Create README.md

README.md

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+---
+library_name: transformers
+license: other
+base_model: NousResearch/Hermes-3-Llama-3.1-8B
+tags:
+- llama-factory
+- full
+- unsloth
+- generated_from_trainer
+model-index:
+- name: kimhyeongjun/Hermes-3-Llama-3.1-8B-Ko-Finance-Advisors
+  results: []
+---
+
+
+# kimhyeongjun/Hermes-3-Llama-3.1-8B-Ko-Finance-Advisors
+
+This model is a fine-tuned version of [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) on the Korean_synthetic_financial_dataset_21K.
+
+이 모델은 한국_합성_금융_데이터셋_21K의 [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)를 미세 조정한 버전입니다.
+
+## Model description
+Based on finance PDF data collected directly from the web, we refined the raw data using the 'meta-llama/Meta-Llama-3.1-70B-Instruct' model. 
+After generating synthetic data based on the cleaned data, we further evaluated the quality of the generated data using the 'meta-llama/Llama-Guard-3-8B' and 'RLHFlow/ArmoRM-Llama3-8B-v0.1' models. 
+We then used 'Alibaba-NLP/gte-large-en-v1.5' to extract embeddings and applied Faiss to perform Jaccard distance-based nearest neighbor analysis to construct the final dataset of 21k, which is multidimensional and sophisticated.
+
+웹에서 직접 수집한 금융 관련 PDF 데이터를 기반으로, 'meta-llama/Meta-Llama-3.1-70B-Instruct' 모델을 활용하여 원시 데이터를 정제하였습니다. 
+정제된 데이터를 바탕으로 합성 데이터를 생성한 후, 'meta-llama/Llama-Guard-3-8B' 및 'RLHFlow/ArmoRM-Llama3-8B-v0.1' 모델을 통해 생성된 데이터의 품질을 심층적으로 평가하였습니다. 
+이어서 'Alibaba-NLP/gte-large-en-v1.5'를 사용하여 임베딩을 추출하고, Faiss를 적용하여 자카드 거리 기반의 근접 이웃 분석을 수행함으로써 다차원적이고 정교한 최종 데이터셋 21k을 구성하였습니다.
+
+
+## sample
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619d8e31c21bf5feb310bd82/gJ6hnvAV2Qx9774AFFwQe.png)
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1
+
+