Text Generation
PEFT
Safetensors
English
French
electronics
embedded-systems
emc
lora
sft
ailiance-tuning
conversational
Instructions to use clemsail/ailiance-emc-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use clemsail/ailiance-emc-sft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "clemsail/ailiance-emc-sft") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d61407e69314b90c1b03ca7bcf8a0f0fd361dc71cc9a3b895c3005acd579e11
- Size of remote file:
- 87.4 MB
- SHA256:
- edc4e2dec3c9a82e69acf008f1d943d005f303cdb600ccccbfab8d5783e96883
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.