Katayoon_-_SELM-Zephyr-7B-iter-2-gguf – AI Model by RichardErkhov | AlphaNeural AI

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Katayoon_-_SELM-Zephyr-7B-iter-2-gguf

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SELM-Zephyr-7B-iter-2 - GGUF

Model creator: https://huggingface.co/Katayoon/
Original model: https://huggingface.co/Katayoon/SELM-Zephyr-7B-iter-2/

Name	Quant method	Size
SELM-Zephyr-7B-iter-2.Q2_K.gguf	Q2_K	2.53GB
SELM-Zephyr-7B-iter-2.IQ3_XS.gguf	IQ3_XS	2.81GB
SELM-Zephyr-7B-iter-2.IQ3_S.gguf	IQ3_S	2.96GB
SELM-Zephyr-7B-iter-2.Q3_K_S.gguf	Q3_K_S	2.95GB
SELM-Zephyr-7B-iter-2.IQ3_M.gguf	IQ3_M	3.06GB
SELM-Zephyr-7B-iter-2.Q3_K.gguf	Q3_K	3.28GB
SELM-Zephyr-7B-iter-2.Q3_K_M.gguf	Q3_K_M	3.28GB
SELM-Zephyr-7B-iter-2.Q3_K_L.gguf	Q3_K_L	3.56GB
SELM-Zephyr-7B-iter-2.IQ4_XS.gguf	IQ4_XS	3.67GB
SELM-Zephyr-7B-iter-2.Q4_0.gguf	Q4_0	3.83GB
SELM-Zephyr-7B-iter-2.IQ4_NL.gguf	IQ4_NL	3.87GB
SELM-Zephyr-7B-iter-2.Q4_K_S.gguf	Q4_K_S	3.86GB
SELM-Zephyr-7B-iter-2.Q4_K.gguf	Q4_K	4.07GB
SELM-Zephyr-7B-iter-2.Q4_K_M.gguf	Q4_K_M	4.07GB
SELM-Zephyr-7B-iter-2.Q4_1.gguf	Q4_1	4.24GB
SELM-Zephyr-7B-iter-2.Q5_0.gguf	Q5_0	4.65GB
SELM-Zephyr-7B-iter-2.Q5_K_S.gguf	Q5_K_S	4.65GB
SELM-Zephyr-7B-iter-2.Q5_K.gguf	Q5_K	4.78GB
SELM-Zephyr-7B-iter-2.Q5_K_M.gguf	Q5_K_M	4.78GB
SELM-Zephyr-7B-iter-2.Q5_1.gguf	Q5_1	5.07GB
SELM-Zephyr-7B-iter-2.Q6_K.gguf	Q6_K	5.53GB
SELM-Zephyr-7B-iter-2.Q8_0.gguf	Q8_0	6.63GB

Original model description:

license: mit base_model: Katayoon/SELM-Zephyr-7B-iter-1 tags:

alignment-handbook
trl
dpo
generated_from_trainer
trl
dpo
generated_from_trainer datasets:
updated
original model-index:
name: SELM-Zephyr-7B-iter-2 results: []

SELM-Zephyr-7B-iter-2

This model is a fine-tuned version of Katayoon/SELM-Zephyr-7B-iter-1 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 2.14.6
Tokenizers 0.19.1