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axel-datos_-_qwen2.5-0.5b-instruct_MATH_lisa-gguf – AI Model by RichardErkhov | AlphaNeural AI
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axel-datos_-_qwen2.5-0.5b-instruct_MATH_lisa-gguf
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qwen2.5-0.5b-instruct_MATH_lisa - GGUF
Model creator:
https://huggingface.co/axel-datos/
Original model:
https://huggingface.co/axel-datos/qwen2.5-0.5b-instruct_MATH_lisa/
Name
Quant method
Size
qwen2.5-0.5b-instruct_MATH_lisa.Q2_K.gguf
Q2_K
0.32GB
qwen2.5-0.5b-instruct_MATH_lisa.IQ3_XS.gguf
IQ3_XS
0.32GB
qwen2.5-0.5b-instruct_MATH_lisa.IQ3_S.gguf
IQ3_S
0.32GB
qwen2.5-0.5b-instruct_MATH_lisa.Q3_K_S.gguf
Q3_K_S
0.32GB
qwen2.5-0.5b-instruct_MATH_lisa.IQ3_M.gguf
IQ3_M
0.32GB
qwen2.5-0.5b-instruct_MATH_lisa.Q3_K.gguf
Q3_K
0.33GB
qwen2.5-0.5b-instruct_MATH_lisa.Q3_K_M.gguf
Q3_K_M
0.33GB
qwen2.5-0.5b-instruct_MATH_lisa.Q3_K_L.gguf
Q3_K_L
0.34GB
qwen2.5-0.5b-instruct_MATH_lisa.IQ4_XS.gguf
IQ4_XS
0.33GB
qwen2.5-0.5b-instruct_MATH_lisa.Q4_0.gguf
Q4_0
0.33GB
qwen2.5-0.5b-instruct_MATH_lisa.IQ4_NL.gguf
IQ4_NL
0.33GB
qwen2.5-0.5b-instruct_MATH_lisa.Q4_K_S.gguf
Q4_K_S
0.36GB
qwen2.5-0.5b-instruct_MATH_lisa.Q4_K.gguf
Q4_K
0.37GB
qwen2.5-0.5b-instruct_MATH_lisa.Q4_K_M.gguf
Q4_K_M
0.37GB
qwen2.5-0.5b-instruct_MATH_lisa.Q4_1.gguf
Q4_1
0.35GB
qwen2.5-0.5b-instruct_MATH_lisa.Q5_0.gguf
Q5_0
0.37GB
qwen2.5-0.5b-instruct_MATH_lisa.Q5_K_S.gguf
Q5_K_S
0.38GB
qwen2.5-0.5b-instruct_MATH_lisa.Q5_K.gguf
Q5_K
0.39GB
qwen2.5-0.5b-instruct_MATH_lisa.Q5_K_M.gguf
Q5_K_M
0.39GB
qwen2.5-0.5b-instruct_MATH_lisa.Q5_1.gguf
Q5_1
0.39GB
qwen2.5-0.5b-instruct_MATH_lisa.Q6_K.gguf
Q6_K
0.47GB
qwen2.5-0.5b-instruct_MATH_lisa.Q8_0.gguf
Q8_0
0.49GB
Original model description:
library_name: transformers license: apache-2.0 base_model: Qwen/qwen2.5-0.5b-instruct tags:
generated_from_trainer datasets:
customized model-index:
name: qwen2.5-0.5b-instruct_MATH_lisa results: []
qwen2.5-0.5b-instruct_MATH_lisa
This model is a fine-tuned version of
Qwen/qwen2.5-0.5b-instruct
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: 1e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
optimizer: Use paged_adamw_32bit 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 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3