Instructions to use mark-eidsaune/paligemma_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mark-eidsaune/paligemma_sft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/paligemma2-3b-pt-224") model = PeftModel.from_pretrained(base_model, "mark-eidsaune/paligemma_sft") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mark-eidsaune/paligemma_sft: direct link, hf CLI and curl.
- Browser
- Download file 1.29 kB
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https://huggingface.co/mark-eidsaune/paligemma_sft/resolve/main/README.md
- Command line
-
hf download hf://mark-eidsaune/paligemma_sft/README.md
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curl -L -o README.md https://huggingface.co/mark-eidsaune/paligemma_sft/resolve/main/README.md
1.29 kB
metadata
library_name: peft
license: gemma
base_model: google/paligemma2-3b-pt-224
tags:
- generated_from_trainer
model-index:
- name: paligemma_sft
results: []
paligemma_sft
This model is a fine-tuned version of google/paligemma2-3b-pt-224 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-05
- train_batch_size: 1
- eval_batch_size: 8
- 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: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 150
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0