Instructions to use minhtien2405/SmolVLM2B-transformers-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhtien2405/SmolVLM2B-transformers-v0 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
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Download README.md from minhtien2405/SmolVLM2B-transformers-v0: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/minhtien2405/SmolVLM2B-transformers-v0/resolve/main/README.md
- Command line
-
hf download hf://minhtien2405/SmolVLM2B-transformers-v0/README.md
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curl -L -o README.md https://huggingface.co/minhtien2405/SmolVLM2B-transformers-v0/resolve/main/README.md
1.35 kB
metadata
library_name: peft
license: apache-2.0
base_model: HuggingFaceTB/SmolVLM-Base
tags:
- generated_from_trainer
model-index:
- name: SmolVLM2B-transformers-v0
results: []
SmolVLM2B-transformers-v0
This model is a fine-tuned version of HuggingFaceTB/SmolVLM-Base 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0