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GOODYEONSU_-_Qwen2.5-0.5B-Instruct-ymg-finetuned-1007-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Qwen2.5-0.5B-Instruct-ymg-finetuned-1007 - GGUF
Model creator:
https://huggingface.co/GOODYEONSU/
Original model:
https://huggingface.co/GOODYEONSU/Qwen2.5-0.5B-Instruct-ymg-finetuned-1007/
Name
Quant method
Size
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q2_K.gguf
Q2_K
0.39GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q3_K_S.gguf
Q3_K_S
0.39GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q3_K.gguf
Q3_K
0.4GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q3_K_M.gguf
Q3_K_M
0.4GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q3_K_L.gguf
Q3_K_L
0.42GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.IQ4_XS.gguf
IQ4_XS
0.4GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q4_0.gguf
Q4_0
0.4GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.IQ4_NL.gguf
IQ4_NL
0.4GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q4_K_S.gguf
Q4_K_S
0.45GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q4_K.gguf
Q4_K
0.46GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q4_K_M.gguf
Q4_K_M
0.46GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q4_1.gguf
Q4_1
0.43GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q5_0.gguf
Q5_0
0.46GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q5_K_S.gguf
Q5_K_S
0.48GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q5_K.gguf
Q5_K
0.49GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q5_K_M.gguf
Q5_K_M
0.49GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q5_1.gguf
Q5_1
0.49GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q6_K.gguf
Q6_K
0.61GB
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007.Q8_0.gguf
Q8_0
0.63GB
Original model description:
library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct tags:
trl
sft
generated_from_trainer model-index:
name: Qwen2.5-0.5B-Instruct-ymg-finetuned-1007 results: []
Qwen2.5-0.5B-Instruct-ymg-finetuned-1007
This model is a fine-tuned version of
Qwen/Qwen2.5-0.5B-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: 5e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 3
gradient_accumulation_steps: 8
total_train_batch_size: 384
total_eval_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.03
num_epochs: 16
Training results
Framework versions
Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1
