Instructions to use tuandunghcmut/Qwen25_Coder_MultipleChoice_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tuandunghcmut/Qwen25_Coder_MultipleChoice_v4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "tuandunghcmut/Qwen25_Coder_MultipleChoice_v4") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use tuandunghcmut/Qwen25_Coder_MultipleChoice_v4 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 tuandunghcmut/Qwen25_Coder_MultipleChoice_v4 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 tuandunghcmut/Qwen25_Coder_MultipleChoice_v4 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tuandunghcmut/Qwen25_Coder_MultipleChoice_v4 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="tuandunghcmut/Qwen25_Coder_MultipleChoice_v4", max_seq_length=2048, )
Training in progress, step 120
Browse files
validation_metrics/metrics_step_120_20250403_183600.json
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{
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"step": 120,
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"timestamp": "20250403_183600",
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"is_best": false,
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"metrics": {
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"eval_loss": 0.7461407780647278,
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"eval_runtime": 23.5864,
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"eval_samples_per_second": 3.816,
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"eval_steps_per_second": 0.509,
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"epoch": 0.8540925266903915,
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"eval_perplexity":
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