We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) 2.4B model optimized for deployment on small or resource-constrained devices, 2) 7.8B model matching the size of its predecessor but offering improved performance, and 3) 32B model delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes.
This repository contains the instruction-tuned 2.4B language model with the following features:
Number of Parameters (without embeddings): 2.14B
Number of Layers: 30
Number of Attention Heads: GQA with 32 Q-heads and 8 KV-heads
Vocab Size: 102,400
Context Length: 32,768 tokens
Tie Word Embeddings: True (unlike 7.8B and 32B models)
Quickstart
We recommend to use transformers v4.43 or later.
Here is the code snippet to run conversational inference with the model:
python
1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
34model_name ="LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct"56model = AutoModelForCausalLM.from_pretrained(7 model_name,8 torch_dtype=torch.bfloat16,9 trust_remote_code=True,10 device_map="auto"11)12tokenizer = AutoTokenizer.from_pretrained(model_name)1314# Choose your prompt15prompt ="Explain how wonderful you are"# English example16prompt ="스스로를 자랑해 봐"# Korean example1718messages =[19{"role":"system",20"content":"You are EXAONE model from LG AI Research, a helpful assistant."},21{"role":"user","content": prompt}22]23input_ids = tokenizer.apply_chat_template(24 messages,25 tokenize=True,26 add_generation_prompt=True,27 return_tensors="pt"28)2930output = model.generate(31 input_ids.to("cuda"),32 eos_token_id=tokenizer.eos_token_id,33 max_new_tokens=128,34 do_sample=False,35)36print(tokenizer.decode(output[0]))
Note
The EXAONE 3.5 instruction-tuned language models were trained to utilize the system prompt,
so we highly recommend using the system prompts provided in the code snippet above.
Evaluation
The following table shows the evaluation results of real-world use cases. The full evaluation results can be found in the technical report.
Models
MT-Bench
LiveBench
Arena-Hard
AlpacaEval
IFEval
KoMT-Bench[1]
LogicKor
EXAONE 3.5 2.4B
7.81
33.0
48.2
37.1
73.6
7.24
8.51
Qwen 2.5 3B
7.21
25.7
26.4
17.4
60.8
5.68
5.21
Qwen 2.5 1.5B
5.72
19.2
10.6
8.4
40.7
3.87
3.60
Llama 3.2 3B
6.94
24.0
14.2
18.7
70.1
3.16
2.86
Gemma 2 2B
7.20
20.0
19.1
29.1
50.5
4.83
5.29
[1] KoMT-Bench is a dataset created by translating MT-Bench into Korean; see README for more details.
Deployment
EXAONE 3.5 models can be inferred in the various frameworks, such as:
TensorRT-LLM
vLLM
SGLang
llama.cpp
Ollama
Please refer to our EXAONE 3.5 GitHub for more details about the inference frameworks.
Quantization
We provide the pre-quantized EXAONE 3.5 models with AWQ and several quantization types in GGUF format.
Please refer to our EXAONE 3.5 collection to find corresponding quantized models.
Limitation
The EXAONE language model has certain limitations and may occasionally generate inappropriate responses. The language model generates responses based on the output probability of tokens, and it is determined during learning from training data. While we have made every effort to exclude personal, harmful, and biased information from the training data, some problematic content may still be included, potentially leading to undesirable responses. Please note that the text generated by EXAONE language model does not reflects the views of LG AI Research.
Inappropriate answers may be generated, which contain personal, harmful or other inappropriate information.
Biased responses may be generated, which are associated with age, gender, race, and so on.
The generated responses rely heavily on statistics from the training data, which can result in the generation of
semantically or syntactically incorrect sentences.
Since the model does not reflect the latest information, the responses may be false or contradictory.
LG AI Research strives to reduce potential risks that may arise from EXAONE language models. Users are not allowed
to engage in any malicious activities (e.g., keying in illegal information) that may induce the creation of inappropriate
outputs violating LG AI’s ethical principles when using EXAONE language models.
@article{exaone-3.5,
title={EXAONE 3.5: Series of Large Language Models for Real-world Use Cases},
author={LG AI Research},
journal={arXiv preprint arXiv:https://arxiv.org/abs/2412.04862},
year={2024}
}
