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Qwen3-4B-Concise-Short-SFT – AI Model by davidanugraha | AlphaNeural AI

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davidanugraha/
Qwen3-4B-Concise-Short-SFT

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text-generation

generated_from_trainer

text-generation-inference

endpoints_compatible

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Qwen3-4B-Concise-Short-SFT

This model is a fine-tuned version of Qwen/Qwen3-4B.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 16
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use OptimizerNames.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: 5