Instructions to use fals3/bigcode-starcoder2-3b-unit-test-prompt-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fals3/bigcode-starcoder2-3b-unit-test-prompt-tuning with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-3b") model = PeftModel.from_pretrained(base_model, "fals3/bigcode-starcoder2-3b-unit-test-prompt-tuning") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from fals3/bigcode-starcoder2-3b-unit-test-prompt-tuning: direct link, hf CLI and curl.
- Browser
- Download file 234 Bytes
-
https://huggingface.co/fals3/bigcode-starcoder2-3b-unit-test-prompt-tuning/resolve/main/all_results.json
- Command line
-
hf download hf://fals3/bigcode-starcoder2-3b-unit-test-prompt-tuning/all_results.json
-
curl -L -o all_results.json https://huggingface.co/fals3/bigcode-starcoder2-3b-unit-test-prompt-tuning/resolve/main/all_results.json
234 Bytes
| { | |
| "epoch": 3.0, | |
| "total_flos": 1.9035319978426368e+17, | |
| "train_loss": 2.436253024195577, | |
| "train_runtime": 2343.5965, | |
| "train_samples": 7280, | |
| "train_samples_per_second": 9.319, | |
| "train_steps_per_second": 0.582 | |
| } |