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Katayoon_-_VPO-SELM-Zephyr-7B-iter-1-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Katayoon_-_VPO-SELM-Zephyr-7B-iter-1-gguf
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Quantization made by Richard Erkhov.
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VPO-SELM-Zephyr-7B-iter-1 - GGUF
Model creator:
https://huggingface.co/Katayoon/
Original model:
https://huggingface.co/Katayoon/VPO-SELM-Zephyr-7B-iter-1/
Name
Quant method
Size
VPO-SELM-Zephyr-7B-iter-1.Q2_K.gguf
Q2_K
2.53GB
VPO-SELM-Zephyr-7B-iter-1.IQ3_XS.gguf
IQ3_XS
2.81GB
VPO-SELM-Zephyr-7B-iter-1.IQ3_S.gguf
IQ3_S
2.96GB
VPO-SELM-Zephyr-7B-iter-1.Q3_K_S.gguf
Q3_K_S
2.95GB
VPO-SELM-Zephyr-7B-iter-1.IQ3_M.gguf
IQ3_M
3.06GB
VPO-SELM-Zephyr-7B-iter-1.Q3_K.gguf
Q3_K
3.28GB
VPO-SELM-Zephyr-7B-iter-1.Q3_K_M.gguf
Q3_K_M
3.28GB
VPO-SELM-Zephyr-7B-iter-1.Q3_K_L.gguf
Q3_K_L
3.56GB
VPO-SELM-Zephyr-7B-iter-1.IQ4_XS.gguf
IQ4_XS
3.67GB
VPO-SELM-Zephyr-7B-iter-1.Q4_0.gguf
Q4_0
3.83GB
VPO-SELM-Zephyr-7B-iter-1.IQ4_NL.gguf
IQ4_NL
3.87GB
VPO-SELM-Zephyr-7B-iter-1.Q4_K_S.gguf
Q4_K_S
3.86GB
VPO-SELM-Zephyr-7B-iter-1.Q4_K.gguf
Q4_K
4.07GB
VPO-SELM-Zephyr-7B-iter-1.Q4_K_M.gguf
Q4_K_M
4.07GB
VPO-SELM-Zephyr-7B-iter-1.Q4_1.gguf
Q4_1
2.07GB
VPO-SELM-Zephyr-7B-iter-1.Q5_0.gguf
Q5_0
4.65GB
VPO-SELM-Zephyr-7B-iter-1.Q5_K_S.gguf
Q5_K_S
4.16GB
VPO-SELM-Zephyr-7B-iter-1.Q5_K.gguf
Q5_K
4.78GB
VPO-SELM-Zephyr-7B-iter-1.Q5_K_M.gguf
Q5_K_M
4.62GB
VPO-SELM-Zephyr-7B-iter-1.Q5_1.gguf
Q5_1
5.07GB
VPO-SELM-Zephyr-7B-iter-1.Q6_K.gguf
Q6_K
5.53GB
VPO-SELM-Zephyr-7B-iter-1.Q8_0.gguf
Q8_0
7.17GB
Original model description:
license: mit base_model: Katayoon/DPO-ONLY-Zephyr-7B tags:
alignment-handbook
trl
dpo
generated_from_trainer
trl
dpo
generated_from_trainer datasets:
updated
original model-index:
name: VPO-SELM-Zephyr-7B-iter-1 results: []
VPO-SELM-Zephyr-7B-iter-1
This model is a fine-tuned version of
Katayoon/DPO-ONLY-Zephyr-7B
on the updated and the original datasets.
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: 5e-07
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 16
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1
Training results
Framework versions
Transformers 4.40.2
Pytorch 2.1.2+cu121
Datasets 2.20.0
Tokenizers 0.19.1
