Text Generation
PEFT
Safetensors
banking
intent-classification
lora
sft
qlora
unsloth
gemma
fine-tuned
vietnamese
Instructions to use anyu205/banking_intent_gemma2_unsloth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use anyu205/banking_intent_gemma2_unsloth with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-9b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "anyu205/banking_intent_gemma2_unsloth") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use anyu205/banking_intent_gemma2_unsloth 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 anyu205/banking_intent_gemma2_unsloth 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 anyu205/banking_intent_gemma2_unsloth to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for anyu205/banking_intent_gemma2_unsloth to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="anyu205/banking_intent_gemma2_unsloth", max_seq_length=2048, )
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