Instructions to use Bllossom/llama-3.2-Korean-Bllossom-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bllossom/llama-3.2-Korean-Bllossom-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Bllossom/llama-3.2-Korean-Bllossom-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Bllossom/llama-3.2-Korean-Bllossom-3B")
model = AutoModelForMultimodalLM.from_pretrained("Bllossom/llama-3.2-Korean-Bllossom-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bllossom/llama-3.2-Korean-Bllossom-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bllossom/llama-3.2-Korean-Bllossom-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bllossom/llama-3.2-Korean-Bllossom-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bllossom/llama-3.2-Korean-Bllossom-3B

SGLang

How to use Bllossom/llama-3.2-Korean-Bllossom-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Bllossom/llama-3.2-Korean-Bllossom-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bllossom/llama-3.2-Korean-Bllossom-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Bllossom/llama-3.2-Korean-Bllossom-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bllossom/llama-3.2-Korean-Bllossom-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bllossom/llama-3.2-Korean-Bllossom-3B with Docker Model Runner:
```
docker model run hf.co/Bllossom/llama-3.2-Korean-Bllossom-3B
```

llama-3.2-Korean-Bllossom-3B / README.md

ShinDJ

Update README.md

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	---
	base_model:
	- meta-llama/Meta-Llama-3.2-3B
	language:
	- en
	- ko
	library_name: transformers
	license: llama3.2
	---


	<a href="https://github.com/MLP-Lab/Bllossom">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/64a90711c05da19ca834f690/a0VE5UCY1HCEhaHtp3mGa.png" alt="image" width="30%" height="30%">
	</a>

	# Update!
	* [2024.12.06] 훨씬 강력한 최신 Bllossom-AICA-5B로 업데이트 되었습니다 [링크](https://huggingface.co/Bllossom/llama-3.2-Korean-Bllossom-AICA-5B)
	* [2024.10.08] Bllossom-3B 모델이 최초 업데이트 되었습니다.


	# Bllossom \| [Demo]() \| [Homepage](https://www.bllossom.ai/) \| [Github](https://github.com/MLP-Lab/Bllossom) \|

	```bash
	저희 Bllossom 팀에서 Bllossom-3B 모델을 공개합니다.
	llama3.2-3B가 나왔는데 한국어가 포함 안되었다구?? 이번 Bllossom-3B는 한국어가 지원되지 않는 기본 모델을 한국어-영어로 강화모델입니다.
	- 100% full-tuning으로 150GB의 정제된 한국어로 추가 사전학습 되었습니다. (GPU많이 태웠습니다)
	- 굉장히 정제된 Instruction Tuning을 진행했습니다.
	- 영어 성능을 전혀 손상시키지 않은 완전한 Bilingual 모델입니다.
	- Instruction tuning만 진행했습니다. DPO 등 성능 올릴 방법으로 튜닝해보세요.
	- MT-Bench, LogicKor 등 벤치마크 점수를 잘받기 위해 정답데이터를 활용하거나 혹은 벤치마크를 타겟팅 해서 학습하지 않았습니다. (해당 벤치마크 타게팅해서 학습하면 8점도 나옵니다...)

	언제나 그랬듯 해당 모델은 상업적 이용이 가능합니다.

	1. Bllossom은 AAAI2024, NAACL2024, LREC-COLING2024 (구두) 발표되었습니다.
	2. 좋은 언어모델 계속 업데이트 하겠습니다!! 한국어 강화를위해 공동 연구하실분(특히논문) 언제든 환영합니다!!
	```



	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_id = 'Bllossom/llama-3.2-Korean-Bllossom-3B'

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)
	instruction = "철수가 20개의 연필을 가지고 있었는데 영희가 절반을 가져가고 민수가 남은 5개를 가져갔으면 철수에게 남은 연필의 갯수는 몇개인가요?"

	messages = [
	{"role": "user", "content": f"{instruction}"}
	]

	input_ids = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	return_tensors="pt"
	).to(model.device)

	terminators = [
	tokenizer.convert_tokens_to_ids("<\|end_of_text\|>"),
	tokenizer.convert_tokens_to_ids("<\|eot_id\|>")
	]

	outputs = model.generate(
	input_ids,
	max_new_tokens=1024,
	eos_token_id=terminators,
	do_sample=True,
	temperature=0.6,
	top_p=0.9
	)

	print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
	```
	```
	철수가 20개의 연필을 가지고 있었고 영희가 절반을 가져가면, 영희가 가져간 연필의 갯수는 20 / 2 = 10개입니다.

	이제 철수가 남은 연필의 갯수를 계산해보겠습니다. 영희가 10개를 가져간 후 철수가 남은 연필의 갯수는 20 - 10 = 10개입니다.

	민수가 남은 5개를 가져갔으므로, 철수가 남은 연필의 갯수는 10 - 5 = 5개입니다.

	따라서 철수가 남은 연필의 갯수는 5개입니다.
	```

	## Supported by

	- AICA <img src="https://aica-gj.kr/images/logo.png" width="20%" height="20%">

	## Citation
	Language Model
	```text
	@misc{bllossom,
	author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim},
	title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},
	year = {2024},
	journal = {LREC-COLING 2024},
	paperLink = {\url{https://arxiv.org/pdf/2403.10882}},
	},
	}
	```

	Vision-Language Model
	```text
	@misc{bllossom-V,
	author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
	title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
	year = {2024},
	publisher = {GitHub},
	journal = {NAACL 2024 findings},
	paperLink = {\url{https://arxiv.org/pdf/2403.11399}},
	},
	}
	```

	## Contact
	- 임경태(KyungTae Lim), Professor at Seoultech. `ktlim@seoultech.ac.kr`
	- 함영균(Younggyun Hahm), CEO of Teddysum. `hahmyg@teddysum.ai`
	- 김한샘(Hansaem Kim), Professor at Yonsei. `khss@yonsei.ac.kr`

	## Contributor
	- 유한결(Hangyeol Yoo), hgyoo@seoultech.ac.kr
	- 최창수(Chansu Choi), choics2623@seoultech.ac.kr