slava-medvedev_-_zelensky-gpt2-125m-gguf – AI Model by RichardErkhov | AlphaNeural AI

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slava-medvedev_-_zelensky-gpt2-125m-gguf

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Quantization made by Richard Erkhov.

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zelensky-gpt2-125m - GGUF

Model creator: https://huggingface.co/slava-medvedev/
Original model: https://huggingface.co/slava-medvedev/zelensky-gpt2-125m/

Name	Quant method	Size
zelensky-gpt2-125m.Q2_K.gguf	Q2_K	0.08GB
zelensky-gpt2-125m.IQ3_XS.gguf	IQ3_XS	0.08GB
zelensky-gpt2-125m.IQ3_S.gguf	IQ3_S	0.08GB
zelensky-gpt2-125m.Q3_K_S.gguf	Q3_K_S	0.08GB
zelensky-gpt2-125m.IQ3_M.gguf	IQ3_M	0.09GB
zelensky-gpt2-125m.Q3_K.gguf	Q3_K	0.09GB
zelensky-gpt2-125m.Q3_K_M.gguf	Q3_K_M	0.09GB
zelensky-gpt2-125m.Q3_K_L.gguf	Q3_K_L	0.1GB
zelensky-gpt2-125m.IQ4_XS.gguf	IQ4_XS	0.1GB
zelensky-gpt2-125m.Q4_0.gguf	Q4_0	0.1GB
zelensky-gpt2-125m.IQ4_NL.gguf	IQ4_NL	0.1GB
zelensky-gpt2-125m.Q4_K_S.gguf	Q4_K_S	0.1GB
zelensky-gpt2-125m.Q4_K.gguf	Q4_K	0.11GB
zelensky-gpt2-125m.Q4_K_M.gguf	Q4_K_M	0.11GB
zelensky-gpt2-125m.Q4_1.gguf	Q4_1	0.11GB
zelensky-gpt2-125m.Q5_0.gguf	Q5_0	0.11GB
zelensky-gpt2-125m.Q5_K_S.gguf	Q5_K_S	0.11GB
zelensky-gpt2-125m.Q5_K.gguf	Q5_K	0.12GB
zelensky-gpt2-125m.Q5_K_M.gguf	Q5_K_M	0.12GB
zelensky-gpt2-125m.Q5_1.gguf	Q5_1	0.12GB
zelensky-gpt2-125m.Q6_K.gguf	Q6_K	0.13GB
zelensky-gpt2-125m.Q8_0.gguf	Q8_0	0.17GB

Original model description:

license: mit base_model: slava-medvedev/zelensky-gpt2-125m tags:

generated_from_trainer model-index:
name: zelensky-gpt2-125m results: []

zelensky-gpt2-125m

This model is a fine-tuned version of slava-medvedev/zelensky-gpt2-125m on the None dataset.

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: 0.0002
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 128
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1