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DooDooHyun_-_AIFT-Yi-Ko-6B-ao-instruct-all-v0.54-gguf – AI Model by RichardErkhov | AlphaNeural AI
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DooDooHyun_-_AIFT-Yi-Ko-6B-ao-instruct-all-v0.54-gguf
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AIFT-Yi-Ko-6B-ao-instruct-all-v0.54 - GGUF
Model creator:
https://huggingface.co/DooDooHyun/
Original model:
https://huggingface.co/DooDooHyun/AIFT-Yi-Ko-6B-ao-instruct-all-v0.54/
Name
Quant method
Size
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q2_K.gguf
Q2_K
2.24GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.IQ3_XS.gguf
IQ3_XS
2.48GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.IQ3_S.gguf
IQ3_S
2.6GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q3_K_S.gguf
Q3_K_S
2.59GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.IQ3_M.gguf
IQ3_M
2.69GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q3_K.gguf
Q3_K
2.86GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q3_K_M.gguf
Q3_K_M
2.86GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q3_K_L.gguf
Q3_K_L
3.08GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.IQ4_XS.gguf
IQ4_XS
3.18GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q4_0.gguf
Q4_0
3.32GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.IQ4_NL.gguf
IQ4_NL
3.35GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q4_K_S.gguf
Q4_K_S
3.34GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q4_K.gguf
Q4_K
3.5GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q4_K_M.gguf
Q4_K_M
3.5GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q4_1.gguf
Q4_1
3.66GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q5_0.gguf
Q5_0
4.0GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q5_K_S.gguf
Q5_K_S
4.0GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q5_K.gguf
Q5_K
4.09GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q5_K_M.gguf
Q5_K_M
4.09GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q5_1.gguf
Q5_1
4.34GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q6_K.gguf
Q6_K
4.72GB
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54.Q8_0.gguf
Q8_0
6.12GB
Original model description:
license: other base_model: beomi/Yi-Ko-6B tags:
generated_from_trainer model-index:
name: AIFT-Yi-Ko-6B-ao-instruct-all-v0.54 results: []
AIFT-Yi-Ko-6B-ao-instruct-all-v0.54
This model is a fine-tuned version of
beomi/Yi-Ko-6B
on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.0.0
Tokenizers 0.15.0
