Instructions to use MusYW/MNLP_M3_mcqa_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MusYW/MNLP_M3_mcqa_model_2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B-Base")
model = PeftModel.from_pretrained(base_model, "MusYW/MNLP_M3_mcqa_model_2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

Unsloth Studio

How to use MusYW/MNLP_M3_mcqa_model_2 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 MusYW/MNLP_M3_mcqa_model_2 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 MusYW/MNLP_M3_mcqa_model_2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MusYW/MNLP_M3_mcqa_model_2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="MusYW/MNLP_M3_mcqa_model_2",
    max_seq_length=2048,
)

MNLP_M3_mcqa_model_2 / tokenizer_config.json

Commit History

Training in progress, step 500

9aefdd2
verified

MusYW commited on Jun 4, 2025

Commit History

Training in progress, step 500 9aefdd2 verified

Training in progress, step 500

9aefdd2
verified