Instructions to use xrusnack/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xrusnack/lora_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xrusnack/lora_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use xrusnack/lora_model 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 xrusnack/lora_model 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 xrusnack/lora_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for xrusnack/lora_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="xrusnack/lora_model", max_seq_length=2048, )
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README.md
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- **Finetuned from model :** unsloth/gemma-2-9b-bnb-4bit
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This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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alpaca_prompt = "### Text: {} ### Summary: {}"
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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inputs = tokenizer(
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
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tokenizer.batch_decode(outputs)
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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- **Finetuned from model :** unsloth/gemma-2-9b-bnb-4bit
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This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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## Example Inference:
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```python
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alpaca_prompt = "### Text: {} ### Summary: {}"
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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inputs = tokenizer(
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
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tokenizer.batch_decode(outputs)
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```
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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