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, )
Update README.md
Browse files
README.md
CHANGED
|
@@ -19,7 +19,7 @@ This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslot
|
|
| 19 |
|
| 20 |
## Training
|
| 21 |
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
|
| 22 |
-
The lora model was trained on
|
| 23 |
|
| 24 |
|
| 25 |
## Example of Inference:
|
|
|
|
| 19 |
|
| 20 |
## Training
|
| 21 |
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
|
| 22 |
+
The lora model was trained on these summaries.
|
| 23 |
|
| 24 |
|
| 25 |
## Example of Inference:
|