Instructions to use burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v3 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-v3 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-v3") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v3: direct link, hf CLI and curl.
- Browser
- Download file 131 MB
-
https://huggingface.co/burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v3/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v3/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/burtenshaw/gemma-4-12b-sdpo-pi-mono-trace-feedback-v3/resolve/main/adapter_model.safetensors
131 MB
- Xet hash:
- 41100e547e2fa72f0280bc124c6a861df6117bbbac61963cbef82fa5211e2fee
- Size of remote file:
- 131 MB
- SHA256:
- 8150ae39a9fd2af62a1e53bbf46c23a37b7093537218a3da257157fd7b45a32f
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