Instructions to use modular-ai/qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use modular-ai/qwen with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B") model = PeftModel.from_pretrained(base_model, "modular-ai/qwen") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use modular-ai/qwen 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 modular-ai/qwen 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 modular-ai/qwen to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for modular-ai/qwen to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="modular-ai/qwen", max_seq_length=2048, )
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# Kant-Qwen-1.5B (LoRA)
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**Qwen2.5-1.5B** fine-tuned
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> **"Kant, I maintain that..."**
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## Training
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- Dataset: [tarnava/kant_qa](https://huggingface.co/datasets/tarnava/kant_qa) (3873 examples)
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# Kant-Qwen-1.5B (LoRA)
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**Qwen2.5-1.5B** fine-tuned .
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## Training
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- Dataset: [tarnava/kant_qa](https://huggingface.co/datasets/tarnava/kant_qa) (3873 examples)
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