Instructions to use arnaultsta/MNLP_M2_mcqa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use arnaultsta/MNLP_M2_mcqa_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-0.6b-base-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "arnaultsta/MNLP_M2_mcqa_model") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use arnaultsta/MNLP_M2_mcqa_model with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for arnaultsta/MNLP_M2_mcqa_model to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for arnaultsta/MNLP_M2_mcqa_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for arnaultsta/MNLP_M2_mcqa_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="arnaultsta/MNLP_M2_mcqa_model", max_seq_length=2048, )
- Xet hash:
- 3312f84c7a83189c1ba45f336e6db409e592dd862b9717dbf9581d336e0b192c
- Size of remote file:
- 5.78 kB
- SHA256:
- bb7ade631c57b0a4c3ae26e2c0c85d54489618c24ef43708dbdfc99d5472dd6f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.