Instructions to use Balab2021/Qwen2.5-3B-OASST1-QLoRA_h200-run2-conservative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Balab2021/Qwen2.5-3B-OASST1-QLoRA_h200-run2-conservative with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct") model = PeftModel.from_pretrained(base_model, "Balab2021/Qwen2.5-3B-OASST1-QLoRA_h200-run2-conservative") - Notebooks
- Google Colab
- Kaggle
run2_conservative
QLoRA adapter fine-tuned from Qwen/Qwen2.5-3B-Instruct on OpenAssistant/oasst1.
Final metrics
- train_loss: 1.3559324711929133
- eval_loss: 1.4965205192565918
- loss_gap: 0.14058804806367853
- eval_perplexity: 4.4661222175075865
- train_runtime: 2643.5786
- samples_per_second: 6.852
- global_steps: 1134
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