Muhammad7865253_-_fine_tuned_model-gguf – AI Model by RichardErkhov | AlphaNeural AI

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Muhammad7865253_-_fine_tuned_model-gguf

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fine_tuned_model - GGUF

Model creator: https://huggingface.co/Muhammad7865253/
Original model: https://huggingface.co/Muhammad7865253/fine_tuned_model/

Name	Quant method	Size
fine_tuned_model.Q2_K.gguf	Q2_K	0.08GB
fine_tuned_model.IQ3_XS.gguf	IQ3_XS	0.08GB
fine_tuned_model.IQ3_S.gguf	IQ3_S	0.08GB
fine_tuned_model.Q3_K_S.gguf	Q3_K_S	0.08GB
fine_tuned_model.IQ3_M.gguf	IQ3_M	0.09GB
fine_tuned_model.Q3_K.gguf	Q3_K	0.09GB
fine_tuned_model.Q3_K_M.gguf	Q3_K_M	0.09GB
fine_tuned_model.Q3_K_L.gguf	Q3_K_L	0.1GB
fine_tuned_model.IQ4_XS.gguf	IQ4_XS	0.1GB
fine_tuned_model.Q4_0.gguf	Q4_0	0.1GB
fine_tuned_model.IQ4_NL.gguf	IQ4_NL	0.1GB
fine_tuned_model.Q4_K_S.gguf	Q4_K_S	0.1GB
fine_tuned_model.Q4_K.gguf	Q4_K	0.11GB
fine_tuned_model.Q4_K_M.gguf	Q4_K_M	0.11GB
fine_tuned_model.Q4_1.gguf	Q4_1	0.11GB
fine_tuned_model.Q5_0.gguf	Q5_0	0.11GB
fine_tuned_model.Q5_K_S.gguf	Q5_K_S	0.11GB
fine_tuned_model.Q5_K.gguf	Q5_K	0.12GB
fine_tuned_model.Q5_K_M.gguf	Q5_K_M	0.12GB
fine_tuned_model.Q5_1.gguf	Q5_1	0.12GB
fine_tuned_model.Q6_K.gguf	Q6_K	0.13GB
fine_tuned_model.Q8_0.gguf	Q8_0	0.17GB

Original model description:

library_name: transformers license: mit base_model: microsoft/DialoGPT-small tags:

generated_from_trainer model-index:
name: fine_tuned_model results: []

fine_tuned_model

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

Loss: 4.3886

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: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use 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
mixed_precision_training: Native AMP

Training results

Name	Quant method	Size
fine_tuned_model.Q2_K.gguf	Q2_K	0.08GB
fine_tuned_model.IQ3_XS.gguf	IQ3_XS	0.08GB
fine_tuned_model.IQ3_S.gguf	IQ3_S	0.08GB
fine_tuned_model.Q3_K_S.gguf	Q3_K_S	0.08GB
fine_tuned_model.IQ3_M.gguf	IQ3_M	0.09GB
fine_tuned_model.Q3_K.gguf	Q3_K	0.09GB
fine_tuned_model.Q3_K_M.gguf	Q3_K_M	0.09GB
fine_tuned_model.Q3_K_L.gguf	Q3_K_L	0.1GB
fine_tuned_model.IQ4_XS.gguf	IQ4_XS	0.1GB
fine_tuned_model.Q4_0.gguf	Q4_0	0.1GB
fine_tuned_model.IQ4_NL.gguf	IQ4_NL	0.1GB
fine_tuned_model.Q4_K_S.gguf	Q4_K_S	0.1GB
fine_tuned_model.Q4_K.gguf	Q4_K	0.11GB
fine_tuned_model.Q4_K_M.gguf	Q4_K_M	0.11GB
fine_tuned_model.Q4_1.gguf	Q4_1	0.11GB
fine_tuned_model.Q5_0.gguf	Q5_0	0.11GB
fine_tuned_model.Q5_K_S.gguf	Q5_K_S	0.11GB
fine_tuned_model.Q5_K.gguf	Q5_K	0.12GB
fine_tuned_model.Q5_K_M.gguf	Q5_K_M	0.12GB
fine_tuned_model.Q5_1.gguf	Q5_1	0.12GB
fine_tuned_model.Q6_K.gguf	Q6_K	0.13GB
fine_tuned_model.Q8_0.gguf	Q8_0	0.17GB

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3