# Load model directly
from transformers import AutoProcessor, Qwen3VLForStreamQA
processor = AutoProcessor.from_pretrained("interlive/DiffStream-T005-stage2-Qwen3-VL-8B")
model = Qwen3VLForStreamQA.from_pretrained("interlive/DiffStream-T005-stage2-Qwen3-VL-8B")Quick Links
DiffStream-T005-stage2-Qwen3-VL-8B
This model is a fine-tuned version of Qwen/Qwen3-VL-8B-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: 2e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 64
- 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_steps: 512
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.57.3
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
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Qwen/Qwen3-VL-8B-Instruct
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