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akhilfau_-_fine-tuned-smolLM2-physics_10-gguf – AI Model by RichardErkhov | AlphaNeural AI
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fine-tuned-smolLM2-physics_10 - GGUF
Model creator:
https://huggingface.co/akhilfau/
Original model:
https://huggingface.co/akhilfau/fine-tuned-smolLM2-physics_10/
Name
Quant method
Size
fine-tuned-smolLM2-physics_10.Q2_K.gguf
Q2_K
0.2GB
fine-tuned-smolLM2-physics_10.IQ3_XS.gguf
IQ3_XS
0.2GB
fine-tuned-smolLM2-physics_10.IQ3_S.gguf
IQ3_S
0.2GB
fine-tuned-smolLM2-physics_10.Q3_K_S.gguf
Q3_K_S
0.2GB
fine-tuned-smolLM2-physics_10.IQ3_M.gguf
IQ3_M
0.21GB
fine-tuned-smolLM2-physics_10.Q3_K.gguf
Q3_K
0.22GB
fine-tuned-smolLM2-physics_10.Q3_K_M.gguf
Q3_K_M
0.22GB
fine-tuned-smolLM2-physics_10.Q3_K_L.gguf
Q3_K_L
0.23GB
fine-tuned-smolLM2-physics_10.IQ4_XS.gguf
IQ4_XS
0.21GB
fine-tuned-smolLM2-physics_10.Q4_0.gguf
Q4_0
0.21GB
fine-tuned-smolLM2-physics_10.IQ4_NL.gguf
IQ4_NL
0.21GB
fine-tuned-smolLM2-physics_10.Q4_K_S.gguf
Q4_K_S
0.24GB
fine-tuned-smolLM2-physics_10.Q4_K.gguf
Q4_K
0.25GB
fine-tuned-smolLM2-physics_10.Q4_K_M.gguf
Q4_K_M
0.25GB
fine-tuned-smolLM2-physics_10.Q4_1.gguf
Q4_1
0.23GB
fine-tuned-smolLM2-physics_10.Q5_0.gguf
Q5_0
0.25GB
fine-tuned-smolLM2-physics_10.Q5_K_S.gguf
Q5_K_S
0.26GB
fine-tuned-smolLM2-physics_10.Q5_K.gguf
Q5_K
0.27GB
fine-tuned-smolLM2-physics_10.Q5_K_M.gguf
Q5_K_M
0.27GB
fine-tuned-smolLM2-physics_10.Q5_1.gguf
Q5_1
0.27GB
fine-tuned-smolLM2-physics_10.Q6_K.gguf
Q6_K
0.34GB
fine-tuned-smolLM2-physics_10.Q8_0.gguf
Q8_0
0.36GB
Original model description:
library_name: transformers license: apache-2.0 base_model: HuggingFaceTB/SmolLM2-360M tags:
generated_from_trainer metrics:
accuracy model-index:
name: fine-tuned-smolLM2-physics_10 results: []
fine-tuned-smolLM2-physics_10
This model is a fine-tuned version of
HuggingFaceTB/SmolLM2-360M
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.4107
Accuracy: 0.1565
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: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.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
Accuracy
0.6273
1.0
40
0.8006
0.0231
0.3887
2.0
80
0.4202
0.1566
0.324
3.0
120
0.4107
0.1565
Framework versions
Transformers 4.46.2
Pytorch 2.4.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3
