feat: add Jetson contributor validation path

docs/conditional-testing.md

@@ -70,21 +70,28 @@ probes.
 For an operator run on the labeled machine:
 
 ```bash
-uv sync --locked --extra dev
+uv sync --locked --extra dev --extra quantization
 CUDA_TORCH_VERSION="$(.venv/bin/python -c \
   'import torch; print(torch.__version__.split("+", 1)[0])')"
 UV_TORCH_BACKEND=cu130 uv pip install --python .venv/bin/python \
   --reinstall-package torch "torch==$CUDA_TORCH_VERSION"
 uv pip check --python .venv/bin/python
-uv run --extra dev python scripts/run_conditional_gate.py cuda-runtime
-uv run --extra dev python scripts/run_conditional_gate.py bitsandbytes-runtime
+uv run --extra dev --extra quantization python scripts/run_conditional_gate.py cuda-runtime
+uv run --extra dev --extra quantization python scripts/run_conditional_gate.py bitsandbytes-runtime
 ```
 
-Jetson CUDA support is tracked separately from this generic x64 CUDA lane. A
-generic Linux ARM build can prove package portability, but it does not prove
-Jetson GPU support because Jetson depends on a JetPack/L4T-matched CUDA, cuDNN,
-and PyTorch runtime. The support plan, recommended container path, and acceptance
-criteria are documented in [NVIDIA Jetson support plan](platforms/jetson.md).
+Jetson CUDA support is tracked separately from this generic x64 CUDA lane. The
+mandatory `Linux ARM64 preflight` uses GitHub's hosted `ubuntu-24.04-arm` runner
+to prove locked CPU packaging, imports, CLI startup, and Jetson tooling contracts.
+It is not GPU evidence. Jetson depends on a JetPack/L4T-matched CUDA, cuDNN, and
+PyTorch runtime.
+
+Physical testing uses only a trusted manual dispatch on the labels `self-hosted`,
+`linux`, `ARM64`, and `jetson`. The job preserves NVIDIA's vendor PyTorch, runs
+`jetson-runtime`, and uploads the sanitized `conditional-jetson-<run-attempt>`
+artifact for 30 days. It never runs for a pull request, schedule, or release.
+Contributor bootstrap, runner isolation, reporting commands, and acceptance
+criteria are documented in the [NVIDIA Jetson support plan](platforms/jetson.md).
 
 ## Apple MPS and MLX
 

docs/platforms/jetson.md

@@ -79,6 +79,41 @@ from being selected: upstream documents that wheel as SBSA/server ARM and says
 Jetson L4T/JetPack requires a source build. Until a pinned source build passes
 on the selected device, bitsandbytes is unsupported for that tier.
 
+### Experimental contributor bootstrap
+
+Start with NVIDIA's PyTorch wheel or PyTorch iGPU container for the exact
+JetPack patch installed on the device. Confirm that `python3 -c 'import torch;
+assert torch.cuda.is_available()'` succeeds before installing OBLITERATUS. Then,
+from a checkout of the exact commit under test, run:
+
+```bash
+python3 -m venv .venv-jetson-tools
+.venv-jetson-tools/bin/python -m pip install "uv==0.12.4"
+.venv-jetson-tools/bin/python scripts/setup_jetson.py \
+  --python python3 \
+  --uv-python .venv-jetson-tools/bin/python \
+  --venv .venv-jetson
+.venv-jetson/bin/python scripts/run_conditional_gate.py jetson-runtime
+.venv-jetson/bin/python scripts/jetson_support.py \
+  --check \
+  --gate-evidence conditional-evidence/jetson-runtime.json \
+  --output conditional-evidence/jetson-report.json \
+  --issue-body conditional-evidence/jetson-issue.md
+```
+
+The bootstrap validates ARM64, L4T, and CUDA before changing the environment.
+It creates a virtual environment with `--system-site-packages`, exports the
+committed lock, and installs locked OBLITERATUS dependencies without replacing
+the vendor `torch`. It also excludes bitsandbytes. The generic bitsandbytes
+package is now an explicit `quantization` extra for supported non-Jetson
+environments; Jetson quantization remains a separate source-build milestone.
+
+For JetPack 6.2, NVIDIA publishes the
+`nvcr.io/nvidia/pytorch:25.06-py3-igpu` container. Run it only on Jetson hardware
+with the NVIDIA runtime, mount a reviewed checkout, and use the same bootstrap
+inside the container. Match other JetPack releases through NVIDIA's
+compatibility table rather than substituting a `latest` tag.
+
 ## Conditional gate
 
 Add a new gate instead of modifying the x64 CUDA gate:
@@ -93,6 +128,35 @@ Add a new gate instead of modifying the x64 CUDA gate:
 The job must run only from a trusted ref or reviewed maintainer dispatch. A
 persistent self-hosted Jetson must never execute untrusted pull-request code.
 
+### Attaching a contributor-owned runner
+
+Register the runner using GitHub's self-hosted runner instructions, on the
+Jetson itself, and add the custom label `jetson`. GitHub supplies the
+`self-hosted`, `linux`, and `ARM64` default labels. Verify that the repository
+shows exactly these required labels before dispatching the job:
+
+```text
+self-hosted, linux, ARM64, jetson
+```
+
+Use a dedicated, non-personal runner account and a disposable or resettable
+workspace. Do not place Hugging Face, SSH, cloud, or signing credentials on the
+runner. Only a maintainer should manually dispatch `Conditional tests` against
+a reviewed commit; the Jetson job is deliberately unavailable to pull-request,
+scheduled, and release triggers. Remove the runner registration token after
+setup and keep the runner offline when it is not being used for reviewed work.
+
+### Reporting results without a project-owned Jetson
+
+Open the [Jetson runtime report](https://github.com/elder-plinius/OBLITERATUS/issues/new?template=jetson-runtime.yml)
+issue form and attach `conditional-evidence/jetson-report.json`, or paste the
+generated `conditional-evidence/jetson-issue.md`. The collector reports only an
+allow-listed architecture, OS/JetPack, PyTorch/CUDA, device-class, test-result,
+and commit profile. It excludes environment variables, hostnames, usernames,
+network addresses, device serials, tokens, and local filesystem paths. Review
+the file yourself before publishing it. A failed report is useful evidence and
+does not imply that the contributor must diagnose the compatibility problem.
+
 The gate should verify:
 
 - `platform.machine()` is `aarch64` or equivalent ARM64.
@@ -146,3 +210,6 @@ them.
 - [Astral: Using uv with PyTorch](https://docs.astral.sh/uv/guides/integration/pytorch/)
 - [Hugging Face: bitsandbytes installation guide](https://huggingface.co/docs/bitsandbytes/installation)
 - [GitHub: secure use of self-hosted runners](https://docs.github.com/en/actions/reference/security/secure-use#hardening-for-self-hosted-runners)
+- [GitHub: use self-hosted runner labels](https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/use-in-a-workflow)
+- [GitHub: hosted ARM64 runners](https://docs.github.com/en/actions/reference/runners/github-hosted-runners)
+- [NVIDIA: PyTorch 25.06 for JetPack 6.2](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-06.html)