chaimachabir_-_gpt2-without-lora-gguf – AI Model by RichardErkhov | AlphaNeural AI

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chaimachabir_-_gpt2-without-lora-gguf

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gpt2-without-lora - GGUF

Model creator: https://huggingface.co/chaimachabir/
Original model: https://huggingface.co/chaimachabir/gpt2-without-lora/

Name	Quant method	Size
gpt2-without-lora.Q2_K.gguf	Q2_K	0.08GB
gpt2-without-lora.IQ3_XS.gguf	IQ3_XS	0.08GB
gpt2-without-lora.IQ3_S.gguf	IQ3_S	0.08GB
gpt2-without-lora.Q3_K_S.gguf	Q3_K_S	0.08GB
gpt2-without-lora.IQ3_M.gguf	IQ3_M	0.09GB
gpt2-without-lora.Q3_K.gguf	Q3_K	0.09GB
gpt2-without-lora.Q3_K_M.gguf	Q3_K_M	0.09GB
gpt2-without-lora.Q3_K_L.gguf	Q3_K_L	0.1GB
gpt2-without-lora.IQ4_XS.gguf	IQ4_XS	0.1GB
gpt2-without-lora.Q4_0.gguf	Q4_0	0.1GB
gpt2-without-lora.IQ4_NL.gguf	IQ4_NL	0.1GB
gpt2-without-lora.Q4_K_S.gguf	Q4_K_S	0.1GB
gpt2-without-lora.Q4_K.gguf	Q4_K	0.11GB
gpt2-without-lora.Q4_K_M.gguf	Q4_K_M	0.11GB
gpt2-without-lora.Q4_1.gguf	Q4_1	0.11GB
gpt2-without-lora.Q5_0.gguf	Q5_0	0.11GB
gpt2-without-lora.Q5_K_S.gguf	Q5_K_S	0.11GB
gpt2-without-lora.Q5_K.gguf	Q5_K	0.12GB
gpt2-without-lora.Q5_K_M.gguf	Q5_K_M	0.12GB
gpt2-without-lora.Q5_1.gguf	Q5_1	0.12GB
gpt2-without-lora.Q6_K.gguf	Q6_K	0.13GB
gpt2-without-lora.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-without-lora results: []

gpt2-without-lora

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

Loss: 3.2524

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: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.4125	1.0	818	3.3266
3.2592	2.0	1636	3.2739
3.0787	3.0	2454	3.2524

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1