bitext/Bitext-customer-support-llm-chatbot-training-dataset
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How to use Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth") # Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth")
model = AutoModelForMultimodalLM.from_pretrained("Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth")How to use Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth 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 Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth 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 Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Arnic/gemma-3n-E2B-it_customer_support_QA_Unsloth",
max_seq_length=2048,
)Links:
This gemma3n model was trained 2x faster with Unsloth and Huggingface's TRL library.