expected performance, we recommend using the following configurations:

1. Ensure the model starts with \n for reasoning steps. The model's output quality may be degraded when you omit it. You can easily apply this feature by using tokenizer.apply_chat_template() with add_generation_prompt=True. Please check the example code on [Quickstart](#quickstart) section. 2. The reasoning steps of EXAONE Deep models enclosed by \n...\n usually have lots of tokens, so previous reasoning steps may be necessary to be removed in multi-turn situation. The provided tokenizer handles this automatically. 3. Avoid using system prompt, and build the instruction on the user prompt. 4. Additional instructions help the models reason more deeply, so that the models generate better output.

• For math problems, the instructions "Please reason step by step, and put your final answer within \boxed{}." are helpful.
• For more information on our evaluation setting including prompts, please refer to our Documentation.

5. In our evaluation, we use temperature=0.6 and top_p=0.95 for generation. 6. When evaluating the models, it is recommended to test multiple times to assess the expected performance accurately.

Limitation

The EXAONE language model has certain limitations and may…