How to use erhanalsr/langusta-kpss-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "erhanalsr/langusta-kpss-lora")
How to use erhanalsr/langusta-kpss-lora with Unsloth Studio:
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 erhanalsr/langusta-kpss-lora to start chatting
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 erhanalsr/langusta-kpss-lora to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for erhanalsr/langusta-kpss-lora to start chatting
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="erhanalsr/langusta-kpss-lora", max_seq_length=2048, )
PEFT adapter trained on erhanalsr/langusta-kpss-reasoning with Unsloth 4-bit QLoRA. This repository contains adapter weights, not a merged full model.
erhanalsr/langusta-kpss-reasoning
Base model
