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Qwen3-4B-Instruct-2507-UserSim-SFT-Factored – AI Model by davidanugraha | AlphaNeural AI
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Qwen3-4B-Instruct-2507-UserSim-SFT-Factored
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transformers
safetensors
qwen3
text-generation
llama-factory
full
generated_from_trainer
conversational
Qwen/Qwen3-4B-Instruct-2507
finetune
other
text-generation-inference
endpoints_compatible
us
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qwen3_4b_sft
This model is a fine-tuned version of
Qwen/Qwen3-4B-Instruct-2507
on the sft_factored 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: 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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 5
Training results
Framework versions
Transformers 5.6.0
Pytorch 2.12.0+cu130
Datasets 4.0.0
Tokenizers 0.22.2
