README.md

---
license: apache-2.0
datasets:
- uonlp/CulturaX
language:
- ko
---
# sangmin6600/mamba2-400m-ko

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한국어 데이터로 처음부터 학습한 Mamba2기반 소형 언어모델 및 BPE토크나이저  
사전학습만 된 모델이며 **사용시 튜닝이 필요합니다.** (it 모델 학습 예정)

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## 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.
```py
pip install -U triton
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
```
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### Direct Use
```py
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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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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#### Software

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## Citation [optional]

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## Glossary [optional]

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