Qwen3vl Ins Math 10k
This model is a fine-tuned version of Qwen/Qwen3-VL-8B-Instruct on the mathinterleave dataset.
Model source
- Kind
- Text model
- File size
- Not recorded yet
- Base model
- Qwen/Qwen3-VL-8B-Instruct
- Version
- vRevision a7a3d57
- Updated
- Oct 1, 2026
- Publisher
- @UoM-CS-NeuroSymbolicAI
- Downloads
- 3
- Source
- Hugging Face
Source description
qwen3vl_ins_math
This model is a fine-tuned version of Qwen/Qwen3-VL-8B-Instruct on the math_interleave 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- 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: 2.0
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.22.2
Sources
1 source
Sources
1 sourceVerified Oct 1
Source excerpts
3 excerpts
Source excerpts
3 excerpts--- library_name: transformers license: other base_model: Qwen/Qwen3-VL-8B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: qwen3vl_ins_math results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # qwen3vl_ins_math This model is a fine-tuned version of [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) on the math_interleave 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - 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: 2.0 ### Training results ### Framework versions - Transformers 4.57.1 - Pytorch 2.5.1+cu121 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 3 downloads · 0 likes · Pipeline image-text-to-text · Library transformers · Repo UoM-CS-NeuroSymbolicAI/qwen3vl_ins_math_10k
Source context: 5 downloads · 0 likes · Pipeline image-text-to-text · Library transformers · Repo UoM-CS-NeuroSymbolicAI/qwen3vl_ins_math_10k
