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, )
| base_model: unsloth/gemma-2-9b-bnb-4bit | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - gemma2 | |
| - trl | |
| license: apache-2.0 | |
| language: | |
| - en | |
| # Uploaded model | |
| - **Finetuned from model :** unsloth/gemma-2-9b-bnb-4bit | |
| This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. | |
| ## Training | |
| The gpt-4o-mini model was used to summarize 100 of the text examples in this dataset https://huggingface.co/datasets/vojtam/czech_books_descriptions | |
| The lora model was trained on these summaries. | |
| ## Example of Inference: | |
| ```python | |
| alpaca_prompt = "### Text: {} ### Summary: {}" | |
| FastLanguageModel.for_inference(model) | |
| inputs = tokenizer( | |
| [ | |
| alpaca_prompt.format( | |
| "", # text to summarize | |
| "", # output - leave this blank for generation! | |
| ) | |
| ], return_tensors = "pt").to("cuda") | |
| outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) | |
| tokenizer.batch_decode(outputs) | |
| ``` | |
| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | |