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RichardErkhov/
4yo1_-_llama3-pre1-pre2-ds-ins2-lora3-gguf

Name: 4yo1_-_llama3-pre1-pre2-ds-ins2-lora3-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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llama3-pre1-pre2-ds-ins2-lora3 - GGUF

Model creator: https://huggingface.co/4yo1/
Original model: https://huggingface.co/4yo1/llama3-pre1-pre2-ds-ins2-lora3/

Name	Quant method	Size
llama3-pre1-pre2-ds-ins2-lora3.Q2_K.gguf	Q2_K	3.05GB
llama3-pre1-pre2-ds-ins2-lora3.IQ3_XS.gguf	IQ3_XS	0.87GB
llama3-pre1-pre2-ds-ins2-lora3.IQ3_S.gguf	IQ3_S	0.01GB
llama3-pre1-pre2-ds-ins2-lora3.Q3_K_S.gguf	Q3_K_S	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.IQ3_M.gguf	IQ3_M	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q3_K.gguf	Q3_K	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q3_K_M.gguf	Q3_K_M	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q3_K_L.gguf	Q3_K_L	4.13GB
llama3-pre1-pre2-ds-ins2-lora3.IQ4_XS.gguf	IQ4_XS	0.01GB
llama3-pre1-pre2-ds-ins2-lora3.Q4_0.gguf	Q4_0	0.01GB
llama3-pre1-pre2-ds-ins2-lora3.IQ4_NL.gguf	IQ4_NL	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q4_K_S.gguf	Q4_K_S	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q4_K.gguf	Q4_K	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q4_K_M.gguf	Q4_K_M	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q4_1.gguf	Q4_1	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q5_0.gguf	Q5_0	0.13GB
llama3-pre1-pre2-ds-ins2-lora3.Q5_K_S.gguf	Q5_K_S	0.19GB
llama3-pre1-pre2-ds-ins2-lora3.Q5_K.gguf	Q5_K	0.01GB
llama3-pre1-pre2-ds-ins2-lora3.Q5_K_M.gguf	Q5_K_M	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q5_1.gguf	Q5_1	0.01GB
llama3-pre1-pre2-ds-ins2-lora3.Q6_K.gguf	Q6_K	0.0GB
llama3-pre1-pre2-ds-ins2-lora3.Q8_0.gguf	Q8_0	0.0GB

Original model description:

library_name: transformers language:

en
ko pipeline_tag: translation license: mit datasets:
pre

Model Card for Model ID

Model Details

Model Card: sapie with Fine-Tuning Model Overview Model Name: 4yo1/llama3-pre1-pre2-ds-ins2-lora3

Model Type: Transformer-based Language Model

Model Size: 8 billion parameters

by: 4yo1

Languages: English and Korean

how to use - sample code

python

from transformers import AutoConfig, AutoModel, AutoTokenizer

config = AutoConfig.from_pretrained("4yo1/llama3-pre1-pre2-ds-ins2-lora3")
model = AutoModel.from_pretrained("4yo1/llama3-pre1-pre2-ds-ins2-lora3")
tokenizer = AutoTokenizer.from_pretrained("4yo1/llama3-pre1-pre2-ds-ins2-lora3")

datasets:

license: mit

RichardErkhov/4yo1_-_llama3-pre1-pre2-ds-ins2-lora3-gguf

Original model description:

Model Card for Model ID

Model Details

how to use - sample code

RichardErkhov/
4yo1_-_llama3-pre1-pre2-ds-ins2-lora3-gguf