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axel-datos_-_Llama-3.2-1B_gsm8k_full-finetuning-gguf – AI Model by RichardErkhov | AlphaNeural AI
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axel-datos_-_Llama-3.2-1B_gsm8k_full-finetuning-gguf
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Quantization made by Richard Erkhov.
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Llama-3.2-1B_gsm8k_full-finetuning - GGUF
Model creator:
https://huggingface.co/axel-datos/
Original model:
https://huggingface.co/axel-datos/Llama-3.2-1B_gsm8k_full-finetuning/
Name
Quant method
Size
Llama-3.2-1B_gsm8k_full-finetuning.Q2_K.gguf
Q2_K
0.54GB
Llama-3.2-1B_gsm8k_full-finetuning.IQ3_XS.gguf
IQ3_XS
0.58GB
Llama-3.2-1B_gsm8k_full-finetuning.IQ3_S.gguf
IQ3_S
0.6GB
Llama-3.2-1B_gsm8k_full-finetuning.Q3_K_S.gguf
Q3_K_S
0.6GB
Llama-3.2-1B_gsm8k_full-finetuning.IQ3_M.gguf
IQ3_M
0.61GB
Llama-3.2-1B_gsm8k_full-finetuning.Q3_K.gguf
Q3_K
0.64GB
Llama-3.2-1B_gsm8k_full-finetuning.Q3_K_M.gguf
Q3_K_M
0.64GB
Llama-3.2-1B_gsm8k_full-finetuning.Q3_K_L.gguf
Q3_K_L
0.68GB
Llama-3.2-1B_gsm8k_full-finetuning.IQ4_XS.gguf
IQ4_XS
0.7GB
Llama-3.2-1B_gsm8k_full-finetuning.Q4_0.gguf
Q4_0
0.72GB
Llama-3.2-1B_gsm8k_full-finetuning.IQ4_NL.gguf
IQ4_NL
0.72GB
Llama-3.2-1B_gsm8k_full-finetuning.Q4_K_S.gguf
Q4_K_S
0.72GB
Llama-3.2-1B_gsm8k_full-finetuning.Q4_K.gguf
Q4_K
0.75GB
Llama-3.2-1B_gsm8k_full-finetuning.Q4_K_M.gguf
Q4_K_M
0.75GB
Llama-3.2-1B_gsm8k_full-finetuning.Q4_1.gguf
Q4_1
0.77GB
Llama-3.2-1B_gsm8k_full-finetuning.Q5_0.gguf
Q5_0
0.83GB
Llama-3.2-1B_gsm8k_full-finetuning.Q5_K_S.gguf
Q5_K_S
0.83GB
Llama-3.2-1B_gsm8k_full-finetuning.Q5_K.gguf
Q5_K
0.85GB
Llama-3.2-1B_gsm8k_full-finetuning.Q5_K_M.gguf
Q5_K_M
0.85GB
Llama-3.2-1B_gsm8k_full-finetuning.Q5_1.gguf
Q5_1
0.89GB
Llama-3.2-1B_gsm8k_full-finetuning.Q6_K.gguf
Q6_K
0.95GB
Llama-3.2-1B_gsm8k_full-finetuning.Q8_0.gguf
Q8_0
1.23GB
Original model description:
library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags:
generated_from_trainer datasets:
customized model-index:
name: Llama-3.2-1B_gsm8k_full-finetuning results: []
Llama-3.2-1B_gsm8k_full-finetuning
This model is a fine-tuned version of
meta-llama/Llama-3.2-1B
on the customized 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: 2e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
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: 0.01
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3
