Instructions to use q1e123/peft-starcoder-lora-a100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use q1e123/peft-starcoder-lora-a100 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2") model = PeftModel.from_pretrained(base_model, "q1e123/peft-starcoder-lora-a100") - Notebooks
- Google Colab
- Kaggle
peft-starcoder-lora-a100
This model is a fine-tuned version of distilbert/distilgpt2 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
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Model tree for q1e123/peft-starcoder-lora-a100
Base model
distilbert/distilgpt2