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SashaSheykina_-_gpt2-finetuned-cXg-nl-to-code-gguf – AI Model by RichardErkhov | AlphaNeural AI
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SashaSheykina_-_gpt2-finetuned-cXg-nl-to-code-gguf
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Quantization made by Richard Erkhov.
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gpt2-finetuned-cXg-nl-to-code - GGUF
Model creator:
https://huggingface.co/SashaSheykina/
Original model:
https://huggingface.co/SashaSheykina/gpt2-finetuned-cXg-nl-to-code/
Name
Quant method
Size
gpt2-finetuned-cXg-nl-to-code.Q2_K.gguf
Q2_K
0.08GB
gpt2-finetuned-cXg-nl-to-code.IQ3_XS.gguf
IQ3_XS
0.08GB
gpt2-finetuned-cXg-nl-to-code.IQ3_S.gguf
IQ3_S
0.08GB
gpt2-finetuned-cXg-nl-to-code.Q3_K_S.gguf
Q3_K_S
0.08GB
gpt2-finetuned-cXg-nl-to-code.IQ3_M.gguf
IQ3_M
0.09GB
gpt2-finetuned-cXg-nl-to-code.Q3_K.gguf
Q3_K
0.09GB
gpt2-finetuned-cXg-nl-to-code.Q3_K_M.gguf
Q3_K_M
0.09GB
gpt2-finetuned-cXg-nl-to-code.Q3_K_L.gguf
Q3_K_L
0.1GB
gpt2-finetuned-cXg-nl-to-code.IQ4_XS.gguf
IQ4_XS
0.1GB
gpt2-finetuned-cXg-nl-to-code.Q4_0.gguf
Q4_0
0.1GB
gpt2-finetuned-cXg-nl-to-code.IQ4_NL.gguf
IQ4_NL
0.1GB
gpt2-finetuned-cXg-nl-to-code.Q4_K_S.gguf
Q4_K_S
0.1GB
gpt2-finetuned-cXg-nl-to-code.Q4_K.gguf
Q4_K
0.11GB
gpt2-finetuned-cXg-nl-to-code.Q4_K_M.gguf
Q4_K_M
0.11GB
gpt2-finetuned-cXg-nl-to-code.Q4_1.gguf
Q4_1
0.11GB
gpt2-finetuned-cXg-nl-to-code.Q5_0.gguf
Q5_0
0.11GB
gpt2-finetuned-cXg-nl-to-code.Q5_K_S.gguf
Q5_K_S
0.11GB
gpt2-finetuned-cXg-nl-to-code.Q5_K.gguf
Q5_K
0.12GB
gpt2-finetuned-cXg-nl-to-code.Q5_K_M.gguf
Q5_K_M
0.12GB
gpt2-finetuned-cXg-nl-to-code.Q5_1.gguf
Q5_1
0.12GB
gpt2-finetuned-cXg-nl-to-code.Q6_K.gguf
Q6_K
0.13GB
gpt2-finetuned-cXg-nl-to-code.Q8_0.gguf
Q8_0
0.17GB
Original model description:
license: mit base_model: gpt2 tags:
Code Generation
Text Generation
generated_from_trainer metrics:
rouge
bleu model-index:
name: gpt2-finetuned-cXg-nl-to-code results: []
gpt2-finetuned-cXg-nl-to-code
This model is a fine-tuned version of
gpt2
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 6.4614
Rouge1: 0.0220
Rouge2: 0.0014
Rougel: 0.0148
Bleu: 0.3339
Meteor: 0.0862
Codebleu: 0.2699
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1
