Instructions to use HimanshuPewal24/bug-explainer-mistral_qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HimanshuPewal24/bug-explainer-mistral_qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "HimanshuPewal24/bug-explainer-mistral_qlora") - Notebooks
- Google Colab
- Kaggle
π Bug Explainer β Mistral-7B QLoRA
Finetuned from mistralai/Mistral-7B-Instruct-v0.2 using 4-bit QLoRA
on Stack Overflow error/answer pairs.
Supports Python Β· JavaScript Β· Java Β· C++.
Training details
- Quantization: 4-bit NF4 + double quantization
- LoRA rank: 16, alpha: 32
- Epochs: 3
- Optimizer: paged_adamw_32bit
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Base model
mistralai/Mistral-7B-Instruct-v0.2