sangmin6600/mamba2-400m-ko

본 모델은 한국어 데이터로 처음부터 학습한 Mamba2 기반 소형 언어모델입니다. 자체 구축한 BPE 토크나이저를 사용했으며, 한국어 데이터로 사전학습(Pretraining) 되었습니다.
사전학습만 된 모델이며 사용시 튜닝이 필요합니다. (it 모델 학습 예정)

Instruction Tuning 완료
sangmin6600/mamba2-400m-ko-sft

Uses

Install Dependencies

python, cuda, torch 버전에 맞는 causal-conv1d, mamba_ssm 설치
주석처리한 부분의 경우 python 3.10, cuda 11.8 torch 2.5 의 예시입니다.

pip install causal-conv1d
#pip install https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.5.0.post8/causal_conv1d-1.5.0.post8+cu11torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
pip install mamba-ssm
#pip install https://github.com/state-spaces/mamba/releases/download/v2.2.4/mamba_ssm-2.2.4+cu11torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl

Direct Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "sangmin6600/mamba2-400m-ko"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to("cuda")

text = """언어모델은"""
input_ids = tokenizer(text, return_tensors="pt")['input_ids'].to("cuda")
output_ids = model.generate(input_ids, max_new_tokens=100, do_sample=True)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

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Bias, Risks, and Limitations

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How to Get Started with the Model

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Training Details

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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