Instructions to use Mr-FineTuner/fullcomplete-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mr-FineTuner/fullcomplete-01 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mr-FineTuner/fullcomplete-01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Mr-FineTuner/fullcomplete-01 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 Mr-FineTuner/fullcomplete-01 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 Mr-FineTuner/fullcomplete-01 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Mr-FineTuner/fullcomplete-01 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Mr-FineTuner/fullcomplete-01", max_seq_length=2048, )
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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<!-- Provide a quick summary of what the model is/does. -->
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model = FastLanguageModel.get_peft_model(
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model,
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r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
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target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj",],
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lora_alpha = 8,
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lora_dropout = 0.2, # Supports any, but = 0 is optimized
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bias = "none", # Supports any, but = "none" is optimized
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use_gradient_checkpointing = "unsloth",
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random_state = 3407,
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use_rslora = False, #
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loftq_config = None, # And LoftQ
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)
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## Model Details
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