Instructions to use KasaiDanto/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use KasaiDanto/model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "KasaiDanto/model") - Notebooks
- Google Colab
- Kaggle
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Download README.md from KasaiDanto/model: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/KasaiDanto/model/resolve/main/README.md
- Command line
-
hf download hf://KasaiDanto/model/README.md
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curl -L -o README.md https://huggingface.co/KasaiDanto/model/resolve/main/README.md
1.42 kB
metadata
base_model: google/gemma-2-2b-it
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: model
results: []
model
This model is a fine-tuned version of google/gemma-2-2b-it on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8714
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: 2
- eval_batch_size: 2
- seed: 4856
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.995) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0284 | 0.9999 | 3912 | 1.9896 |
| 1.8333 | 1.9999 | 7824 | 1.8714 |
Framework versions
- PEFT 0.14.0
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0