Instructions to use Krishnaoak17/autoXae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Krishnaoak17/autoXae with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-Coder-1.5B") model = PeftModel.from_pretrained(base_model, "Krishnaoak17/autoXae") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Krishnaoak17/autoXae: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
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https://huggingface.co/Krishnaoak17/autoXae/resolve/main/README.md
- Command line
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hf download hf://Krishnaoak17/autoXae/README.md
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curl -L -o README.md https://huggingface.co/Krishnaoak17/autoXae/resolve/main/README.md
1.26 kB
metadata
library_name: peft
license: apache-2.0
base_model: 01-ai/Yi-Coder-1.5B
tags:
- generated_from_trainer
model-index:
- name: autoXae
results: []
autoXae
This model is a fine-tuned version of 01-ai/Yi-Coder-1.5B 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: 4
- total_train_batch_size: 4
- optimizer: Use adamw_torch 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: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Tokenizers 0.20.3