RichardErkhov/Changgil_-_K2S3-SOLAR-11b-v4.0-gguf

Quantization made by Richard Erkhov.

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K2S3-SOLAR-11b-v4.0 - GGUF

• Model creator: https://huggingface.co/Changgil/
• Original model: https://huggingface.co/Changgil/K2S3-SOLAR-11b-v4.0/

Name	Quant method	Size
K2S3-SOLAR-11b-v4.0.Q2_K.gguf	Q2_K	3.73GB
K2S3-SOLAR-11b-v4.0.IQ3_XS.gguf	IQ3_XS	4.14GB
K2S3-SOLAR-11b-v4.0.IQ3_S.gguf	IQ3_S	4.37GB
K2S3-SOLAR-11b-v4.0.Q3_K_S.gguf	Q3_K_S	4.34GB
K2S3-SOLAR-11b-v4.0.IQ3_M.gguf	IQ3_M	4.51GB
K2S3-SOLAR-11b-v4.0.Q3_K.gguf	Q3_K	4.84GB
K2S3-SOLAR-11b-v4.0.Q3_K_M.gguf	Q3_K_M	4.84GB
K2S3-SOLAR-11b-v4.0.Q3_K_L.gguf	Q3_K_L	5.26GB
K2S3-SOLAR-11b-v4.0.IQ4_XS.gguf	IQ4_XS	5.43GB
K2S3-SOLAR-11b-v4.0.Q4_0.gguf	Q4_0	5.66GB
K2S3-SOLAR-11b-v4.0.IQ4_NL.gguf	IQ4_NL	5.72GB
K2S3-SOLAR-11b-v4.0.Q4_K_S.gguf	Q4_K_S	5.7GB
K2S3-SOLAR-11b-v4.0.Q4_K.gguf	Q4_K	6.02GB
K2S3-SOLAR-11b-v4.0.Q4_K_M.gguf	Q4_K_M	6.02GB
K2S3-SOLAR-11b-v4.0.Q4_1.gguf	Q4_1	6.27GB
K2S3-SOLAR-11b-v4.0.Q5_0.gguf	Q5_0	6.89GB
K2S3-SOLAR-11b-v4.0.Q5_K_S.gguf	Q5_K_S	6.89GB
K2S3-SOLAR-11b-v4.0.Q5_K.gguf	Q5_K	7.08GB
K2S3-SOLAR-11b-v4.0.Q5_K_M.gguf	Q5_K_M	7.08GB
K2S3-SOLAR-11b-v4.0.Q5_1.gguf	Q5_1	7.51GB
K2S3-SOLAR-11b-v4.0.Q6_K.gguf	Q6_K	8.2GB
K2S3-SOLAR-11b-v4.0.Q8_0.gguf	Q8_0	10.62GB

Original model description:

license: cc-by-nc-4.0 language: - ko

---

Developed by :

• K2S3

Model Number:

• K2S3-SOLAR-11b-v4.0

Base Model :

• upstage/SOLAR-10.7B-v1.0

Training Data

• The training data for this model includes the Standard Korean Dictionary, training data from KULLM at Korea University, abstracts of master's and doctoral theses, Korean language samples from AI Hub, alpaca-gpt4-data, and samples from The OpenOrca Dataset.
• 이 모델의 훈련 데이터에는 표준국어대사전, 고려대학교 KULLM에서 제공한 훈련 데이터, 석사 및 박사학위 논문의 초록, AI Hub에서 제공한 한국어 데이터 샘플, alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다.

Training Method

• This model was fine-tuned on the "upstage/SOLAR-10.7B-v1.0" base model using a full parameter tuning method with SFT (Supervised Fine-Tuning).
• 이 모델은 "upstage/SOLAR-10.7B-v1.0" 기반 모델을 SFT를 사용하여 전체 파라미터 조정 방법으로 미세조정되었습니다.

Hardware

• Hardware: Utilized two A100 (80G*2EA) GPUs for training.
• Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp.
• 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다.