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
|
@@ -13,10 +13,21 @@ language:
|
|
| 13 |
|
| 14 |
# Uploaded model
|
| 15 |
|
| 16 |
-
- **Developed by:** xrusnack
|
| 17 |
-
- **License:** apache-2.0
|
| 18 |
- **Finetuned from model :** unsloth/gemma-2-9b-bnb-4bit
|
| 19 |
|
| 20 |
This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 21 |
|
| 22 |
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
|
|
|
| 13 |
|
| 14 |
# Uploaded model
|
| 15 |
|
|
|
|
|
|
|
| 16 |
- **Finetuned from model :** unsloth/gemma-2-9b-bnb-4bit
|
| 17 |
|
| 18 |
This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
|
| 19 |
+
Example Infernce:
|
| 20 |
+
alpaca_prompt = "### Text: {} ### Summary: {}"
|
| 21 |
+
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
|
| 22 |
+
inputs = tokenizer(
|
| 23 |
+
[
|
| 24 |
+
alpaca_prompt.format(
|
| 25 |
+
"", # text to summarize
|
| 26 |
+
"", # output - leave this blank for generation!
|
| 27 |
+
)
|
| 28 |
+
], return_tensors = "pt").to("cuda")
|
| 29 |
+
|
| 30 |
+
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
|
| 31 |
+
tokenizer.batch_decode(outputs)
|
| 32 |
|
| 33 |
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|