Instructions to use Violetjy/llms_proj1_500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Violetjy/llms_proj1_500 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Violetjy/llms_proj1_500") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Violetjy/llms_proj1_500 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 Violetjy/llms_proj1_500 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 Violetjy/llms_proj1_500 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Violetjy/llms_proj1_500 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Violetjy/llms_proj1_500", max_seq_length=2048, )
- Xet hash:
- fcc666b9031e75fa262dfedcc40615d1a1eae211790ca7cc1ed00e07bbd49994
- Size of remote file:
- 168 MB
- SHA256:
- 002bf89a929e9c0bd7cc7a55e989bbd218142e85484d3300257af50911b5badd
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