Instructions to use BenjaminPittsley/ue5-code-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenjaminPittsley/ue5-code-expert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BenjaminPittsley/ue5-code-expert") model = AutoModelForSeq2SeqLM.from_pretrained("BenjaminPittsley/ue5-code-expert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from BenjaminPittsley/ue5-code-expert: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
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https://huggingface.co/BenjaminPittsley/ue5-code-expert/resolve/main/README.md
- Command line
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hf download hf://BenjaminPittsley/ue5-code-expert/README.md
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curl -L -o README.md https://huggingface.co/BenjaminPittsley/ue5-code-expert/resolve/main/README.md
1.24 kB
metadata
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: ue5-code-expert
results: []
ue5-code-expert
This model is a fine-tuned version of google/flan-t5-small 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2