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ahmedheakl_-_asm2asm-deepseek1.3b-xtokenizer-armv8-gguf – AI Model by RichardErkhov | AlphaNeural AI
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ahmedheakl_-_asm2asm-deepseek1.3b-xtokenizer-armv8-gguf
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asm2asm-deepseek1.3b-xtokenizer-armv8 - GGUF
Model creator:
https://huggingface.co/ahmedheakl/
Original model:
https://huggingface.co/ahmedheakl/asm2asm-deepseek1.3b-xtokenizer-armv8/
Name
Quant method
Size
asm2asm-deepseek1.3b-xtokenizer-armv8.Q2_K.gguf
Q2_K
0.52GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q3_K_S.gguf
Q3_K_S
0.6GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q3_K.gguf
Q3_K
0.66GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q3_K_M.gguf
Q3_K_M
0.66GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q3_K_L.gguf
Q3_K_L
0.69GB
asm2asm-deepseek1.3b-xtokenizer-armv8.IQ4_XS.gguf
IQ4_XS
0.7GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q4_0.gguf
Q4_0
0.72GB
asm2asm-deepseek1.3b-xtokenizer-armv8.IQ4_NL.gguf
IQ4_NL
0.73GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q4_K_S.gguf
Q4_K_S
0.76GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q4_K.gguf
Q4_K
0.81GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q4_K_M.gguf
Q4_K_M
0.81GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q4_1.gguf
Q4_1
0.8GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q5_0.gguf
Q5_0
0.87GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q5_K_S.gguf
Q5_K_S
0.89GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q5_K.gguf
Q5_K
0.93GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q5_K_M.gguf
Q5_K_M
0.93GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q5_1.gguf
Q5_1
0.95GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q6_K.gguf
Q6_K
1.09GB
asm2asm-deepseek1.3b-xtokenizer-armv8.Q8_0.gguf
Q8_0
1.33GB
Original model description:
library_name: transformers license: other base_model: deepseek-ai/deepseek-coder-1.3b-instruct tags:
trl
sft
generated_from_trainer model-index:
name: asm2asm-deepseek-1.3b-500k-mac-x86-O0-arm-gnueabi-gcc results: []
asm2asm-deepseek-1.3b-500k-mac-x86-O0-arm-gnueabi-gcc
This model is a fine-tuned version of
deepseek-ai/deepseek-coder-1.3b-instruct
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: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Framework versions
Transformers 4.44.2
Pytorch 2.4.1+cu118
Datasets 3.0.0
Tokenizers 0.19.1
