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SFT_think_answer – AI Model by eternite | AlphaNeural AI
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SFT_think_answer
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peft
safetensors
qwen2_5_vl
image-text-to-text
adapter
lora
transformers
text-generation
conversational
Qwen/Qwen2.5-VL-3B-Instruct
text-generation-inference
endpoints_compatible
us
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SFT_think_answer
This model is a fine-tuned version of
Qwen/Qwen2.5-VL-3B-Instruct
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0003
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: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 32
total_eval_batch_size: 2
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.05
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
0.0003
3.0
225
0.0003
Framework versions
PEFT 0.19.1
Transformers 5.10.2
Pytorch 2.12.0+cu126
Datasets 5.0.0
Tokenizers 0.22.2
