test

Okay, the user is asking for open-source LLM types in Korean. Let me first make sure I understand the question correctly. They want to know the different types or categories of open-source large language models.

Hmm, open-source LLMs can be categorized in a few ways. Maybe by their training data, their architecture, or their specific use cases. Wait, but the user might just be looking for examples of popular open-source models, not the categories. Let me check the question again: “오픈소스 LLM 종류 알려줘” which translates to “Tell me the types of open-source LLMs.” So they probably want examples of different models.

I should list some well-known open-source LLMs. Let me recall the major ones. There’s Meta’s Llama series, like Llama, Llama2, Llama3. Then there’s Mistral AI’s models like Mistral 7B. Then there’s Google’s Gemma, maybe? Wait, Gemma is open-source. Also, there’s the Falcon series from TechCrunch, but I think Falcon is open-source. Oh, and the Qwen series from Tongyi Lab. Wait, Qwen is part of Alibaba, but I’m not sure if it’s fully open-source. Let me confirm.

Wait, the user wants open-source models, so I should stick to those that are actually available on platforms like Hugging Face or GitHub. Let me list the main ones:

• Llama series (Llama, Llama2, Llama3) by Meta
• Mistral 7B by Mistral AI
• Gemma by Google
• Falcon by Turing Institute
• Qwen by Tongyi Lab (but check if it’s open-source)
• BLOOM by BigScience
• Starling by Stability AI
• Mixtral by Mistral AI (it’s a mixture of experts)
• Phi series by Microsoft (Phi-2, Phi-3)
• Baichuan by Baichuan AI
• Yi series by 01.AI

Wait, I need to verify if all these are open-source. For example, Llama3 is open-source under Meta’s license. Mistral 7B is open-source. Gemma is open-source. Falcon is open-source. Qwen: I think some versions are open-source, like Qwen-Max, but maybe not all. Wait, Tongyi Lab has open-sourced some models, but I should check. Maybe better to mention Qwen as a possible one but note if it’s fully open.

BLOOM is definitely open-source. Starling is open-source? Wait, Stability AI’s models are often open-source, but I’m not sure about Starling. Let me think. Maybe it’s better to stick to the most well-known ones.

Also, maybe categorize them by their size or architecture, but the user asked for types, so probably examples.

Wait, the user might not know the difference between different models, so listing them with a brief note on each would be helpful.

Let me structure the answer: list each model, its developer, and a short note on what makes it notable.

Check if I’m missing any major ones. For example, there’s also the RWKV series, but that’s more of a different architecture (RNN-based), but maybe the user is looking for LLMs, which are usually transformer-based. So maybe not necessary to include RWKV unless it’s a major LLM.

Also, maybe mention if they are for specific tasks, like code generation (CodeLlama), but the user just wants types, so maybe not necessary.

Let me confirm each model’s open-source status:

• Llama 2 and 3: Yes, Meta released them as open-source.
• Mistral 7B: Yes, Mistral AI.
• Gemma: Yes, Google.
• Falcon: Yes, by the Turing Institute.
• BLOOM: Yes, BigScience.
• Mixtral: Yes, Mistral AI’s mixture of experts.
• Phi series: Microsoft’s Phi-2 and Phi-3 are open-source.
• Baichuan: Some versions are open-source.
• Yi: The Yi series from 01.AI is open-source.

Qwen: I think Qwen has some open-source models on Hugging Face, like Qwen, Qwen1.5, etc. So maybe include that.

Also, maybe mention that some models are smaller (like 7B parameters) while others are larger, but the user didn’t ask for that.

Wait, the user asked for “종류” which can mean types or categories. But in Korean, “종류” usually refers to different types or examples, not categories. So they probably want a list of examples.

So I’ll list the main ones with their developers and a brief note.

Make sure to mention that some models are fine-tuned for specific purposes, like CodeLlama for coding.

Wait, CodeLlama is a variant of Llama, so maybe under the Llama series.

Also, check if there are any other major ones like MPT (MosaicML’s MPT series), but I’m not sure if they’re open-source. MPT-7B is open-source, I think.

So adding MPT-7B as well.

Let me compile the list:

1. Llama series (Llama, Llama2, Llama3) – Meta
2. Mistral 7B – Mistral AI
3. Gemma – Google
4. Falcon – Turing Institute
5. BLOOM – BigScience
6. Mixtral – Mistral AI (MoE)
7. Phi series (Phi-2, Phi-3) – Microsoft
8. Baichuan series – Baichuan AI
9. Yi series – 01.AI
10. CodeLlama – Meta (for coding)
11. MPT-7B – MosaicML

Wait, but CodeLlama is part of the Llama family, so maybe list it under Llama.

Also, need to confirm if MPT-7B is open-source. Yes, MosaicML released MPT-7B as open-source.

Okay, so including that.

Now, write this in Korean, making sure to list them clearly. Maybe bullet points.

Also, note that some models might have different versions, but the main ones are listed.

Check for any inaccuracies. For example, Mistral 7B is open-source, yes. Gemma is open-source, yes.

So the answer should list these models with their developers and maybe a short note on their key features.

Make sure to use Korean for the descriptions.

Wait, the user asked for the answer in Korean, so all the model names should stay in English, but the descriptions in Korean.

So, for example:

• Llama 시리즈 (Meta): Llama, Llama2, Llama3 등이 있습니다.

But need to be precise.

Let me structure it properly.

다음은 주요 오픈소스 대형 언어 모델(LLM) 종류입니다. 각 모델은 개발사와 주요 특징을 간략히 설명했습니다.

📌 주요 오픈소스 LLM 종류

1. Llama 시리즈 (Meta)

   • Llama, Llama2, Llama3
   • Meta에서 공개한 모델로, 7B~70B 파라미터 규모로 다양한 작업에 활용 가능합니다.
   • CodeLlama (코딩 전용 버전)도 포함됨.

2. Mistral 7B (Mistral AI)

   • 7B 파라미터로 경량화되어 빠른 추론이 가능하며, Mixtral (MoE 아키텍처)도 공개되었습니다.

3. Gemma (Google)

   • 구글에서 공개한 2B/7B 규모의 경량 모델로, 가볍고 효율적입니다.

4. Falcon (TII Hyderabad)

   • 7B~180B 규모로, 대규모 훈련 데이터를 기반으로 한 고성능 모델입니다.

5. BLOOM (BigScience)

   • 176B 파라미터로 다국어 지원이 강점이며, 오픈소스 공동 개발 프로젝트입니다.

6. Phi 시리즈 (Microsoft)

   • Phi-2, Phi-3 등으로, 경량화와 고정밀도를 결합한 모델입니다.

7. Baichuan 시리즈 (Baichuan AI)

   • 중국 회사에서 개발한 7B~128B 규모의 다국어 모델로, 코딩 및 일반 작업에 최적화되었습니다.

8. Yi 시리즈 (01.AI)

   • 6B~66B 규모로, 다국어 지원과 고성능을 자랑합니다.

9. MPT-7B (MosaicML)

   • 7B 파라미터로 설계된 경량 모델로, 실시간 응용에 적합합니다.

🔍 참고 사항

• 대부분의 모델은 Hugging Face나 GitHub에서 공개되어 있습니다.
• CodeLlama는 코딩 작업에 특화된 Llama 기반 모델입니다.
• 일부 모델(예: Qwen)은 일부 버전만 오픈소스로 공개되었으니, 정확한 라이선스 확인이 필요합니다.

오픈소스 모델은 자유롭게 사용/변경이 가능하지만, 라이선스 조건을 반드시 확인하세요. 😊 G