Instructions to use bookxd/quillory-r2_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bookxd/quillory-r2_data with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "bookxd/quillory-r2_data") - Notebooks
- Google Colab
- Kaggle
| { | |
| "rung": "r2_class", | |
| "base": "Qwen/Qwen2.5-7B-Instruct", | |
| "seed": 0, | |
| "n_samples": 6793, | |
| "steps": 1157, | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lr": 0.0001, | |
| "epochs": 1.35, | |
| "corpus": "/workspace/data/corpus_v4.jsonl", | |
| "samples_cap": null, | |
| "pos_frac": null, | |
| "n_recommend": null, | |
| "kind_counts": { | |
| "positive": 2746, | |
| "negative": 2727, | |
| "benign": 1320 | |
| }, | |
| "span_mask": false, | |
| "body_weight": null, | |
| "steps_overridden": true, | |
| "final_loss": 1.1919145464897156, | |
| "kl_vs_base_nats": 0.08033226108447478, | |
| "kl_lambda": 0.5, | |
| "kl_train_mean": 0.08721451923251151, | |
| "kl_reference_lamerton_roger": 0.006, | |
| "minutes": 63.867197561264035 | |
| } |