Monad
Collection
9 items
•
Updated
Configuration Parsing
Warning:
In adapter_config.json: "peft.task_type" must be a string
SmolVLM-Base-vqav2
This model is a fine-tuned version of HuggingFaceTB/SmolVLM-Base on the None 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use paged_adamw_8bit 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: 50
- num_epochs: 50
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
- 0
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no pipeline_tag.
Model tree for Digiquanta/SmolVLM-Base-vqav2
Base model
HuggingFaceTB/SmolLM2-1.7B
Quantized
HuggingFaceTB/SmolLM2-1.7B-Instruct
Finetuned
HuggingFaceTB/SmolVLM-Base