Instructions to use burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-12B-it") model = PeftModel.from_pretrained(base_model, "burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v2") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v2: direct link, hf CLI and curl.
- Browser
- Download file 131 MB
-
https://huggingface.co/burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v2/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v2/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v2/resolve/main/adapter_model.safetensors
131 MB
- Xet hash:
- b5c360d786a09b5310f02d17b68866669db4a0091f83d1e1bae2a8eda9a89a99
- Size of remote file:
- 131 MB
- SHA256:
- 43d95c0d90163a6936d6b5bc802b7c60ea3d903c2783ec4aa752fd33fbb552ae
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