Instructions to use Graphiiz/git-base-pokemon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Graphiiz/git-base-pokemon with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/git-base") model = PeftModel.from_pretrained(base_model, "Graphiiz/git-base-pokemon") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Graphiiz/git-base-pokemon: direct link, hf CLI and curl.
- Browser
- Download file 1.19 kB
-
https://huggingface.co/Graphiiz/git-base-pokemon/resolve/main/README.md
- Command line
-
hf download hf://Graphiiz/git-base-pokemon/README.md
-
curl -L -o README.md https://huggingface.co/Graphiiz/git-base-pokemon/resolve/main/README.md
1.19 kB
metadata
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/git-base
model-index:
- name: git-base-pokemon
results: []
git-base-pokemon
This model is a fine-tuned version of microsoft/git-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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2