Instructions to use Shibyan/llama3.1_Odata2SQL-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shibyan/llama3.1_Odata2SQL-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "Shibyan/llama3.1_Odata2SQL-lora") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from Shibyan/llama3.1_Odata2SQL-lora: direct link, hf CLI and curl.
- Browser
- Download file 255 Bytes
-
https://huggingface.co/Shibyan/llama3.1_Odata2SQL-lora/resolve/main/all_results.json
- Command line
-
hf download hf://Shibyan/llama3.1_Odata2SQL-lora/all_results.json
-
curl -L -o all_results.json https://huggingface.co/Shibyan/llama3.1_Odata2SQL-lora/resolve/main/all_results.json
255 Bytes
| { | |
| "epoch": 2.7906976744186047, | |
| "num_input_tokens_seen": 77408, | |
| "total_flos": 3495387434582016.0, | |
| "train_loss": 0.3114104429880778, | |
| "train_runtime": 5555.8356, | |
| "train_samples_per_second": 0.093, | |
| "train_steps_per_second": 0.005 | |
| } |