joshcarp_-_gpt2-evy-gguf – AI Model by RichardErkhov | AlphaNeural AI

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joshcarp_-_gpt2-evy-gguf

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gpt2-evy - GGUF

Model creator: https://huggingface.co/joshcarp/
Original model: https://huggingface.co/joshcarp/gpt2-evy/

Name	Quant method	Size
gpt2-evy.Q2_K.gguf	Q2_K	0.08GB
gpt2-evy.Q3_K_S.gguf	Q3_K_S	0.08GB
gpt2-evy.Q3_K.gguf	Q3_K	0.09GB
gpt2-evy.Q3_K_M.gguf	Q3_K_M	0.09GB
gpt2-evy.Q3_K_L.gguf	Q3_K_L	0.1GB
gpt2-evy.IQ4_XS.gguf	IQ4_XS	0.1GB
gpt2-evy.Q4_0.gguf	Q4_0	0.1GB
gpt2-evy.IQ4_NL.gguf	IQ4_NL	0.1GB
gpt2-evy.Q4_K_S.gguf	Q4_K_S	0.1GB
gpt2-evy.Q4_K.gguf	Q4_K	0.11GB
gpt2-evy.Q4_K_M.gguf	Q4_K_M	0.11GB
gpt2-evy.Q4_1.gguf	Q4_1	0.11GB
gpt2-evy.Q5_0.gguf	Q5_0	0.11GB
gpt2-evy.Q5_K_S.gguf	Q5_K_S	0.11GB
gpt2-evy.Q5_K.gguf	Q5_K	0.12GB
gpt2-evy.Q5_K_M.gguf	Q5_K_M	0.12GB
gpt2-evy.Q5_1.gguf	Q5_1	0.12GB
gpt2-evy.Q6_K.gguf	Q6_K	0.13GB
gpt2-evy.Q8_0.gguf	Q8_0	0.17GB

Original model description:

license: mit tags:

generated_from_trainer base_model: gpt2 model-index:
name: gpt2-evy results: []

gpt2-evy

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5637

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-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	31	1.1270
No log	2.0	62	0.9170
No log	3.0	93	0.8410
1.3727	4.0	124	0.7949
1.3727	5.0	155	0.7361
1.3727	6.0	186	0.7021
0.9077	7.0	217	0.6789
0.9077	8.0	248	0.6490
0.9077	9.0	279	0.6346
0.7106	10.0	310	0.6219
0.7106	11.0	341	0.5986
0.7106	12.0	372	0.5797
0.5814	13.0	403	0.5835
0.5814	14.0	434	0.5872
0.5814	15.0	465	0.5741
0.5814	16.0	496	0.5749
0.4916	17.0	527	0.5662
0.4916	18.0	558	0.5529
0.4916	19.0	589	0.5672
0.4005	20.0	620	0.5646
0.4005	21.0	651	0.5701
0.4005	22.0	682	0.5650
0.357	23.0	713	0.5637

Framework versions

Transformers 4.41.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1