Instructions to use PranathReddy/solveall-qwen35-9b-full-reward-ablation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PranathReddy/solveall-qwen35-9b-full-reward-ablation with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "PranathReddy/solveall-qwen35-9b-full-reward-ablation") - Notebooks
- Google Colab
- Kaggle
Download assets/optimization_comparison.png from PranathReddy/solveall-qwen35-9b-full-reward-ablation: direct link, hf CLI and curl.
- Browser
- Download file 625 kB
-
https://huggingface.co/PranathReddy/solveall-qwen35-9b-full-reward-ablation/resolve/main/assets/optimization_comparison.png
- Command line
-
hf download hf://PranathReddy/solveall-qwen35-9b-full-reward-ablation/assets/optimization_comparison.png
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curl -L -o optimization_comparison.png https://huggingface.co/PranathReddy/solveall-qwen35-9b-full-reward-ablation/resolve/main/assets/optimization_comparison.png
625 kB

- Xet hash:
- aabbaa923fcd7903ef34a0a983e73263f735a33ec7fbaa9a0af935578b2b1351
- Size of remote file:
- 625 kB
- SHA256:
- 081cf4dab5f8230a0ea2af79f04d72a6c28048a65d395eaafcc6b4f1f7268ffd
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