Instructions to use Jabr7/Mini-Boni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jabr7/Mini-Boni with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jabr7/Mini-Boni", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Jabr7/Mini-Boni 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 Jabr7/Mini-Boni 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 Jabr7/Mini-Boni to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Jabr7/Mini-Boni to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Jabr7/Mini-Boni", max_seq_length=2048, )
Update README.md
Browse files
README.md
CHANGED
|
@@ -63,7 +63,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
| 63 |
from peft import PeftModel
|
| 64 |
|
| 65 |
base_model = "unsloth/gemma-2-2b-it-bnb-4bit"
|
| 66 |
-
adapter = "
|
| 67 |
|
| 68 |
tokenizer = AutoTokenizer.from_pretrained(base_model)
|
| 69 |
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
|
|
|
|
| 63 |
from peft import PeftModel
|
| 64 |
|
| 65 |
base_model = "unsloth/gemma-2-2b-it-bnb-4bit"
|
| 66 |
+
adapter = "Jabr7/Mini-Boni"
|
| 67 |
|
| 68 |
tokenizer = AutoTokenizer.from_pretrained(base_model)
|
| 69 |
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
|