Instructions to use jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2 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 jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2 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 jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r4_epochs2", max_seq_length=2048, )
- Xet hash:
- bd761c5ae269e996c41b1590f5ca2855713a57c9a651b41f6ba5dba3c1fa8a6b
- Size of remote file:
- 134 MB
- SHA256:
- 6ae1459689df670e050efa68c7ac5ea49fc195ab7bca61ff9b5a299a895f03c8
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