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+# SWE Bench Verified (Compressed)
+
+<picture>
+  <img src="./plot.png" alt="SWE-Bench Verified Total Image Size" style="width:100%">
+</picture>
+
+Setting up all the SWE-Bench Verified images used to take over 200 GiB of storage and 100+ GiB of transfer.
+
+Now it’s just:
+- 31 GiB total storage (down from 206 GiB)
+- 5 GiB network transfer (down from 100 GiB)
+- ~ 5 minutes setup
+
+
+## 🚀 Getting the Images
+
+Images follow the naming convention:
+
+```
+logicstar/sweb.eval.x86_64.<repo>_1776_<instance>
+```
+
+### Docker
+```bash
+curl -L -# https://huggingface.co/LogicStar/SWE-Bench-Verified-Compressed/resolve/main/saved.tar.zst?download=true | zstd -d --long=31 --stdout | docker load 
+```
+
+### Podman
+⚠️ Podman cannot load docker-archives with manifests larger than 1 MiB.
+We split the archive into two parts:
+```bash
+curl -L -# https://huggingface.co/LogicStar/SWE-Bench-Verified-Compressed/resolve/main/saved.1.tar.zst?download=true | zstd -d --long=31 --stdout | podman load 
+curl -L -# https://huggingface.co/LogicStar/SWE-Bench-Verified-Compressed/resolve/main/saved.2.tar.zst?download=true | zstd -d --long=31 --stdout | podman load 
+```
+
+For faster downloads and parallelized loading, use the Hugging Face CLI to download the compressed OCI Layout and our load.py script to load the images in parallel:
+
+```bash
+# Clone the repo and cd into it
+hf download ...
+python3 load.py
+```
+
+## 🛠 Using the Images
+
+Just pass --namespace logicstar to the SWE-Bench harness. Example:
+
+```bash
+python -m swebench.harness.run_evaluation \
+    --predictions_path gold \
+    --max_workers 1 \
+    --run_id validate-gold \
+    --namespace logicstar
+```