RichardErkhov/shleeeee_-_mistral-ko-7b-tech-gguf

Quantization made by Richard Erkhov.

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mistral-ko-7b-tech - GGUF

• Model creator: https://huggingface.co/shleeeee/
• Original model: https://huggingface.co/shleeeee/mistral-ko-7b-tech/

Name	Quant method	Size
mistral-ko-7b-tech.Q2_K.gguf	Q2_K	2.53GB
mistral-ko-7b-tech.IQ3_XS.gguf	IQ3_XS	2.81GB
mistral-ko-7b-tech.IQ3_S.gguf	IQ3_S	2.96GB
mistral-ko-7b-tech.Q3_K_S.gguf	Q3_K_S	2.95GB
mistral-ko-7b-tech.IQ3_M.gguf	IQ3_M	3.06GB
mistral-ko-7b-tech.Q3_K.gguf	Q3_K	3.28GB
mistral-ko-7b-tech.Q3_K_M.gguf	Q3_K_M	3.28GB
mistral-ko-7b-tech.Q3_K_L.gguf	Q3_K_L	3.56GB
mistral-ko-7b-tech.IQ4_XS.gguf	IQ4_XS	3.67GB
mistral-ko-7b-tech.Q4_0.gguf	Q4_0	3.83GB
mistral-ko-7b-tech.IQ4_NL.gguf	IQ4_NL	3.87GB
mistral-ko-7b-tech.Q4_K_S.gguf	Q4_K_S	3.86GB
mistral-ko-7b-tech.Q4_K.gguf	Q4_K	4.07GB
mistral-ko-7b-tech.Q4_K_M.gguf	Q4_K_M	4.07GB
mistral-ko-7b-tech.Q4_1.gguf	Q4_1	4.24GB
mistral-ko-7b-tech.Q5_0.gguf	Q5_0	4.65GB
mistral-ko-7b-tech.Q5_K_S.gguf	Q5_K_S	4.65GB
mistral-ko-7b-tech.Q5_K.gguf	Q5_K	4.78GB
mistral-ko-7b-tech.Q5_K_M.gguf	Q5_K_M	4.78GB
mistral-ko-7b-tech.Q5_1.gguf	Q5_1	5.07GB
mistral-ko-7b-tech.Q6_K.gguf	Q6_K	5.53GB
mistral-ko-7b-tech.Q8_0.gguf	Q8_0	7.17GB

Original model description:

language: - ko pipeline_tag: text-generation tags: - finetune license: other

Model Card for mistral-ko-7b-tech

It is a fine-tuned model using Korean in the mistral-7b model.

Model Details

• Model Developers : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park)
• Repository : To be added
• Model Architecture : The mistral-ko-7b-wiki-neft is is a fine-tuned version of the Mistral-7B-v0.1.
• Lora target modules : q_proj, k_proj, v_proj, o_proj,gate_proj
• train_batch : 4
• Max_step : 500

Dataset

Korean Custom Dataset(2000)

Prompt template: Mistral

<s>[INST]{['instruction']}[/INST]{['output']}</s>

Usage

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-ko-7b-tech")
model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-ko-7b-tech")

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shleeeee/mistral-ko-7b-tech")

Evaluation