Instructions to use liushiliushi/llama-uncertainty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use liushiliushi/llama-uncertainty with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("../../meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "liushiliushi/llama-uncertainty") - Notebooks
- Google Colab
- Kaggle
Llama 7B Uncertainty Calibration Model (Brier Loss)
This model is a fine-tuned version of Llama-3.1-8B-Instruct optimized for uncertainty calibration using Brier score loss.
Model Details
Model Description
- Developed by: liushiliushi
- Model type: Llama fine-tuned with PEFT/LoRA
- Language(s): English
- License: Same as base model (Llama 3.1)
- Finetuned from model: meta-llama/Llama-3.1-8B-Instruct
Uses
Direct Use
This model is optimized for tasks requiring well-calibrated uncertainty estimates.
Training Details
Training Hyperparameters
- Learning rate: 1e-5
- Epochs: 2
- Loss function: Brier score
- Batch size: 16
Framework versions
- PEFT 0.12.0
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Model tree for liushiliushi/llama-uncertainty
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct