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MLP-KTLim
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llama-3-Korean-Bllossom-8B

Text Generation
Transformers
Safetensors
English
Korean
llama
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metadata
language:
  - en
  - ko
license: apache-2.0
library_name: transformers
base_model:
  - meta-llama/Meta-Llama-3-8B

Bllossom | Demo | Homepage | Github |

The Bllossom language model is a Korean-English bilingual language model based on the open-source LLama3. It enhances the connection of knowledge between Korean and English. It has the following features:

  • Knowledge Linking: Linking Korean and English knowledge through additional training
  • Vocabulary Expansion: Expansion of Korean vocabulary to enhance Korean expressiveness.
  • Instruction Tuning: Tuning using custom-made instruction following data specialized for Korean language and Korean culture
  • Human Feedback: DPO has been applied
  • Vision-Language Alignment: Aligning the vision transformer with this language model

This model devel by MLPLab at Seoultech, Teddysum and Yonsei Univ

Demo Video

Bllossom-V Demo

Bllossom Demo(Kakao)γ…€γ…€γ…€γ…€γ…€γ…€γ…€γ…€

NEWS

  • [2024/04] We released Bllossom v2.0, based on llama-3
  • [2023/12] We released Bllossom-Vision v1.0, based on Bllossom
  • [2023/08] We released Bllossom v1.0, based on llama-2.
  • [2023/07] We released Bllossom v0.7, based on polyglot-ko.

Example code

Install Dependencies 

pip install torch transformers==4.40.0 accelerate

Python code with Pipeline 

import transformers
import torch

model_id = "MLP-KTLim/Bllossom"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

pipeline.model.eval()

PROMPT = '''당신은 μœ μš©ν•œ AI μ–΄μ‹œμŠ€ν„΄νŠΈμž…λ‹ˆλ‹€. μ‚¬μš©μžμ˜ μ§ˆμ˜μ— λŒ€ν•΄ μΉœμ ˆν•˜κ³  μ •ν™•ν•˜κ²Œ λ‹΅λ³€ν•΄μ•Ό ν•©λ‹ˆλ‹€.'''
instruction = "μ„œμšΈκ³Όν•™κΈ°μˆ λŒ€ν•™κ΅ MLP연ꡬ싀에 λŒ€ν•΄ μ†Œκ°œν•΄μ€˜"

messages = [
    {"role": "system", "content": f"{PROMPT}"},
    {"role": "user", "content": f"{instruction}"}
    ]

prompt = pipeline.tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=2048,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty = 1.1
)

print(outputs[0]["generated_text"][len(prompt):])

# μ„œμšΈκ³Όν•™κΈ°μˆ λŒ€ν•™κ΅ MLP연ꡬ싀은 λ©€ν‹°λͺ¨λ‹¬ μžμ—°μ–΄μ²˜λ¦¬ 연ꡬλ₯Ό ν•˜κ³  μžˆμŠ΅λ‹ˆλ‹€. ꡬ성원은 μž„κ²½νƒœ κ΅μˆ˜μ™€ κΉ€λ―Όμ€€, 김상민, 졜창수, μ›μΈν˜Έ, μœ ν•œκ²°, μž„ν˜„μ„, μ†‘μŠΉμš°, μœ‘μ •ν›ˆ, μ‹ λ™μž¬ 학생이 μžˆμŠ΅λ‹ˆλ‹€.

Python code with AutoModel 


import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = 'MLP-KTLim/Bllossom'

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

model.eval()

PROMPT = '''당신은 μœ μš©ν•œ AI μ–΄μ‹œμŠ€ν„΄νŠΈμž…λ‹ˆλ‹€. μ‚¬μš©μžμ˜ μ§ˆμ˜μ— λŒ€ν•΄ μΉœμ ˆν•˜κ³  μ •ν™•ν•˜κ²Œ λ‹΅λ³€ν•΄μ•Ό ν•©λ‹ˆλ‹€.'''
instruction = "μ„œμšΈκ³Όν•™κΈ°μˆ λŒ€ν•™κ΅ MLP연ꡬ싀에 λŒ€ν•΄ μ†Œκ°œν•΄μ€˜"

messages = [
    {"role": "system", "content": f"{PROMPT}"},
    {"role": "user", "content": f"{instruction}"}
    ]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=2048,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty = 1.1
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
# μ„œμšΈκ³Όν•™κΈ°μˆ λŒ€ν•™κ΅ MLP연ꡬ싀은 λ©€ν‹°λͺ¨λ‹¬ μžμ—°μ–΄μ²˜λ¦¬ 연ꡬλ₯Ό ν•˜κ³  μžˆμŠ΅λ‹ˆλ‹€. ꡬ성원은 μž„κ²½νƒœ κ΅μˆ˜μ™€ κΉ€λ―Όμ€€, 김상민, 졜창수, μ›μΈν˜Έ, μœ ν•œκ²°, μž„ν˜„μ„, μ†‘μŠΉμš°, μœ‘μ •ν›ˆ, μ‹ λ™μž¬ 학생이 μžˆμŠ΅λ‹ˆλ‹€.

Citation

Language Model

@misc{bllossom,
  author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim},
  title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},
  year = {2024},
  journal = {LREC-COLING 2024},
  paperLink = {\url{https://arxiv.org/pdf/2403.10882}},
 },
}

Vision-Language Model

@misc{bllossom-V,
  author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
  title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
  year = {2024},
  publisher = {GitHub},
  journal = {NAACL 2024 findings},
  paperLink = {\url{https://arxiv.org/pdf/2403.11399}},
 },
}

Contact

  • μž„κ²½νƒœ(KyungTae Lim), Professor at Seoultech. ktlim@seoultech.ac.kr
  • ν•¨μ˜κ· (Younggyun Hahm), CEO of Teddysum. hahmyg@teddysum.ai
  • κΉ€ν•œμƒ˜(Hansaem Kim), Professor at Yonsei. khss@yonsei.ac.kr

Contributor