Instructions to use Noor201/gemma-sql-copilot-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Noor201/gemma-sql-copilot-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2b-it-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Noor201/gemma-sql-copilot-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Noor201/gemma-sql-copilot-lora 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 Noor201/gemma-sql-copilot-lora 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 Noor201/gemma-sql-copilot-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Noor201/gemma-sql-copilot-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Noor201/gemma-sql-copilot-lora", max_seq_length=2048, )
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Training Framework: Unsloth (PEFT/LoRA)
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Precision: 4-bit (QLoRA)
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Hardware: Trained on a single NVIDIA T4 GPU via Google Colab.
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Training Framework: Unsloth (PEFT/LoRA)
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Precision: 4-bit (QLoRA)
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Hardware: Trained on a single NVIDIA T4 GPU via Google Colab.
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## 📊 Training Results
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During the fine-tuning process, the model achieved the following performance metrics on the dataset:
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- **Final Training Loss:** 0.0006
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- **Final Validation Loss:** 9.3803
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- **Epochs:** 2
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