Text Generation
PEFT
Safetensors
English
text-generation-inference
unsloth
llama-3.1
math
reasoning
orca
structured-output
lora
conversational
Instructions to use x0root/Llama-3.1-8B-Orca-Structured-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use x0root/Llama-3.1-8B-Orca-Structured-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "x0root/Llama-3.1-8B-Orca-Structured-LoRA") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use x0root/Llama-3.1-8B-Orca-Structured-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 x0root/Llama-3.1-8B-Orca-Structured-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 x0root/Llama-3.1-8B-Orca-Structured-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for x0root/Llama-3.1-8B-Orca-Structured-LoRA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="x0root/Llama-3.1-8B-Orca-Structured-LoRA", max_seq_length=2048, )
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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