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Introduction
We introduce EXAONE-3.0-7.8B-Instruct, a pre-trained and instruction-tuned bilingual (English and Korean) generative model with 7.8 billion parameters.
The model was pre-trained with 8T curated tokens and post-trained with supervised fine-tuning and direct preference optimization.
It demonstrates highly competitive benchmark performance against other state-of-the-art open models of similar size.
1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
34model = AutoModelForCausalLM.from_pretrained(5"LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",6 torch_dtype=torch.bfloat16,7 trust_remote_code=True,8 device_map="auto"9)10tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct")1112# Choose your prompt13prompt ="Explain who you are"# English example14prompt ="너의 소원을 말해봐"# Korean example1516messages =[17{"role":"system",18"content":"You are EXAONE model from LG AI Research, a helpful assistant."},19{"role":"user","content": prompt}20]21input_ids = tokenizer.apply_chat_template(22 messages,23 tokenize=True,24 add_generation_prompt=True,25 return_tensors="pt"26)2728output = model.generate(29 input_ids.to("cuda"),30 eos_token_id=tokenizer.eos_token_id,31 max_new_tokens=12832)33print(tokenizer.decode(output[0]))
Note
The EXAONE 3.0 instruction-tuned language model was trained to utilize the system prompt,
so we highly recommend using the system prompts provided in the code snippet above.
Evaluation
We compared EXAONE-3.0-7.8B-Instruct with similar-sized instruction-tuned LLMs. To verify the performance of real-world use cases, we measured benchmarks that have a high correlation with LMSYS Chatbot Arena.
Some experimental results are shown below. The full evaluation results can be found in the technical report.
Language
Benchmark
EXAONE 3.0 7.8B Inst.
Llama 3.1 8B Inst.
Gemma 2 9B Inst.
QWEN 2 7B Inst.
Phi 3 7B Inst.
Mistral 7B Inst.
English
MT-Bench
9.01
7.95
8.52
8.41
8.52
7.72
Arena-Hard-v0.1
46.8
28.0
42.1
21.7
29.1
16.2
WildBench
48.2
34.5
41.5
34.9
32.8
29.0
AlpacaEval 2.0 LC
45.0
31.5
47.5
24.5
37.1
31.0
Korean
KoMT-Bench[1]
8.92
6.06
7.92
7.69
4.87
5.20
LogicKor
8.62
5.40
8.07
6.12
3.76
3.42
[1] KoMT-Bench is a dataset created by translating MT-Bench into Korean; see README for more details.
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 model. 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 model.
