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RichardErkhov/
4yo1_-_llama_1-gguf

Name: 4yo1_-_llama_1-gguf
Availability: InStock

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llama_1 - GGUF

Model creator: https://huggingface.co/4yo1/
Original model: https://huggingface.co/4yo1/llama_1/

Name	Quant method	Size
llama_1.Q2_K.gguf	Q2_K	2.96GB
llama_1.Q3_K_S.gguf	Q3_K_S	3.41GB
llama_1.Q3_K.gguf	Q3_K	3.74GB
llama_1.Q3_K_M.gguf	Q3_K_M	3.74GB
llama_1.Q3_K_L.gguf	Q3_K_L	4.03GB
llama_1.IQ4_XS.gguf	IQ4_XS	4.18GB
llama_1.Q4_0.gguf	Q4_0	4.34GB
llama_1.IQ4_NL.gguf	IQ4_NL	4.38GB
llama_1.Q4_K_S.gguf	Q4_K_S	4.37GB
llama_1.Q4_K.gguf	Q4_K	4.58GB
llama_1.Q4_K_M.gguf	Q4_K_M	4.58GB
llama_1.Q4_1.gguf	Q4_1	4.78GB
llama_1.Q5_0.gguf	Q5_0	5.21GB
llama_1.Q5_K_S.gguf	Q5_K_S	5.21GB
llama_1.Q5_K.gguf	Q5_K	5.34GB
llama_1.Q5_K_M.gguf	Q5_K_M	5.34GB
llama_1.Q5_1.gguf	Q5_1	5.65GB
llama_1.Q6_K.gguf	Q6_K	6.14GB
llama_1.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers language:

en
ko pipeline_tag: translation tags:
llama-3-ko license: mit datasets:
4yo1/llama3_test1

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Model Details

Model Type: Transformer-based Language Model

Languages: English and Korean

Model Description

This fine-tuning approach allows the model to adapt to specific tasks or datasets with a minimal number of additional parameters, making it efficient and effective for specialized applications.

how to use - sample code

python

from transformers import AutoConfig, AutoModel, AutoTokenizer

config = AutoConfig.from_pretrained("4yo1/llama")
model = AutoModel.from_pretrained("4yo1/llama")
tokenizer = AutoTokenizer.from_pretrained("4yo1/llama")

datasets:

4yo1/llama3_test1

license: mit

RichardErkhov/4yo1_-_llama_1-gguf