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adaptiveaiventures_-_Llama-32-1B-finetuned-aaivtest5-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Llama-32-1B-finetuned-aaivtest5 - GGUF
Model creator:
https://huggingface.co/adaptiveaiventures/
Original model:
https://huggingface.co/adaptiveaiventures/Llama-32-1B-finetuned-aaivtest5/
Name
Quant method
Size
Llama-32-1B-finetuned-aaivtest5.Q2_K.gguf
Q2_K
0.08GB
Llama-32-1B-finetuned-aaivtest5.IQ3_XS.gguf
IQ3_XS
0.08GB
Llama-32-1B-finetuned-aaivtest5.IQ3_S.gguf
IQ3_S
0.08GB
Llama-32-1B-finetuned-aaivtest5.Q3_K_S.gguf
Q3_K_S
0.08GB
Llama-32-1B-finetuned-aaivtest5.IQ3_M.gguf
IQ3_M
0.09GB
Llama-32-1B-finetuned-aaivtest5.Q3_K.gguf
Q3_K
0.09GB
Llama-32-1B-finetuned-aaivtest5.Q3_K_M.gguf
Q3_K_M
0.09GB
Llama-32-1B-finetuned-aaivtest5.Q3_K_L.gguf
Q3_K_L
0.1GB
Llama-32-1B-finetuned-aaivtest5.IQ4_XS.gguf
IQ4_XS
0.1GB
Llama-32-1B-finetuned-aaivtest5.Q4_0.gguf
Q4_0
0.1GB
Llama-32-1B-finetuned-aaivtest5.IQ4_NL.gguf
IQ4_NL
0.1GB
Llama-32-1B-finetuned-aaivtest5.Q4_K_S.gguf
Q4_K_S
0.1GB
Llama-32-1B-finetuned-aaivtest5.Q4_K.gguf
Q4_K
0.11GB
Llama-32-1B-finetuned-aaivtest5.Q4_K_M.gguf
Q4_K_M
0.11GB
Llama-32-1B-finetuned-aaivtest5.Q4_1.gguf
Q4_1
0.11GB
Llama-32-1B-finetuned-aaivtest5.Q5_0.gguf
Q5_0
0.11GB
Llama-32-1B-finetuned-aaivtest5.Q5_K_S.gguf
Q5_K_S
0.11GB
Llama-32-1B-finetuned-aaivtest5.Q5_K.gguf
Q5_K
0.12GB
Llama-32-1B-finetuned-aaivtest5.Q5_K_M.gguf
Q5_K_M
0.12GB
Llama-32-1B-finetuned-aaivtest5.Q5_1.gguf
Q5_1
0.12GB
Llama-32-1B-finetuned-aaivtest5.Q6_K.gguf
Q6_K
0.13GB
Llama-32-1B-finetuned-aaivtest5.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: Llama-32-1B-finetuned-aaivtest5 results: []
Llama-32-1B-finetuned-aaivtest5
This model is a fine-tuned version of
gpt2
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.0245
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: 2
eval_batch_size: 2
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
1.6887
1.0
47
1.2088
1.006
2.0
94
1.0331
0.8893
3.0
141
1.0245
Framework versions
Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0
