Instructions to use jhenberthf/marites-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jhenberthf/marites-ai with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "jhenberthf/marites-ai") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use jhenberthf/marites-ai 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 jhenberthf/marites-ai 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 jhenberthf/marites-ai to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jhenberthf/marites-ai to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jhenberthf/marites-ai", max_seq_length=2048, )
- Xet hash:
- eaf5ef79af56db926190c29d738f24a25bde72ad827f2a10d6312a0f011b080a
- Size of remote file:
- 17.2 MB
- SHA256:
- 58863f24a4fce98ad9921167b8fc1857e1a25770ebd6e8882395b15d9920969b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.