Instructions to use yujinku/foodberry0802 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use yujinku/foodberry0802 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 yujinku/foodberry0802 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 yujinku/foodberry0802 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for yujinku/foodberry0802 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="yujinku/foodberry0802", max_seq_length=2048, )
foodberry0802 : GGUF
This model was finetuned and converted to GGUF format using Unsloth.
Example usage:
- For text only LLMs:
llama-cli -hf yujinku/foodberry0802 --jinja - For multimodal models:
llama-mtmd-cli -hf yujinku/foodberry0802 --jinja
Available Model files:
llama-3.2-3b.Q8_0.ggufThis was trained 2x faster with Unsloth
Inference Providers NEW
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