Text Generation
PEFT
PyTorch
Safetensors
mistral
Generated from Trainer
conversational
4-bit precision
bitsandbytes
Instructions to use HeroMask/Meddi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HeroMask/Meddi with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "HeroMask/Meddi") - Notebooks
- Google Colab
- Kaggle
Download adapter_model/data/109 from HeroMask/Meddi: direct link, hf CLI and curl.
- Browser
- Download file 3.67 MB
-
https://huggingface.co/HeroMask/Meddi/resolve/main/adapter_model/data/109
- Command line
-
hf download hf://HeroMask/Meddi/adapter_model/data/109
-
curl -L -o 109 https://huggingface.co/HeroMask/Meddi/resolve/main/adapter_model/data/109
3.67 MB
- Xet hash:
- 6475b23c5374a53a1ef87ac787c92bfb4f3b295f5f59cf5fd9db13cbc7024f71
- Size of remote file:
- 3.67 MB
- SHA256:
- cb0fc07ce979f2961318f06036b9eca74e233c3627a22ee665392a20ae92413b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.