Instructions to use mmikaildemir/12-june-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmikaildemir/12-june-finetuning with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mmikaildemir/12-june-finetuning", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use mmikaildemir/12-june-finetuning 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 mmikaildemir/12-june-finetuning 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 mmikaildemir/12-june-finetuning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mmikaildemir/12-june-finetuning to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="mmikaildemir/12-june-finetuning", max_seq_length=2048, )
Uploaded finetuned model
- Developed by: mmikaildemir
- License: apache-2.0
- Finetuned from model : unsloth/gemma-3-12b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Hardware compatibility
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16-bit
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Model tree for mmikaildemir/12-june-finetuning
Base model
google/gemma-3-12b-pt Finetuned
google/gemma-3-12b-it Quantized
unsloth/gemma-3-12b-it-unsloth-bnb-4bit