metadata

language:
  - ko
  - en
license: llama3
library_name: transformers
tags:
  - llama
  - llama-3
base_model:
  - meta-llama/Meta-Llama-3-8B-Instruct
datasets:
  - MarkrAI/KoCommercial-Dataset

Waktaverse-Llama-3-KO-8B-Instruct Model Card

Model Details

Waktaverse-Llama-3-KO-8B-Instruct is a Korean language model developed by Waktaverse AI team. This large language model is a specialized version of the Meta-Llama-3-8B-Instruct, tailored for Korean natural language processing tasks. It is designed to handle a variety of complex instructions and generate coherent, contextually appropriate responses.

Developed by: Waktaverse AI
Model type: Large Language Model
Language(s) (NLP): Korean, English
License: Llama3
Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct

Model Sources

Repository: GitHub
Paper : [More Information Needed]

Uses

Direct Use

The model can be utilized directly for tasks such as text completion, summarization, and question answering without any fine-tuning.

Out-of-Scope Use

This model is not intended for use in scenarios that involve high-stakes decision-making including medical, legal, or safety-critical areas due to the potential risks of relying on automated decision-making. Moreover, any attempt to deploy the model in a manner that infringes upon privacy rights or facilitates biased decision-making is strongly discouraged.

Bias, Risks, and Limitations

While Waktaverse Llama 3 is a robust model, it shares common limitations associated with machine learning models including potential biases in training data, vulnerability to adversarial attacks, and unpredictable behavior under edge cases. There is also a risk of cultural and contextual misunderstanding, particularly when the model is applied to languages and contexts it was not specifically trained on.

How to Get Started with the Model

You can run conversational inference using the Transformers Auto classes. We highly recommend that you add Korean system prompt for better output. Adjust the hyperparameters as you need.

Example Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = (
    "cuda:0" if torch.cuda.is_available() else # Nvidia GPU
    "mps" if torch.backends.mps.is_available() else # Apple Silicon GPU
    "cpu"
)

model_id = "PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct"

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

################################################################################
# Generation parameters
################################################################################
num_return_sequences=1
max_new_tokens=1024
temperature=0.6
top_p=0.9
repetition_penalty=1.1

def prompt_template(system, user):
    return (
        "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n"
        f"{system}<|eot_id|>"
        
        "<|start_header_id|>user<|end_header_id|>\n\n"
        f"{user}<|eot_id|>"
        
        "<|start_header_id|>assistant<|end_header_id|>\n\n"
    )

def generate_response(system ,user):
    prompt = prompt_template(system, user)
    
    input_ids = tokenizer.encode(
        prompt,
        add_special_tokens=False,
        return_tensors="pt"
    ).to(device)
    
    outputs = model.generate(
        input_ids=input_ids,
        pad_token_id=tokenizer.eos_token_id,
        num_return_sequences=num_return_sequences,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=repetition_penalty
    )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=False)

system_prompt = "다음 지시사항에 대한 응답을 작성해주세요."
user_prompt = "피보나치 수열에 대해 설명해주세요."
response = generate_response(system_prompt, user_prompt)
print(response)

Example Output

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

다음 지시사항에 대한 응답을 작성해주세요.<|eot_id|><|start_header_id|>user<|end_header_id|>

피보나치 수열에 대해 설명해주세요.<|eot_id|><|start_header_id|>assistant<|end_header_id|>

피보나치 수열은 0과 1 두 개의 숫자로 시작하는 무한정 길이가 있는 수열입니다. 이 수열의 각 요소를 다음 공식으로 계산합니다: F(n) = F(n-1) + F(n-2), 여기서 F(0) = 0, F(1) = 1입니다.

예를 들어, 첫 번째 10개의 피보나치 숫자는 다음과 같습니다:

0, 1, 1, 2, 3, 5, 8, 13, 21, 34

Training Details

Training Data

The model is trained on the MarkrAI/KoCommercial-Dataset, which consists of various commercial texts in Korean.

Training Procedure

The model training used LoRA for computational efficiency. 0.04 billion parameters(0.51% of total parameters) were trained.

Training Hyperparameters

################################################################################
# bitsandbytes parameters
################################################################################
load_in_4bit=True
bnb_4bit_compute_dtype=torch.bfloat16
bnb_4bit_quant_type="nf4"
bnb_4bit_use_double_quant=True

################################################################################
# LoRA parameters
################################################################################
task_type="CAUSAL_LM"
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
r=16
lora_alpha=32
lora_dropout=0.1
bias="none"

################################################################################
# TrainingArguments parameters
################################################################################
num_train_epochs=1
per_device_train_batch_size=1
gradient_accumulation_steps=1
gradient_checkpointing=True
learning_rate=2e-5
lr_scheduler_type="cosine"
warmup_ratio=0.1
optim = "paged_adamw_32bit"
weight_decay=0.01

################################################################################
# SFT parameters
################################################################################
max_seq_length=2048
packing=False

Evaluation

Metrics

Ko-HellaSwag:
Ko-MMLU:
Ko-Arc:
Ko-Truthful QA:
Ko-CommonGen V2:

Results

Benchmark	Waktaverse Llama 3 8B	Llama 3 8B
Ko-HellaSwag:	0	0
Ko-MMLU:	0	0
Ko-Arc:	0	0
Ko-Truthful QA:	0	0
Ko-CommonGen V2:	0	0

Technical Specifications

Compute Infrastructure

Hardware

GPU: NVIDIA GeForce RTX 4080 SUPER

Software

Operating System: Linux
Deep Learning Framework: Hugging Face Transformers, PyTorch

Training Details

Training time: 18 hours
More details on Weights & Biases

Citation

Waktaverse-Llama-3

@article{waktaversellama3modelcard,
  title={Waktaverse Llama 3 Model Card},
  author={AI@Waktaverse},
  year={2024},
  url = {https://huggingface.co/PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct}

Llama-3

@article{llama3modelcard,
  title={Llama 3 Model Card},
  author={AI@Meta},
  year={2024},
  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}

Model Card Authors

PathFinderKR