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
- Xet hash:
- d5b0fce0178a970f51878c487967ea2535e6cef5011b0b669d52d5665d993856
- Size of remote file:
- 7.63 MB
- SHA256:
- ae9bad0f3041f17d32056ff72a3ff915765208160a6299dd484fa9c50698df45
路
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