Instructions to use andstor/bigcode-starcoderbase-unit-test-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use andstor/bigcode-starcoderbase-unit-test-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase") model = PeftModel.from_pretrained(base_model, "andstor/bigcode-starcoderbase-unit-test-lora") - Notebooks
- Google Colab
- Kaggle
| license: bigcode-openrail-m | |
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - andstor/methods2test_small | |
| metrics: | |
| - accuracy | |
| base_model: bigcode/starcoderbase | |
| model-index: | |
| - name: output | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Causal Language Modeling | |
| dataset: | |
| name: andstor/methods2test_small fm+fc+c+m+f+t+tc | |
| type: andstor/methods2test_small | |
| args: fm+fc+c+m+f+t+tc | |
| metrics: | |
| - type: accuracy | |
| value: 0.72106682199951 | |
| name: Accuracy | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/andstor/methods2test_small/runs/1pax4nzw) | |
| # output | |
| This model is a fine-tuned version of [bigcode/starcoderbase](https://huggingface.co/bigcode/starcoderbase) on the andstor/methods2test_small fm+fc+c+m+f+t+tc dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6379 | |
| - Accuracy: 0.7211 | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 2 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 16 | |
| - total_eval_batch_size: 2 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 3.0 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.41.0.dev0 | |
| - Pytorch 2.2.1+cu118 | |
| - Datasets 2.17.1 | |
| - Tokenizers 0.19.1 |