Instructions to use AnalyticsIntelligence/PIDGIN_gemma3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnalyticsIntelligence/PIDGIN_gemma3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AnalyticsIntelligence/PIDGIN_gemma3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Update handler.py
Browse files- handler.py +2 -1
handler.py
CHANGED
|
@@ -6,7 +6,8 @@ from peft import PeftModel
|
|
| 6 |
class EndpointHandler:
|
| 7 |
def __init__(self, path=""):
|
| 8 |
# CORRECTED: Use gemma-3-4b instead of gemma-2-2b
|
| 9 |
-
|
|
|
|
| 10 |
|
| 11 |
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
|
| 12 |
model = AutoModelForCausalLM.from_pretrained(
|
|
|
|
| 6 |
class EndpointHandler:
|
| 7 |
def __init__(self, path=""):
|
| 8 |
# CORRECTED: Use gemma-3-4b instead of gemma-2-2b
|
| 9 |
+
#unsloth/gemma-3-4b-it-unsloth-bnb-4bit
|
| 10 |
+
model_id = "unsloth/gemma-3-4b-it-unsloth"
|
| 11 |
|
| 12 |
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
|
| 13 |
model = AutoModelForCausalLM.from_pretrained(
|