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pseshadri_-_gpt2-medium-cardio-llm-v1-gguf – AI Model by RichardErkhov | AlphaNeural AI
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pseshadri_-_gpt2-medium-cardio-llm-v1-gguf
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Quantization made by Richard Erkhov.
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gpt2-medium-cardio-llm-v1 - GGUF
Model creator:
https://huggingface.co/pseshadri/
Original model:
https://huggingface.co/pseshadri/gpt2-medium-cardio-llm-v1/
Name
Quant method
Size
gpt2-medium-cardio-llm-v1.Q2_K.gguf
Q2_K
0.17GB
gpt2-medium-cardio-llm-v1.IQ3_XS.gguf
IQ3_XS
0.18GB
gpt2-medium-cardio-llm-v1.IQ3_S.gguf
IQ3_S
0.19GB
gpt2-medium-cardio-llm-v1.Q3_K_S.gguf
Q3_K_S
0.19GB
gpt2-medium-cardio-llm-v1.IQ3_M.gguf
IQ3_M
0.2GB
gpt2-medium-cardio-llm-v1.Q3_K.gguf
Q3_K
0.21GB
gpt2-medium-cardio-llm-v1.Q3_K_M.gguf
Q3_K_M
0.21GB
gpt2-medium-cardio-llm-v1.Q3_K_L.gguf
Q3_K_L
0.23GB
gpt2-medium-cardio-llm-v1.IQ4_XS.gguf
IQ4_XS
0.22GB
gpt2-medium-cardio-llm-v1.Q4_0.gguf
Q4_0
0.23GB
gpt2-medium-cardio-llm-v1.IQ4_NL.gguf
IQ4_NL
0.23GB
gpt2-medium-cardio-llm-v1.Q4_K_S.gguf
Q4_K_S
0.23GB
gpt2-medium-cardio-llm-v1.Q4_K.gguf
Q4_K
0.25GB
gpt2-medium-cardio-llm-v1.Q4_K_M.gguf
Q4_K_M
0.25GB
gpt2-medium-cardio-llm-v1.Q4_1.gguf
Q4_1
0.25GB
gpt2-medium-cardio-llm-v1.Q5_0.gguf
Q5_0
0.27GB
gpt2-medium-cardio-llm-v1.Q5_K_S.gguf
Q5_K_S
0.27GB
gpt2-medium-cardio-llm-v1.Q5_K.gguf
Q5_K
0.29GB
gpt2-medium-cardio-llm-v1.Q5_K_M.gguf
Q5_K_M
0.29GB
gpt2-medium-cardio-llm-v1.Q5_1.gguf
Q5_1
0.29GB
gpt2-medium-cardio-llm-v1.Q6_K.gguf
Q6_K
0.32GB
gpt2-medium-cardio-llm-v1.Q8_0.gguf
Q8_0
0.41GB
Original model description:
library_name: transformers license: mit base_model: gpt2-medium tags:
generated_from_trainer model-index:
name: gpt2-medium-cardio-llm-v1 results: []
gpt2-medium-cardio-llm-v1
This model is a fine-tuned version of
gpt2-medium
on an unknown 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: 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
Framework versions
Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3
