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juniorVision_-_SmolLM-135M_inst-sample-240809-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Quantization made by Richard Erkhov.
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SmolLM-135M_inst-sample-240809 - GGUF
Model creator:
https://huggingface.co/juniorVision/
Original model:
https://huggingface.co/juniorVision/SmolLM-135M_inst-sample-240809/
Name
Quant method
Size
SmolLM-135M_inst-sample-240809.Q2_K.gguf
Q2_K
0.08GB
SmolLM-135M_inst-sample-240809.IQ3_XS.gguf
IQ3_XS
0.08GB
SmolLM-135M_inst-sample-240809.IQ3_S.gguf
IQ3_S
0.08GB
SmolLM-135M_inst-sample-240809.Q3_K_S.gguf
Q3_K_S
0.08GB
SmolLM-135M_inst-sample-240809.IQ3_M.gguf
IQ3_M
0.08GB
SmolLM-135M_inst-sample-240809.Q3_K.gguf
Q3_K
0.09GB
SmolLM-135M_inst-sample-240809.Q3_K_M.gguf
Q3_K_M
0.09GB
SmolLM-135M_inst-sample-240809.Q3_K_L.gguf
Q3_K_L
0.09GB
SmolLM-135M_inst-sample-240809.IQ4_XS.gguf
IQ4_XS
0.09GB
SmolLM-135M_inst-sample-240809.Q4_0.gguf
Q4_0
0.09GB
SmolLM-135M_inst-sample-240809.IQ4_NL.gguf
IQ4_NL
0.09GB
SmolLM-135M_inst-sample-240809.Q4_K_S.gguf
Q4_K_S
0.1GB
SmolLM-135M_inst-sample-240809.Q4_K.gguf
Q4_K
0.1GB
SmolLM-135M_inst-sample-240809.Q4_K_M.gguf
Q4_K_M
0.1GB
SmolLM-135M_inst-sample-240809.Q4_1.gguf
Q4_1
0.09GB
SmolLM-135M_inst-sample-240809.Q5_0.gguf
Q5_0
0.1GB
SmolLM-135M_inst-sample-240809.Q5_K_S.gguf
Q5_K_S
0.1GB
SmolLM-135M_inst-sample-240809.Q5_K.gguf
Q5_K
0.1GB
SmolLM-135M_inst-sample-240809.Q5_K_M.gguf
Q5_K_M
0.1GB
SmolLM-135M_inst-sample-240809.Q5_1.gguf
Q5_1
0.1GB
SmolLM-135M_inst-sample-240809.Q6_K.gguf
Q6_K
0.13GB
SmolLM-135M_inst-sample-240809.Q8_0.gguf
Q8_0
0.13GB
Original model description:
library_name: transformers license: apache-2.0 base_model: HuggingFaceTB/SmolLM-135M tags:
trl
sft
generated_from_trainer datasets:
generator model-index:
name: SmolLM-135M_inst-sample-240809 results: []
SmolLM-135M_inst-sample-240809
This model is a fine-tuned version of
HuggingFaceTB/SmolLM-135M
on the generator 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
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
total_eval_batch_size: 64
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: 1.0
Training results
Framework versions
Transformers 4.46.1
Pytorch 2.4.1+cu124
Datasets 3.1.0
Tokenizers 0.20.1
