jokoban_-_my_distilpt2_ft-gguf – AI Model by RichardErkhov | AlphaNeural AI

You can deploy this model and start earning money today!

RichardErkhov/
jokoban_-_my_distilpt2_ft-gguf

like

0

endpoints_compatible

Views

No views yet

Quantization made by Richard Erkhov.

Request more models

my_distilpt2_ft - GGUF

Model creator: https://huggingface.co/jokoban/
Original model: https://huggingface.co/jokoban/my_distilpt2_ft/

Name	Quant method	Size
my_distilpt2_ft.Q2_K.gguf	Q2_K	0.06GB
my_distilpt2_ft.IQ3_XS.gguf	IQ3_XS	0.07GB
my_distilpt2_ft.IQ3_S.gguf	IQ3_S	0.07GB
my_distilpt2_ft.Q3_K_S.gguf	Q3_K_S	0.07GB
my_distilpt2_ft.IQ3_M.gguf	IQ3_M	0.07GB
my_distilpt2_ft.Q3_K.gguf	Q3_K	0.07GB
my_distilpt2_ft.Q3_K_M.gguf	Q3_K_M	0.07GB
my_distilpt2_ft.Q3_K_L.gguf	Q3_K_L	0.07GB
my_distilpt2_ft.IQ4_XS.gguf	IQ4_XS	0.07GB
my_distilpt2_ft.Q4_0.gguf	Q4_0	0.08GB
my_distilpt2_ft.IQ4_NL.gguf	IQ4_NL	0.08GB
my_distilpt2_ft.Q4_K_S.gguf	Q4_K_S	0.08GB
my_distilpt2_ft.Q4_K.gguf	Q4_K	0.08GB
my_distilpt2_ft.Q4_K_M.gguf	Q4_K_M	0.08GB
my_distilpt2_ft.Q4_1.gguf	Q4_1	0.08GB
my_distilpt2_ft.Q5_0.gguf	Q5_0	0.09GB
my_distilpt2_ft.Q5_K_S.gguf	Q5_K_S	0.09GB
my_distilpt2_ft.Q5_K.gguf	Q5_K	0.09GB
my_distilpt2_ft.Q5_K_M.gguf	Q5_K_M	0.09GB
my_distilpt2_ft.Q5_1.gguf	Q5_1	0.09GB
my_distilpt2_ft.Q6_K.gguf	Q6_K	0.1GB
my_distilpt2_ft.Q8_0.gguf	Q8_0	0.12GB

Original model description:

library_name: transformers license: apache-2.0 base_model: distilgpt2 tags:

generated_from_trainer metrics:
accuracy model-index:
name: my_distilpt2_ft results: []

my_distilpt2_ft

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0086
Accuracy: 0.9986

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: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Framework versions

Transformers 4.47.1
Pytorch 2.5.1
Datasets 3.2.0
Tokenizers 0.21.0