How to use Jmnlalu/MangARTI 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, "Jmnlalu/MangARTI")
How to use Jmnlalu/MangARTI with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Jmnlalu/MangARTI")
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jmnlalu/MangARTI", dtype="auto")
How to use Jmnlalu/MangARTI 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 Jmnlalu/MangARTI 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 Jmnlalu/MangARTI to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Jmnlalu/MangARTI to start chatting
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Jmnlalu/MangARTI", max_seq_length=2048, )
