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VirgiF_-_continue_pretrain_gemma_more_pause-gguf – AI Model by RichardErkhov | AlphaNeural AI
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VirgiF_-_continue_pretrain_gemma_more_pause-gguf
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Quantization made by Richard Erkhov.
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continue_pretrain_gemma_more_pause - GGUF
Model creator:
https://huggingface.co/VirgiF/
Original model:
https://huggingface.co/VirgiF/continue_pretrain_gemma_more_pause/
Name
Quant method
Size
continue_pretrain_gemma_more_pause.Q2_K.gguf
Q2_K
1.08GB
continue_pretrain_gemma_more_pause.IQ3_XS.gguf
IQ3_XS
1.16GB
continue_pretrain_gemma_more_pause.IQ3_S.gguf
IQ3_S
1.2GB
continue_pretrain_gemma_more_pause.Q3_K_S.gguf
Q3_K_S
1.2GB
continue_pretrain_gemma_more_pause.IQ3_M.gguf
IQ3_M
1.22GB
continue_pretrain_gemma_more_pause.Q3_K.gguf
Q3_K
1.29GB
continue_pretrain_gemma_more_pause.Q3_K_M.gguf
Q3_K_M
1.29GB
continue_pretrain_gemma_more_pause.Q3_K_L.gguf
Q3_K_L
1.36GB
continue_pretrain_gemma_more_pause.IQ4_XS.gguf
IQ4_XS
1.4GB
continue_pretrain_gemma_more_pause.Q4_0.gguf
Q4_0
1.44GB
continue_pretrain_gemma_more_pause.IQ4_NL.gguf
IQ4_NL
1.45GB
continue_pretrain_gemma_more_pause.Q4_K_S.gguf
Q4_K_S
1.45GB
continue_pretrain_gemma_more_pause.Q4_K.gguf
Q4_K
1.52GB
continue_pretrain_gemma_more_pause.Q4_K_M.gguf
Q4_K_M
1.52GB
continue_pretrain_gemma_more_pause.Q4_1.gguf
Q4_1
1.56GB
continue_pretrain_gemma_more_pause.Q5_0.gguf
Q5_0
1.68GB
continue_pretrain_gemma_more_pause.Q5_K_S.gguf
Q5_K_S
1.68GB
continue_pretrain_gemma_more_pause.Q5_K.gguf
Q5_K
1.71GB
continue_pretrain_gemma_more_pause.Q5_K_M.gguf
Q5_K_M
1.71GB
continue_pretrain_gemma_more_pause.Q5_1.gguf
Q5_1
1.79GB
continue_pretrain_gemma_more_pause.Q6_K.gguf
Q6_K
1.92GB
continue_pretrain_gemma_more_pause.Q8_0.gguf
Q8_0
2.49GB
Original model description:
library_name: transformers license: gemma base_model: google/gemma-2b tags:
trl
sft
generated_from_trainer model-index:
name: continue_pretrain_gemma_more_pause results: []
continue_pretrain_gemma_more_pause
This model is a fine-tuned version of
google/gemma-2b
on the None 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: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
Training results
Framework versions
Transformers 4.44.2
Pytorch 2.1.2+cu121
Datasets 2.19.1
Tokenizers 0.19.1
