mamba2-400m-ko-sft

sangmin6600/mamba2-400m-ko-sft

<!-- Provide a quick summary of what the model is/does. -->

본 모델은 한국어 데이터로 처음부터 학습한 Mamba2 기반 소형 언어모델입니다.
자체 구축한 BPE 토크나이저를 사용했으며, 사전학습(Pretraining) 완료 후 Instruction Tuning(IT)을 통해 질의응답, 지시문 수행, 간단한 대화 작업에 적합하도록 추가 학습되었습니다.

본 모델 개발 이후, 약 7B 규모의 Mamba2 아키텍처 확장을 시도했습니다.
그러나 학습 과정에서 activation 메모리 사용량이 매우 크게 증가하여 학습이 불가능했습니다.
특히 Mamba 계열 모델은 Transformer 구조와 달리 selective scan 기반의 state-space 연산을 사용하며,
학습 시 backward pass를 위해 더 많은 중간 activation을 보존해야 합니다.
이로 인해 동일 파라미터 규모의 Transformer 대비 activation memory overhead가 더 크게 발생했습니다.

Fully Sharded Data Parallel (FSDP), Activation Checkpointing, 8bit-optimizer, 입력 길이 8K 제한을 적용해도 80GB VRAM GPU x 4 환경에서 OOM 문제가 해결되지 않았으며, 대규모 mamba모델 학습은 추가적인 시스템 자원 또는 구조적 개선이 필요하다는 결론에 도달했습니다.

<!-- ## Model Details --> <!-- ### Model Description --> <!-- Provide a longer summary of what this model is. --> <!-- - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] --> <!-- ### Model Sources [optional] --> <!-- Provide the basic links for the model. --> <!-- - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] -->

Uses

Install Dependencies

python, cuda, torch 버전에 맞는 causal-conv1d, mamba_ssm 설치

pip install causal-conv1d
pip install mamba-ssm

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Direct Use

from transformers import AutoTokenizer, AutoModelForCausalLM

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

inputs = [
    # {"role": "system", "content": "..."},
    {"role": "user", "content": "인공지능 언어 모델이 뭐야?"},
]
text = tokenizer.apply_chat_template(inputs, tokenize=False, add_generation_prompt=True)

input_ids = tokenizer(text, return_tensors="pt")['input_ids'].to("cuda")
output_ids = model.generate(input_ids, max_new_tokens=2048, do_sample=True)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> <!-- [More Information Needed] -->

Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> <!-- [More Information Needed] -->

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Preprocessing [optional]

[More Information Needed]

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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Testing Data, Factors & Metrics

Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]