From 790eeee5957703f18e475656b5b4180effbf95db Mon Sep 17 00:00:00 2001
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Model: SEOKDONG/llama3.2_1B_korean_v0.2_sft_by_aidx
Source: Original Platform
---
 .gitattributes          |   36 +
 README.md               |   88 ++
 config.json             |   40 +
 generation_config.json  |   12 +
 model.safetensors       |    3 +
 special_tokens_map.json |   16 +
 tokenizer.json          |    3 +
 tokenizer_config.json   | 2062 +++++++++++++++++++++++++++++++++++++++
 8 files changed, 2260 insertions(+)
 create mode 100644 .gitattributes
 create mode 100644 README.md
 create mode 100644 config.json
 create mode 100644 generation_config.json
 create mode 100644 model.safetensors
 create mode 100644 special_tokens_map.json
 create mode 100644 tokenizer.json
 create mode 100644 tokenizer_config.json

diff --git a/.gitattributes b/.gitattributes
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..406f6aa
--- /dev/null
+++ b/README.md
@@ -0,0 +1,88 @@
+---
+library_name: transformers
+tags:
+- korean
+- 1B
+- ko
+license: apache-2.0
+language:
+- ko
+metrics:
+- accuracy
+base_model:
+- meta-llama/Llama-3.2-1B
+pipeline_tag: text-generation
+---
+### ⛱   해당 모델은은 LlaMA3.2 1B를 Foundation 모델로 하는 한국어 및 한국의 다양한 
+### 문화에 적용할 수 있도록 하기 위해
+### 개발 되었으며 자체 제작한 53개 영역의 한국어 데이터를 활용하여 한국 사회 가치와 
+### 문화를 이해하는 모델 입니다. Thanks for ktds✌
+
+### V0.2 Epoch=2
+                
+
+# ❶ 모델 설명
+- 모델명 및 주요기능:
+  해당 모델은은 LlaMA3.2 1B 모델을 기반으로 SFT 방식으로 파인튜닝된 모델입니다.
+  한국어와 한국의 다양한 문화적 맥락을 이해하도록 설계되었으며 ✨✨, 자체 제작한 53개 영역의 한국어
+  데이터를 활용해 한국 사회의 가치와 문화를 반영합니다.
+  주요 기능으로는 텍스트 생성, 대화 추론, 문서 요약, 질의응답, 감정 분석 및 자연어 처리 관련 다양한 작업을 지원하며,
+  활용 분야는 법률, 재무, 과학, 교육, 비즈니스, 문화 연구 등 다양한 분야에서 응용될 수 있습니다.
+- 모델 아키텍처:
+  해당 모델은 LlaMA3.2 1B 모델을 기반으로, 파라미터 수는 10억 개(1B)로 구성된 고성능 언어 모델입니다.
+  이 모델은LlaMA3.2 1B를 파운데이션 모델로 삼아, SFT(지도 미세 조정) 방식을 통해 한국어와 한국 문화에 특화된 성능을 발휘하도록 훈련되었습니다.
+  LlaMA3.2 1B의 경량화된 구조는 빠른 추론 속도와 메모리 효율성을 보장하며, 다양한 자연어 처리 작업에 적합하게 최적화되어 있습니다.
+  이 아키텍처는 텍스트 생성, 질의응답, 문서 요약, 감정 분석과 같은 다양한 작업에서 탁월한 성능을 보여줍니다.
+
+# ❷ 학습 데이터
+- 해당 모델은은 자체 개발한 총 3.6GB 크기의 데이터를 바탕으로 학습되었습니다. 모두 233만 건의 QnA, 요약, 분류 등 데이터를 포함하며,
+  그 중 133만 건은 53개 영역의 객관식 문제로 구성되었습니다. 이 영역에는 한국사, 사회, 재무, 법률, 세무, 수학, 생물, 물리, 화학 등이 포함되며,
+  Chain of Thought 방식으로 학습되었습니다. 또한 130만 건의 주관식 문제는 한국사, 재무, 법률, 세무, 수학 등 38개 영역에 걸쳐 학습되었습니다.
+  학습 데이터 중 한국의 사회 가치와 인간의 감정을 이해하고 지시한 사항에 따라 출력할 수 있는 데이터를 학습하였습니다.
+- 학습 Instruction Datasets Format: 
+  <pre><code>{"prompt": "prompt text", "completion": "ideal generated text"}</code></pre>
+    
+# ❸ 사용 사례
+  해당 모델은 다양한 응용 분야에서 사용될 수 있습니다. 예를 들어:
+- 교육 분야: 역사, 수학, 과학 등 다양한 학습 자료에 대한 질의응답 및 설명 생성.
+- 비즈니스: 법률, 재무, 세무 관련 질의에 대한 답변 제공 및 문서 요약.
+- 연구 및 문화: 한국 사회와 문화에 맞춘 자연어 처리 작업, 감정 분석, 문서 생성 및 번역.
+- 고객 서비스: 사용자와의 대화 생성 및 맞춤형 응답 제공.
+- 이 모델은 다양한 자연어 처리 작업에서 높은 활용도를 가집니다.
+
+# ❹ 한계 ⛈⛈
+- 해당 모델은 한국어와 한국 문화에 특화되어 있으나, 
+  특정 영역(예: 최신 국제 자료, 전문 분야)의 데이터 부족으로 인해 다른 언어 또는
+  문화에 대한 응답의 정확성이 떨어질 수 있습니다.
+  또한, 복잡한 논리적 사고를 요구하는 문제에 대해 제한된 추론 능력을 보일 수 있으며,
+  편향된 데이터가 포함될 경우 편향된 응답이 생성될 가능성도 존재합니다.
+
+# ❺ 사용 방법
+  <pre><code>
+from transformers import AutoModel, AutoTokenizer
+
+base_model = "SEOKDONG/llama3.2_1B_korean_v0.2_sft_by_aidx"
+    
+model = AutoModelForCausalLM.from_pretrained(base_LLM_model)
+tokenizer = AutoTokenizer.from_pretrained(base_LLM_model)
+
+from tqdm import tqdm
+for i in tqdm(range(0,1)): #len(answer_list))):
+    input_text =  """
+    당신은 AI 비서입니다. 다음 질문에 맞는 답변을 고르세요. 답변은 1,2,3,4 중에 하나만 선택하세요. 
+    다음 중 탄수화물의 일종으로, 포도당의 이중 결합으로 구성되어 있으며 자주 사용되는 설탕의 성분은 무엇인가? 
+    선택지: 1. 셀룰로오스 2. 자당 3. 녹말 4. 글리코겐 답변:"""
+    
+    inputs = tokenizer(input_text, return_tensors="pt")
+    inputs = inputs.to("cuda:0")
+    # 3. 모델을 사용한 추론
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=1024,temperature=0.1, do_sample=True, repetition_penalty=1.10)
+    # 4. 결과 디코딩
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(result.split("답변:")[1].strip())
+
+    결과: '2. 자당입니다. 자당은 포도당이 두 개의 이중 결합으로 이루어진 형태로 존재하며, 주로 설탕의 주요 성분으로 사용됩니다. 
+        셀룰로오스는 단백질, 녹말은 식물성 단백질, 글리코겐은 지방과 관련된 단백질로, 모두 설탕의 성분이 아닙니다. 따라서 정답은 2입니다.'
+</code></pre>
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..5366f9d
--- /dev/null
+++ b/config.json
@@ -0,0 +1,40 @@
+{
+  "_name_or_path": "./workspace/llama3/1B_last/",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "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": "float16",
+  "transformers_version": "4.46.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..aba45c8
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.46.3"
+}
diff --git a/model.safetensors b/model.safetensors
new file mode 100644
index 0000000..763e978
--- /dev/null
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:077298e5e7beea07347e1b67db090c798ffe925eefed6f6923ffdb201894ab4c
+size 2471645464
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000..02ee80b
--- /dev/null
+++ 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
+  }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000..1c1d8d5
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
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+oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
+size 17209920
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000..4ff488a
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,2062 @@
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+      "special": true
+    },
+    "128228": {
+      "content": "<|reserved_special_token_220|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128229": {
+      "content": "<|reserved_special_token_221|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128230": {
+      "content": "<|reserved_special_token_222|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128231": {
+      "content": "<|reserved_special_token_223|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128232": {
+      "content": "<|reserved_special_token_224|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128233": {
+      "content": "<|reserved_special_token_225|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128234": {
+      "content": "<|reserved_special_token_226|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128235": {
+      "content": "<|reserved_special_token_227|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128236": {
+      "content": "<|reserved_special_token_228|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128237": {
+      "content": "<|reserved_special_token_229|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128238": {
+      "content": "<|reserved_special_token_230|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128239": {
+      "content": "<|reserved_special_token_231|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128240": {
+      "content": "<|reserved_special_token_232|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128241": {
+      "content": "<|reserved_special_token_233|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128242": {
+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_235|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}