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ahmedheakl_-_asm2asm-qwen2.5coder-1.5b-400k-2ep-gguf – AI Model by RichardErkhov | AlphaNeural AI
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ahmedheakl_-_asm2asm-qwen2.5coder-1.5b-400k-2ep-gguf
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asm2asm-qwen2.5coder-1.5b-400k-2ep - GGUF
Model creator:
https://huggingface.co/ahmedheakl/
Original model:
https://huggingface.co/ahmedheakl/asm2asm-qwen2.5coder-1.5b-400k-2ep/
Name
Quant method
Size
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q2_K.gguf
Q2_K
0.63GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.IQ3_XS.gguf
IQ3_XS
0.68GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.IQ3_S.gguf
IQ3_S
0.71GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q3_K_S.gguf
Q3_K_S
0.71GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.IQ3_M.gguf
IQ3_M
0.72GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q3_K.gguf
Q3_K
0.77GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q3_K_M.gguf
Q3_K_M
0.77GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q3_K_L.gguf
Q3_K_L
0.82GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.IQ4_XS.gguf
IQ4_XS
0.84GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q4_0.gguf
Q4_0
0.87GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.IQ4_NL.gguf
IQ4_NL
0.88GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q4_K_S.gguf
Q4_K_S
0.88GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q4_K.gguf
Q4_K
0.92GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q4_K_M.gguf
Q4_K_M
0.92GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q4_1.gguf
Q4_1
0.95GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q5_0.gguf
Q5_0
1.02GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q5_K_S.gguf
Q5_K_S
1.02GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q5_K.gguf
Q5_K
1.05GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q5_K_M.gguf
Q5_K_M
1.05GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q5_1.gguf
Q5_1
1.1GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q6_K.gguf
Q6_K
1.19GB
asm2asm-qwen2.5coder-1.5b-400k-2ep.Q8_0.gguf
Q8_0
1.53GB
Original model description:
library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct tags:
trl
sft
generated_from_trainer model-index:
name: asm2asm-qwen2.5coder-1.5b-400k-2ep results: []
asm2asm-qwen2.5coder-1.5b-400k-2ep
This model is a fine-tuned version of
Qwen/Qwen2.5-Coder-1.5B-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: 8
total_train_batch_size: 8
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
