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kimhyeongjun_-_llama-3-8b-patent-small-dataset-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Quantization made by Richard Erkhov.
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llama-3-8b-patent-small-dataset - GGUF
Model creator:
https://huggingface.co/kimhyeongjun/
Original model:
https://huggingface.co/kimhyeongjun/llama-3-8b-patent-small-dataset/
Name
Quant method
Size
llama-3-8b-patent-small-dataset.Q2_K.gguf
Q2_K
2.96GB
llama-3-8b-patent-small-dataset.IQ3_XS.gguf
IQ3_XS
3.28GB
llama-3-8b-patent-small-dataset.IQ3_S.gguf
IQ3_S
3.43GB
llama-3-8b-patent-small-dataset.Q3_K_S.gguf
Q3_K_S
3.41GB
llama-3-8b-patent-small-dataset.IQ3_M.gguf
IQ3_M
3.52GB
llama-3-8b-patent-small-dataset.Q3_K.gguf
Q3_K
3.74GB
llama-3-8b-patent-small-dataset.Q3_K_M.gguf
Q3_K_M
3.74GB
llama-3-8b-patent-small-dataset.Q3_K_L.gguf
Q3_K_L
4.03GB
llama-3-8b-patent-small-dataset.IQ4_XS.gguf
IQ4_XS
4.18GB
llama-3-8b-patent-small-dataset.Q4_0.gguf
Q4_0
4.34GB
llama-3-8b-patent-small-dataset.IQ4_NL.gguf
IQ4_NL
4.38GB
llama-3-8b-patent-small-dataset.Q4_K_S.gguf
Q4_K_S
4.37GB
llama-3-8b-patent-small-dataset.Q4_K.gguf
Q4_K
4.58GB
llama-3-8b-patent-small-dataset.Q4_K_M.gguf
Q4_K_M
4.58GB
llama-3-8b-patent-small-dataset.Q4_1.gguf
Q4_1
4.78GB
llama-3-8b-patent-small-dataset.Q5_0.gguf
Q5_0
5.21GB
llama-3-8b-patent-small-dataset.Q5_K_S.gguf
Q5_K_S
5.21GB
llama-3-8b-patent-small-dataset.Q5_K.gguf
Q5_K
5.34GB
llama-3-8b-patent-small-dataset.Q5_K_M.gguf
Q5_K_M
5.34GB
llama-3-8b-patent-small-dataset.Q5_1.gguf
Q5_1
5.65GB
llama-3-8b-patent-small-dataset.Q6_K.gguf
Q6_K
6.14GB
llama-3-8b-patent-small-dataset.Q8_0.gguf
Q8_0
7.95GB
Original model description:
license: other base_model: meta-llama/Meta-Llama-3-8B-Instruct tags:
llama-factory
full
generated_from_trainer model-index:
name: llama-3-8b-chat-patent-small results: [] language:
en
llama-3-8b-chat-patent-small
This model is a fine-tuned version of
meta-llama/Meta-Llama-3-8B-Instruct
on the english translation of a small dataset of 16,000 Korean patents.
Model description
This model is provided for testing purposes only.
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 3.0
mixed_precision_training: Native AMP
Framework versions
Transformers 4.39.3
Pytorch 2.1.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2
