Question Answering
PEFT
Safetensors
Transformers
English
gemma
lora
sft
4bit
trl
merged
qa
instruction-tuned
270m
Instructions to use sweatSmile/Gemma-3-270m-Buddha-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sweatSmile/Gemma-3-270m-Buddha-QA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-270m") model = PeftModel.from_pretrained(base_model, "sweatSmile/Gemma-3-270m-Buddha-QA") - Transformers
How to use sweatSmile/Gemma-3-270m-Buddha-QA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sweatSmile/Gemma-3-270m-Buddha-QA")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sweatSmile/Gemma-3-270m-Buddha-QA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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| ' + message['content'] + '<end_of_turn> | |
| ' }}{% elif message['role'] == 'system' %}{{ '<start_of_turn>system | |
| ' + message['content'] + '<end_of_turn> | |
| ' }}{% elif message['role'] == 'assistant' %}{{ '<start_of_turn>model | |
| ' + message['content'] + '<end_of_turn> | |
| ' }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<start_of_turn>model | |
| ' }}{% endif %}{% endfor %} |