Text Generation
PEFT
Safetensors
English
lora
qlora
question-answering
rag
grounded-generation
conversational
Eval Results (legacy)
Instructions to use whosouravsharma/paper-qa-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use whosouravsharma/paper-qa-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "whosouravsharma/paper-qa-lora") - Notebooks
- Google Colab
- Kaggle
Download state.json from whosouravsharma/paper-qa-lora: direct link, hf CLI and curl.
- Browser
- Download file 914 Bytes
-
https://huggingface.co/whosouravsharma/paper-qa-lora/resolve/main/state.json
- Command line
-
hf download hf://whosouravsharma/paper-qa-lora/state.json
-
curl -L -o state.json https://huggingface.co/whosouravsharma/paper-qa-lora/resolve/main/state.json
914 Bytes
| { | |
| "base_model": "Qwen/Qwen2.5-1.5B-Instruct", | |
| "dataset_repo": "whosouravsharma/paper-qa-qasper-sft", | |
| "lora": { | |
| "r": 16, | |
| "alpha": 32, | |
| "dropout": 0.05, | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj" | |
| ] | |
| }, | |
| "training": { | |
| "learning_rate": 0.0002, | |
| "epochs": 3.0, | |
| "per_device_batch_size": 4, | |
| "gradient_accumulation_steps": 4, | |
| "max_seq_length": 2048 | |
| }, | |
| "quantization": "4bit-nf4", | |
| "trainable_params": 4358144, | |
| "total_params": 892974592, | |
| "trainable_pct": 0.488, | |
| "final_eval_metrics": { | |
| "eval_loss": 1.285805106163025, | |
| "eval_runtime": 207.0525, | |
| "eval_samples_per_second": 8.283, | |
| "eval_steps_per_second": 2.072, | |
| "eval_entropy": 1.2657356866589793, | |
| "eval_num_tokens": 3310809.0, | |
| "eval_mean_token_accuracy": 0.7257560479057419, | |
| "epoch": 3.0 | |
| }, | |
| "trained_at": "2026-09-04T11:54:00.127450+00:00" | |
| } |