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
File size: 667 Bytes
e018a61 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"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
} |