Mikivis_-_gpt2-large-lora-sft1-gguf – AI Model by RichardErkhov | AlphaNeural AI

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Mikivis_-_gpt2-large-lora-sft1-gguf

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Quantization made by Richard Erkhov.

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gpt2-large-lora-sft1 - GGUF

Model creator: https://huggingface.co/Mikivis/
Original model: https://huggingface.co/Mikivis/gpt2-large-lora-sft1/

Name	Quant method	Size
gpt2-large-lora-sft1.Q2_K.gguf	Q2_K	0.32GB
gpt2-large-lora-sft1.IQ3_XS.gguf	IQ3_XS	0.36GB
gpt2-large-lora-sft1.IQ3_S.gguf	IQ3_S	0.37GB
gpt2-large-lora-sft1.Q3_K_S.gguf	Q3_K_S	0.37GB
gpt2-large-lora-sft1.IQ3_M.gguf	IQ3_M	0.4GB
gpt2-large-lora-sft1.Q3_K.gguf	Q3_K	0.43GB
gpt2-large-lora-sft1.Q3_K_M.gguf	Q3_K_M	0.43GB
gpt2-large-lora-sft1.Q3_K_L.gguf	Q3_K_L	0.46GB
gpt2-large-lora-sft1.IQ4_XS.gguf	IQ4_XS	0.44GB
gpt2-large-lora-sft1.Q4_0.gguf	Q4_0	0.46GB
gpt2-large-lora-sft1.IQ4_NL.gguf	IQ4_NL	0.47GB
gpt2-large-lora-sft1.Q4_K_S.gguf	Q4_K_S	0.47GB
gpt2-large-lora-sft1.Q4_K.gguf	Q4_K	0.51GB
gpt2-large-lora-sft1.Q4_K_M.gguf	Q4_K_M	0.51GB
gpt2-large-lora-sft1.Q4_1.gguf	Q4_1	0.51GB
gpt2-large-lora-sft1.Q5_0.gguf	Q5_0	0.55GB
gpt2-large-lora-sft1.Q5_K_S.gguf	Q5_K_S	0.55GB
gpt2-large-lora-sft1.Q5_K.gguf	Q5_K	0.59GB
gpt2-large-lora-sft1.Q5_K_M.gguf	Q5_K_M	0.59GB
gpt2-large-lora-sft1.Q5_1.gguf	Q5_1	0.6GB
gpt2-large-lora-sft1.Q6_K.gguf	Q6_K	0.65GB
gpt2-large-lora-sft1.Q8_0.gguf	Q8_0	0.84GB

Original model description:

license: apache-2.0

gpt2-large-lora-sft1

This model is a fine-tuned version of /data1/sunhaoyu/pretrained-models/gpt2-large-lora-sft on the customized 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: 0.00015
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 6
total_train_batch_size: 6
total_eval_batch_size: 48
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.5

Training results

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu117
Datasets 2.10.1
Tokenizers 0.13.3