Upload MistralForCausalLM

README.md

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----
-license: apache-2.0
-language:
-- ko
-- en
-metrics:
-- accuracy
-base_model:
-- openchat/openchat_3.5
-pipeline_tag: text-generation
----
-### ⛱   해당 모델은은 openchat3.5를 Foundation 모델로 하는 한국어 및 한국의 다양한 
-### 문화에 적용할 수 있도록 하기 위해
-### 개발 되었으며 자체 제작한 53영역의 한국어 데이터를 활용하여 한국 사회 가치와 
-### 문화를 이해하는 모델 입니다. ✌
-
-                
-
-# ❶ 모델 설명
-- 모델명 및 주요기능:
-  해당 모델은은 OpenChat 3.5 모델을 기반으로 SFT 방식으로 파인튜닝된 Mistral 7B / openchat3.5 기반 모델입니다.
-  한국어와 한국의 다양한 문화적 맥락을 이해하도록 설계되었으며 ✨✨, 자체 제작한 135개 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 모델은은 Mistral 7B 모델을 기반으로, 파라미터 수는 70억 개(7B)로 구성된 고성능 언어 모델입니다.
-  이 모델은 OpenChat 3.5를 파운데이션 모델로 삼아, SFT(지도 미세 조정) 방식을 통해 한국어와 한국 문화에 특화된 성능을 발휘하도록 훈련되었습니다.
-  Mistral 7B의 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
-  
-  tokenizer = AutoTokenizer.from_pretrained("")
-  model = AutoModel.from_pretrained("")
-  
-  inputs = tokenizer("안녕하세요", return_tensors="pt")
-  outputs = model(**inputs)
-</code></pre>
-
-
----
-Here’s the English version of the provided text:
-
-
-
-# ❶ Model Description
-
-**Model Name and Key Features**:  
-KTDSbaseLM v0.11 is based on the OpenChat 3.5 model, fine-tuned using the SFT method on the Mistral 7B model. 
-It is designed to understand Korean and various cultural contexts, utilizing data from 135 domains in Korean society. 
-The model supports tasks such as text generation, conversation inference, document summarization, 
-question answering, sentiment analysis, and other NLP tasks. 
-Its applications span fields like law, finance, science, education, business, and cultural research.
-
-**Model Architecture**:  
-KTDSBaseLM v0.11 is a high-performance language model with 7 billion parameters based on the Mistral 7B model. 
-It uses OpenChat 3.5 as the foundation and is fine-tuned using SFT to excel in Korean language and culture. 
-The streamlined Mistral 7B architecture ensures fast inference and memory efficiency, 
-optimized for various NLP tasks like text generation, question answering, document summarization, and sentiment analysis.
-
----
-
-# ❷ Training Data
-
-KTDSbaseLM v0.11 was trained on 3.6GB of data, comprising 2.33 million Q&A instances. 
-This includes 1.33 million multiple-choice questions across 53 domains such as history, 
-finance, law, tax, and science, trained with the Chain of Thought method. Additionally, 
-1.3 million short-answer questions cover 38 domains including history, finance, and law. 
-
-**Training Instruction Dataset Format**:  
-`{"prompt": "prompt text", "completion": "ideal generated text"}`
-
----
-
-# ❸ Use Cases
-
-KTDSbaseLM v0.11 can be used across multiple fields, such as:
-
-- **Education**: Answering questions and generating explanations for subjects like history, math, and science.
-- **Business**: Providing responses and summaries for legal, financial, and tax-related queries.
-- **Research and Culture**: Performing NLP tasks, sentiment analysis, document generation, and translation.
-- **Customer Service**: Generating conversations and personalized responses for users.
-
-This model is highly versatile in various NLP tasks.
-
----
-
-# ❹ Limitations
-
-KTDSBaseLM v0.11 is specialized in Korean language and culture. 
-However, it may lack accuracy in responding to topics outside its scope, 
-such as international or specialized data. 
-Additionally, it may have limited reasoning ability for complex logical problems and 
-may produce biased responses if trained on biased data.
-
----
-
-
-
+---
+license: apache-2.0
+---

config.json

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+{
+  "_name_or_path": "/workspace/openchat/model/openchat_peft",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
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+  "bos_token_id": 1,
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+  "model_type": "mistral",
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+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 32002
+}

generation_config.json

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+  "temperature": 0.5,
+  "transformers_version": "4.44.2"
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