Text Generation
PEFT
Safetensors
mistral3
decision-model
typed-decisions
jev
jevbench
calibration
decode-free
multilingual
vision-language
lora
Instructions to use StandardThinking/StandardOne-8B-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use StandardThinking/StandardOne-8B-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-3-8B-Instruct-2512-BF16") model = PeftModel.from_pretrained(base_model, "StandardThinking/StandardOne-8B-LoRA") - Notebooks
- Google Colab
- Kaggle
Download docs/assets/00-benchmark-card.png from StandardThinking/StandardOne-8B-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 274 kB
-
https://huggingface.co/StandardThinking/StandardOne-8B-LoRA/resolve/main/docs/assets/00-benchmark-card.png
- Command line
-
hf download hf://StandardThinking/StandardOne-8B-LoRA/docs/assets/00-benchmark-card.png
-
curl -L -o 00-benchmark-card.png https://huggingface.co/StandardThinking/StandardOne-8B-LoRA/resolve/main/docs/assets/00-benchmark-card.png
274 kB

- Xet hash:
- 51d7b91678c2100d394b4a04af86d538f5751e8ff35b0967f173aaea015849d3
- Size of remote file:
- 274 kB
- SHA256:
- 367836a2bbd25453d1b3e128aecdee41ca45b581b5c658e886982c890a569010
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.