chicagoillinoiswork_-_gpt2-qa-2024-12-11-gguf – AI Model by RichardErkhov | AlphaNeural AI

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Quantization made by Richard Erkhov.

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gpt2-qa-2024-12-11 - GGUF

Model creator: https://huggingface.co/chicagoillinoiswork/
Original model: https://huggingface.co/chicagoillinoiswork/gpt2-qa-2024-12-11/

Name	Quant method	Size
gpt2-qa-2024-12-11.Q2_K.gguf	Q2_K	0.08GB
gpt2-qa-2024-12-11.IQ3_XS.gguf	IQ3_XS	0.08GB
gpt2-qa-2024-12-11.IQ3_S.gguf	IQ3_S	0.08GB
gpt2-qa-2024-12-11.Q3_K_S.gguf	Q3_K_S	0.08GB
gpt2-qa-2024-12-11.IQ3_M.gguf	IQ3_M	0.09GB
gpt2-qa-2024-12-11.Q3_K.gguf	Q3_K	0.09GB
gpt2-qa-2024-12-11.Q3_K_M.gguf	Q3_K_M	0.09GB
gpt2-qa-2024-12-11.Q3_K_L.gguf	Q3_K_L	0.1GB
gpt2-qa-2024-12-11.IQ4_XS.gguf	IQ4_XS	0.1GB
gpt2-qa-2024-12-11.Q4_0.gguf	Q4_0	0.1GB
gpt2-qa-2024-12-11.IQ4_NL.gguf	IQ4_NL	0.1GB
gpt2-qa-2024-12-11.Q4_K_S.gguf	Q4_K_S	0.1GB
gpt2-qa-2024-12-11.Q4_K.gguf	Q4_K	0.11GB
gpt2-qa-2024-12-11.Q4_K_M.gguf	Q4_K_M	0.11GB
gpt2-qa-2024-12-11.Q4_1.gguf	Q4_1	0.11GB
gpt2-qa-2024-12-11.Q5_0.gguf	Q5_0	0.11GB
gpt2-qa-2024-12-11.Q5_K_S.gguf	Q5_K_S	0.11GB
gpt2-qa-2024-12-11.Q5_K.gguf	Q5_K	0.12GB
gpt2-qa-2024-12-11.Q5_K_M.gguf	Q5_K_M	0.12GB
gpt2-qa-2024-12-11.Q5_1.gguf	Q5_1	0.12GB
gpt2-qa-2024-12-11.Q6_K.gguf	Q6_K	0.13GB
gpt2-qa-2024-12-11.Q8_0.gguf	Q8_0	0.17GB

Original model description:

library_name: transformers license: mit base_model: gpt2 tags:

generated_from_trainer model-index:
name: gpt2-qa-2024-12-11 results: []

gpt2-qa-2024-12-11

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

Loss: 0.1989

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: 8
eval_batch_size: 8
seed: 42
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

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	63	0.2012
No log	2.0	126	0.1969
No log	3.0	189	0.1989

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3