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Cambridge-KAIST2_-_SmolLM-14m-Dolma-v0.1-Base-tied-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Cambridge-KAIST2_-_SmolLM-14m-Dolma-v0.1-Base-tied-gguf
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Quantization made by Richard Erkhov.
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SmolLM-14m-Dolma-v0.1-Base-tied - GGUF
Model creator:
https://huggingface.co/Cambridge-KAIST2/
Original model:
https://huggingface.co/Cambridge-KAIST2/SmolLM-14m-Dolma-v0.1-Base-tied/
Name
Quant method
Size
SmolLM-14m-Dolma-v0.1-Base-tied.Q2_K.gguf
Q2_K
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.IQ3_XS.gguf
IQ3_XS
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.IQ3_S.gguf
IQ3_S
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q3_K_S.gguf
Q3_K_S
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.IQ3_M.gguf
IQ3_M
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q3_K.gguf
Q3_K
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q3_K_M.gguf
Q3_K_M
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q3_K_L.gguf
Q3_K_L
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.IQ4_XS.gguf
IQ4_XS
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q4_0.gguf
Q4_0
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.IQ4_NL.gguf
IQ4_NL
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q4_K_S.gguf
Q4_K_S
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q4_K.gguf
Q4_K
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q4_K_M.gguf
Q4_K_M
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q4_1.gguf
Q4_1
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q5_0.gguf
Q5_0
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q5_K_S.gguf
Q5_K_S
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q5_K.gguf
Q5_K
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q5_K_M.gguf
Q5_K_M
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q5_1.gguf
Q5_1
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q6_K.gguf
Q6_K
0.01GB
SmolLM-14m-Dolma-v0.1-Base-tied.Q8_0.gguf
Q8_0
0.01GB
Original model description:
library_name: transformers base_model: JW17/SmolLM-14m-v0.1 tags:
generated_from_trainer model-index:
name: SmolLM-14m-Dolma-v0.1-Base-tied results: []
SmolLM-14m-Dolma-v0.1-Base-tied
This model is a fine-tuned version of
JW17/SmolLM-14m-v0.1
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: 0.003
train_batch_size: 1024
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 2048
total_eval_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: warmup_stable_decay
lr_scheduler_warmup_steps: 1000
num_epochs: 1
Training results
Framework versions
Transformers 4.47.1
Pytorch 2.5.1
Datasets 3.2.0
Tokenizers 0.21.0
