Instructions to use pepoo20/Qwen2Math_Pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use pepoo20/Qwen2Math_Pretrain with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-Math-7B") model = PeftModel.from_pretrained(base_model, "pepoo20/Qwen2Math_Pretrain") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use pepoo20/Qwen2Math_Pretrain 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 pepoo20/Qwen2Math_Pretrain 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 pepoo20/Qwen2Math_Pretrain to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for pepoo20/Qwen2Math_Pretrain to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="pepoo20/Qwen2Math_Pretrain", max_seq_length=2048, )
- Xet hash:
- 664cedf288783d4688c61dcdc7a2aac06c1e8f0aa7759bb879dc40fb79fa4bcf
- Size of remote file:
- 5.37 kB
- SHA256:
- 7f1e35fce2a4c30a9b12b835f2e1bfa112f2c4cfbc570c843894e0ee2f221628
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