Instructions to use leftyfeep/ape-fiction-full-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use leftyfeep/ape-fiction-full-instruct 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 leftyfeep/ape-fiction-full-instruct 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 leftyfeep/ape-fiction-full-instruct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for leftyfeep/ape-fiction-full-instruct to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="leftyfeep/ape-fiction-full-instruct", max_seq_length=2048, )
ape = Algorithmic Pattern Emulation
A finetune of Mistral Nemo Instruct 2407 using my fullfictions-85kmax dataset. The prompts are fairly simple. My goal is to train a model that can write long ficiton that make sense. The training data contains the full text of public domain short stories and novels. 85k of context is about the limit I've been able to train without getting OOM errors using rented GPUs.
Thanks to unsloth brothers, gutenberg volunteers, the Mistral Nemo team, and the folks in various discord servers who have helped me out.
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