open-r1/codeforces-cots
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How to use ndemoss28/Ladder-3B with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "ndemoss28/Ladder-3B")A QLoRA adapter for Qwen2.5-Coder-3B-Instruct, trained on verified Codeforces
reasoning traces with Ladder.
This model has not been evaluated. There is no pass@1 for it, and none is claimed. It is stored here so the weights are not stranded on a temporary disk, and so any GPU can pick it up and score it. The repository stays private until there are measured numbers to publish with it.
| Base | unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit |
| Method | QLoRA, 4-bit, rank 32, loss on the assistant turn only |
| Data | 2,271 traces, each verified by executing its solution against the problem's own tests |
| Steps | 150, effective batch 16, context 8,192 |
| Hardware | Tesla T4, 5.05 h, 594.9 tok/s, peak VRAM 6.78 GB |
git clone https://github.com/NiLabs-Models/ladder
ladder eval --config configs/ladder-3b-kaggle.yaml # base
ladder eval --config configs/ladder-3b-kaggle.yaml --adapter <this> # tuned
Scoring runs generated programs against real Codeforces test cases. No model-as-judge, no similarity metric.
checker and interactive problems are excluded from evaluation.