Instructions to use kvjv/secondmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kvjv/secondmodel with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/paligemma-3b-pt-224") model = PeftModel.from_pretrained(base_model, "kvjv/secondmodel") - Notebooks
- Google Colab
- Kaggle
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Download README.md from kvjv/secondmodel: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/kvjv/secondmodel/resolve/main/README.md
- Command line
-
hf download hf://kvjv/secondmodel/README.md
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curl -L -o README.md https://huggingface.co/kvjv/secondmodel/resolve/main/README.md
1.2 kB
metadata
base_model: google/paligemma-3b-pt-224
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: secondmodel
results: []
secondmodel
This model is a fine-tuned version of google/paligemma-3b-pt-224 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1