Instructions to use K00B404/mistral_7B_pythoncodes25k_120steps_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use K00B404/mistral_7B_pythoncodes25k_120steps_lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("K00B404/mistral_7B_pythoncodes25k_120steps_lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use K00B404/mistral_7B_pythoncodes25k_120steps_lora 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 K00B404/mistral_7B_pythoncodes25k_120steps_lora 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 K00B404/mistral_7B_pythoncodes25k_120steps_lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for K00B404/mistral_7B_pythoncodes25k_120steps_lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="K00B404/mistral_7B_pythoncodes25k_120steps_lora", max_seq_length=2048, )
- Xet hash:
- 4f190798ae40f2beb894def9da8eb694e73ae5e0618a73f25e3bfd503d0bb268
- Size of remote file:
- 168 MB
- SHA256:
- fe345a48c7e91d5af19263b14b17c76170aedb6596a53209504c3b2f44f36ff8
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