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OmniCoT-R1 – AI Model by Eustia1 | AlphaNeural AI
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Eustia1
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OmniCoT-R1
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transformers
safetensors
qwen2_5_vl
image-text-to-text
llama-factory
full
generated_from_trainer
conversational
Qwen/Qwen2.5-VL-7B-Instruct
finetune
other
text-generation-inference
endpoints_compatible
us
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OmniCoT-R1
This model is a fine-tuned version of
Qwen/Qwen2.5-VL-7B-Instruct
on OmniCoT-related panoramic CoT data.
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: 2
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0
Training results
Framework versions
Transformers 4.57.3
Pytorch 2.9.1+cu128
Datasets 4.0.0
Tokenizers 0.22.1
