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Model: Bllossom/llama-3.2-Korean-Bllossom-3B Source: Original Platform
2026-06-02 03:10:18 +08:00
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--- a/README.md
+++ b/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
--- a/config.json
+++ b/config.json
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+{
+  "_name_or_path": "/backup/hgyoo/crawl/3b_100per_sfted_final",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0",
+  "use_cache": false,
+  "vocab_size": 128256
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.45.0"
+}
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+}
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,16 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json