Image-Text-to-Text
PEFT
Safetensors
English
board-game
ticket-to-ride
vision-language-model
lora
qwen3-vl
behavioral-cloning
conversational
Instructions to use DavidLacour/tchu-qwen3-vl-2b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DavidLacour/tchu-qwen3-vl-2b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-2B-Instruct") model = PeftModel.from_pretrained(base_model, "DavidLacour/tchu-qwen3-vl-2b-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e190d15c1b72e29763dee646a599c102320d4e365fbebab06eebbc1a7ceefd72
- Size of remote file:
- 69.8 MB
- SHA256:
- 572633d4f1bfa5e7203a1af1d5d36b6715c64c52bbbb807260521dd2c24b3ff6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.