Hyperloom Qwen3 14b Fp8 12h

Hyperloom Qwen3-14B-FP8 12h Run

Load .env with the preamble below and resolve HYPERLOOM_SKILL_PATH. Read and follow the optimizer skill at @${HYPERLOOM_SKILL_PATH} before launching. If HYPERLOOM_SKILL_PATH is missing, fall back to @hyperloom/inference_optimizer/SKILL.md (wheel install) or @src/hyperloom/inference_optimizer/SKILL.md (source checkout). This skill provides the concrete workload and launch constraints for a 12-hour Qwen3-14B-FP8 demo.

The serving framework is sglang, vllm or atom. SGLang and vLLM follow the sections below as written. For ATOM (FRAMEWORK=atom, or the user asked for ATOM, including a Docker run whose container has not been detected yet), use the ATOM section for the execution shell, run mode, setup, runtime install and launch command; the workload flags in Environment, the model choice, User-visible Progress and Launch Requirements apply to ATOM unchanged.

Execution Shell

Run this in the current Hyperloom workspace before setup or runtime installation. Repeat it in each new execution shell, including inside Docker; the shared loader fills dotenv gaps without replacing existing non-empty exports.

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set -e
export REPO_ROOT="$(pwd -P)"
INSTALL_SH="${REPO_ROOT}/hyperloom/inference_optimizer/assets/install.sh"
if [ ! -f "$INSTALL_SH" ]; then
  INSTALL_SH="${REPO_ROOT}/src/hyperloom/inference_optimizer/assets/install.sh"
fi
. "${INSTALL_SH%/*}/runtime_env.sh"
load_dotenv_no_clobber

Run Mode

Resolve the run mode before launching Hyperloom:

1. If HYPERLOOM_RUN_MODE=baremetal or it is unset, run this demo directly on the host.
2. If HYPERLOOM_RUN_MODE=docker, this skill owns the Docker setup. Ask the user whether they want a vllm or sglang Docker image unless HYPERLOOM_IMAGE is already set. Use a ROCm image that already contains the selected framework; do not install the framework inside Docker.

In docker mode:

• If hyperloom-setup already ran, do not re-run setup on the host.
• Read HYPERLOOM_DOCKER_TARGET_HOST from .env when present. If it names a host different from $(hostname), first SSH to that host and continue this Docker setup there; do not start Docker on the login/current host.
• Always run setup inside the container after docker run.
• Pass --install-framework none --yes in the container (ROCm/framework comes from the image). Do not use --skip-base-check — let Phase 1 preflight validate the container environment.
• Do not run python -m hyperloom optimize on the host.

Prior workload cleanup (required)

Before any replacement launch after a failed or abandoned demo run (docker run, install.sh, or a new/fresh optimize), follow IR-1 — Prior workload cleanup gate in @${HYPERLOOM_SKILL_PATH}. Run all probes on the docker host; never skip the user-approval step (#1314).

Suggested Docker images:

• vllm: docker.io/rocm/vllm:rocm10.0.0_ubuntu24.04_py3.14_pytorch_2.12.0_vllm_0.27.0
• sglang MI300X: docker.io/lmsysorg/sglang-rocm:v0.5.21-rocm10-mi30x-20261008
• sglang MI355X: docker.io/lmsysorg/sglang-rocm:v0.5.21-rocm10-mi35x-20261008
• atom: see ATOM Docker container

In Docker mode, start a long-running container on HYPERLOOM_DOCKER_TARGET_HOST (or the current host when it is unset) before running setup or optimize:

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export REPO_ROOT="$(pwd -P)"
docker run -d \
  --name "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}" \
  --shm-size "${HYPERLOOM_SHM_SIZE:-64g}" \
  --entrypoint tail \
  --device /dev/kfd \
  --device /dev/dri \
  --group-add video \
  -v "$REPO_ROOT:$REPO_ROOT" \
  "$HYPERLOOM_IMAGE" \
  -f /dev/null

Mount the Hyperloom workspace at the same absolute path (-v "$REPO_ROOT:$REPO_ROOT") so paths in .env, logs, and session artifacts stay valid. If USER_DATA_PATH or a pre-downloaded model directory is outside the workspace, add matching -v host_path:host_path mounts before starting the container.

Then run the setup backend inside the container:

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docker exec -w "$REPO_ROOT" "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}" bash -lc \
  'REPO_ROOT="$(pwd -P)"; PYTHONPATH="$REPO_ROOT" python3 -m hyperloom setup -- --install-framework none --yes'

After that, run all remaining commands for this demo inside the same container with docker exec -w "$REPO_ROOT" ...; do not run python -m hyperloom optimize on the host in Docker mode.

Do not use docker exec -d to launch optimize. Detached docker exec discards stdout and stderr, so an optimizer that dies on startup looks like "backgrounding does not work." Use one attached docker exec -w "$REPO_ROOT" "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}" bash -lc '…' that runs the Launch recipe in @${HYPERLOOM_SKILL_PATH}. Startup preflight loads kernel-agent.env.sh; do not source it. Under Claw, hand that attached exec to the bash tool with run_in_background=true and no setsid, nohup, or trailing &; otherwise the command inside the exec is setsid nohup … > "$RUN_LOG" 2>&1 < /dev/null & plus --launch-info-file. Confirm with pgrep -af 'hyperloom optimize'. If nothing is alive or the launch-info JSON has no .session_dir, read the run log and fix that error; do not retry with a different backgrounding trick.

When the demo is finished, ask the user whether to stop the container. If they say yes, run:

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docker stop "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}"

Environment

• MODEL_PATH=<optional; if unset, download Qwen/Qwen3-14B-FP8 from Hugging Face with the Python steps below, then set MODEL_PATH to that local path>

• FRAMEWORK=<sglang, vllm or atom; provided by the existing environment or repository-root .env; do not invent it>

• GPU_TYPE=<do not set; omit --gpu-type and let Hyperloom auto-detect from ROCm/system info> Required optimize CLI flags:

• --tp 1

• --conc 64

• --isl 1024

• --osl 1024

• --precision fp8

• --target-gain 50

• --max-hours 12

• --phase-budget-framework-pct 0.43

• --phase-budget-kernel-pct 0.42

Before launch, read the repository-root .env file if it exists and load the needed environment variables from it, such as LLM API keys/base URLs, FRAMEWORK, and HF_TOKEN. Do not copy secret values into the prompt, terminal output, reports, or logs. Do not modify USER_DATA_PATH.

Before resolving or downloading any model, always ask the user which model path to use. Present the currently resolved option when MODEL_PATH is already set, and always offer a custom local path plus the demo default. Do not continue until the user chooses one.

Use this decision flow:

• If the user chooses the existing MODEL_PATH, inspect that path and use it only when it contains config.json; otherwise ask again for a valid path or the demo default.
• If the user provides a custom local path, export MODEL_PATH to that path and require config.json before launch.
• If the user chooses the demo default, set MODEL_PATH=${REPO_ROOT}/.cache/hyperloom-models/Qwen3-14B-FP8 and download Qwen/Qwen3-14B-FP8 there when config.json is not already present.

Do not assume the Hugging Face CLI exists; resolve or download the selected model with Python:

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python -m pip install -U huggingface_hub
export REPO_ROOT="$(pwd -P)"
export MODEL_PATH="${MODEL_PATH:-${REPO_ROOT}/.cache/hyperloom-models/Qwen3-14B-FP8}"
python - <<'PY'
import os
from pathlib import Path
from huggingface_hub import snapshot_download

target = Path(os.environ["MODEL_PATH"]).expanduser()
if (target / "config.json").is_file():
    print(f"Using existing model at {target.resolve()}")
else:
    snapshot_download(
        repo_id="Qwen/Qwen3-14B-FP8",
        local_dir=str(target),
    )
print(target.resolve())
PY

Pre-launch Runtime Install

Before the first optimize launch, run the full runtime installer in the same environment that will launch the optimizer. Preflight loads kernel-agent.env.sh before it can reach the later Ray/Magpie/InferenceX auto-install checks, so this step must happen before launching.

For Docker mode, run this inside the container. For bare-metal mode, run it on the host:

Use the execution-shell preamble first, then run:

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load_dotenv_no_clobber
: "${USER_DATA_PATH:?USER_DATA_PATH missing}"
export USER_DATA_PATH
export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}"
ulimit -Sn 65536 || true
bash "$INSTALL_SH"

The optimizer's startup preflight loads kernel-agent.env.sh in process; do not source the generated file in the launch shell.

User-visible Progress

Keep the user informed with concise status updates throughout the demo. Do not dump full debug logs into chat; report the important values and paths so the user can tell that work is progressing.

Before launch, report the launch plan:

• model path and whether it is an existing local model or a downloaded default;
• run mode (baremetal or docker) and target host/container when applicable;
• framework, TP, concurrency, ISL, OSL, precision, max hours, and required demo flags;
• USER_DATA_PATH and where runtime artifacts will be written.

After the runtime install, report whether it succeeded and the path to kernel-agent.env.sh. After starting the optimizer, report:

• optimizer PID;
• run log path;
• launch-info JSON path;
• resolved session directory;
• state.json path;
• initial health check result.

On each requested status check, read persisted state and print a short summary. Use platform-scheduled invocations if recurring checks are requested; do not start a background watchdog, hold a blocking polling connection, or auto-resume. Busy logs alone are not evidence of useful progress. Include:

• process alive/stopped;
• phase and stop_reason;
• baseline throughput, current best throughput, and cumulative gain when present;
• latest benchmark result or candidate decision when available;
• the most relevant recent log lines, excluding secrets.

When the run finishes, report the final status, final report path, best result, and the stop reason. Never print API keys, tokens, or custom header values.

Launch Requirements

1. Run the pre-launch runtime install above; startup preflight loads the generated runtime environment in process.
2. Keep PYTHONPATH="$REPO_ROOT:${PYTHONPATH:-}" in the launch shell so critic subprocesses can import hyperloom.agents after changing cwd.
3. Run it detached the way the harness understands: if $CLAW_SESSION_ID is set and your bash tool takes a run_in_background parameter, hand the optimizer command to it with run_in_background=true, without shell-level detachment (setsid, nohup, or a trailing &); otherwise use setsid nohup ... &. See the Launch section of the packaged hyperloom/inference_optimizer/SKILL.md for why — a hand-detached run is invisible to Claw and its sandbox is reclaimed about fifteen minutes after the turn ends. In Docker mode that launch still runs inside one attached docker exec … bash -lc; never docker exec -d.
4. Pass all required optimize CLI flags in the python -m hyperloom optimize command. Do not rely on .env alone for TP, CONC, ISL, OSL, or PRECISION: the flag is the top of the ladder, while a value reaching the environment from .env is indistinguishable from one a previous run left there, and on --resume-from the session's recorded workload outranks it.
5. Include --phase-budget-framework-pct 0.43 and --phase-budget-kernel-pct 0.42 in the optimize command. Do not pass --no-framework-agent or --no-kernel — this demo runs the full OPTIMIZE phase (FRAMEWORK_AGENT + KERNEL_AGENT).
6. Report the session ID, log path, PID, and initial health check result.
7. Inspect persisted state on requested status checks; report when work stops.
8. After diagnosing an unexpected crash and obtaining explicit resume approval, only run optimize --resume-from "$SESSION_DIR" against the same session dir. After the first launch, never start a new optimize; that creates a new <UTC_ts> session and is forbidden.
9. If stop_reason in the current session state.json is final, stop and exit.

ATOM

ATOM is an AMD out-of-tree serving engine that Hyperloom launches as python3 -m atom.entrypoints.openai_server. The subsections below replace the generic execution shell, run mode, setup, runtime install and launch command for FRAMEWORK=atom; everything else above applies unchanged. ATOM is single-node (IR-8 in the optimizer skill). The KERNEL_AGENT phase runs GEAK, the same backend every other framework gets when the operator names none; preserve an explicit KERNEL_OPT_BACKEND_ORDER from the caller or .env rather than clearing it. For the per-kernel KernelForge backend use the 12h forge demo.

ATOM run mode

Follow the setup skill's Run Mode Resolution, shared with the vLLM/SGLang workflow. Reuse the user's HYPERLOOM_RUN_MODE selection (baremetal or docker) from setup, the caller, or .env. If no valid selection is available, ask the user to choose before setup, container creation, or launch. Do not default to either mode or infer a preference from the framework or a previous validation run.

• docker: run in an ATOM container on the selected development host.
• baremetal: run directly in that host's ATOM/ROCm Python environment. A development platform that is itself a container still counts as baremetal when no additional Docker container is started.

Export the selected HYPERLOOM_RUN_MODE in the execution shell and run only its matching entry below; both entries use the same setup, runtime install and launch steps.

ATOM execution shell

In the chosen Hyperloom workspace, use the shared loader with caller exports taking precedence. Repeat this preamble in each new execution shell, including inside Docker, with the selected HYPERLOOM_RUN_MODE exported. Keep USER_DATA_PATH unchanged; the loader handles readonly roots and Docker's host Python isolation without a new shell.

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set -e
export REPO_ROOT="$(pwd -P)"
INSTALL_SH="${REPO_ROOT}/hyperloom/inference_optimizer/assets/install.sh"
if [ ! -f "$INSTALL_SH" ]; then
  INSTALL_SH="${REPO_ROOT}/src/hyperloom/inference_optimizer/assets/install.sh"
fi
. "${INSTALL_SH%/*}/runtime_env.sh"
load_dotenv_no_clobber
export FRAMEWORK=atom
export PYTHONPATH="${REPO_ROOT}:${REPO_ROOT}/src:${PYTHONPATH:-}"

ATOM baremetal

After choosing direct execution, select the mode in that execution shell:

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export HYPERLOOM_RUN_MODE=baremetal

Activate the existing ATOM environment, or select its executable with PYTHON; if the environment has no ATOM yet, setup can install it (see ATOM Python and setup). Do not run any Docker commands.

ATOM Docker container

Only after the user selects HYPERLOOM_RUN_MODE=docker: use the approved HYPERLOOM_DOCKER_TARGET_HOST, or the current development host if unset. Do not create containers or run setup/optimize on a login host. Reuse completed setup only in the actual execution environment, not a different host Python.

Suggested MI355X image: docker.io/rocm/atom-dev:v0.1.7-rc0; preserve an explicit HYPERLOOM_IMAGE. Choose an image compatible with the target GPU. After approval, mount the workspace at the same absolute path. Add matching mounts for USER_DATA_PATH and any model directory outside the workspace:

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export HYPERLOOM_RUN_MODE=docker
export REPO_ROOT="$(pwd -P)"
export HYPERLOOM_IMAGE="${HYPERLOOM_IMAGE:-docker.io/rocm/atom-dev:v0.1.7-rc0}"
docker run -d \
  --name "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}" \
  --shm-size "${HYPERLOOM_SHM_SIZE:-64g}" \
  --entrypoint tail \
  --device /dev/kfd \
  --device /dev/dri \
  --group-add video \
  -v "$REPO_ROOT:$REPO_ROOT" \
  "$HYPERLOOM_IMAGE" \
  -f /dev/null

Enter the container and feed the ATOM steps below to Bash. This non-TTY form forwards exported selections by name without printing their values; use -it instead of -i for an interactive terminal. Add any other required shell-only credential/provider variables to the list. Mounted .env supplies missing values. Do not forward host PYTHON, PATH, or venv settings: select the container's existing ATOM environment after entry.

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set -e
_atom_container_env=()
for _atom_name in HYPERLOOM_RUN_MODE USER_DATA_PATH MODEL_PATH KERNEL_OPT_BACKEND_ORDER CLAW_SESSION_ID \
  FORGE_AGENT_BACKEND FORGE_AGENT_CLI CLAUDE_MODEL CODEX_MODEL HYPERLOOM_SKILL_PATH \
  ANTHROPIC_API_KEY ANTHROPIC_BASE_URL ANTHROPIC_AUTH_TOKEN OPENAI_API_KEY OPENAI_BASE_URL; do
  if printenv "$_atom_name" > /dev/null; then
    _atom_container_env+=(--env "$_atom_name")
  fi
done
unset _atom_name
docker exec -i -w "$REPO_ROOT" "${_atom_container_env[@]}" \
  "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}" bash

Inside that shell, repeat the ATOM execution shell preamble, then run the setup, runtime install and launch steps below. Repeat the preamble and Python selection for each new docker exec shell. Stop this session's container only with approval after the run finishes.

ATOM prior workload cleanup

Before replacement launches, follow IR-1 — Prior workload cleanup gate in the packaged optimizer skill, on the direct environment or Docker target as applicable. ATOM worker command lines can omit the server entrypoint. Identify this session's PIDs, process groups, ports and container before proposing cleanup; never broadly kill Python workers or restart the machine. Check GPU usage before GPU work:

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rocm-smi --showmemuse
pgrep -af "openai_server|spawn_main"

ATOM Python and setup

Activate the existing ATOM venv or select an explicit PYTHON; otherwise use python3 from PATH. Do not create a fresh venv or require /opt/venv.

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export PYTHON="${PYTHON:-$(command -v python3)}"
export INFERENCE_OPTIMIZER_FORCE_PYTHON=1

Setup checks the selected Python, ROCm torch and ATOM import/server readiness. Run its read-only check even when setup previously completed:

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"$PYTHON" -m hyperloom setup --check-only -- \
  --install-framework none --frameworks atom --require-frameworks \
  --user-data-path "${USER_DATA_PATH:?USER_DATA_PATH missing}"

Reuse successful setup in this environment. Only if setup is needed, explain its changes and obtain approval before running:

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"$PYTHON" -m hyperloom setup -- \
  --install-framework none --frameworks atom --require-frameworks \
  --user-data-path "${USER_DATA_PATH:?USER_DATA_PATH missing}" --yes

If the check reports ATOM missing in a baremetal environment and the operator approves installing it, run setup with atom instead of none. It installs AITER and ATOM from source into the selected Python, keeping its ROCm torch. Setup refuses when SGLang or vLLM also imports from that Python, because those engines would then load ATOM's plugins; report the refusal and propose a separate container for ATOM rather than removing the other engine. Do not do this in Docker mode, where ATOM comes from the image:

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"$PYTHON" -m hyperloom setup -- \
  --install-framework atom \
  --user-data-path "${USER_DATA_PATH:?USER_DATA_PATH missing}" --yes

none skips framework installation only: actual setup writes .env and may apply ROCm hotfixes; --yes is not user consent. Do not bypass base checks, repeat onboarding unnecessarily, or install SGLang/vLLM to compensate for missing ATOM.

For additional serving settings, use the optimizer's --server-args option. Do not launch a separate server or export EXTRA_ATOM_ARGS: Hyperloom materializes that transport variable. Keep the initial configuration untuned; do not copy another GPU's block/KV settings or the final settings of a previous optimization. Persist FRAMEWORK=atom in .env only when requested.

Resolve the model with the decision flow in Environment, in the actual execution environment, running its Python steps with the selected "$PYTHON". If huggingface_hub is missing, obtain approval before installing it with "$PYTHON" -m pip install huggingface_hub.

ATOM runtime install

Prepare runtime in the same environment as ATOM, using the existing installer. Reuse a prepared runtime; installation needs approval because it installs dependencies and may start services. If shell and setup-written .env disagree on USER_DATA_PATH, reconcile the selected root first: the installer treats setup's .env as authoritative. Never silently replace the artifact root.

Use the ATOM execution-shell preamble and selected Python above, then run:

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: "${USER_DATA_PATH:?USER_DATA_PATH missing}" "${PYTHON:?Select the existing ATOM Python first}"
export USER_DATA_PATH
ulimit -Sn 65536 || true
bash "$INSTALL_SH"

For every launch, including resume, the optimizer loads kernel-agent.env.sh in process. Startup preflight checks the selected framework. Do not source the generated runtime file in the shell. Readiness failures require diagnosis and approval for repairs, not a provider switch.

ATOM first launch

Use the packaged optimizer skill's Launch a New Optimization instructions to prepare RUN_LOG, PID_FILE, LAUNCH_INFO_FILE and run-scoped metadata in this execution environment. Keep the selected ATOM Python and backend for launch. After setup and runtime preparation, use this complete command for both modes. Do not replace workload flags with environment-only settings or add --no-framework-agent / --no-kernel:

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set -e
: "${PYTHON:?PYTHON missing}" "${MODEL_PATH:?MODEL_PATH missing}"
: "${RUN_LOG:?RUN_LOG missing}" "${LAUNCH_INFO_FILE:?LAUNCH_INFO_FILE missing}"
"$PYTHON" -m hyperloom optimize --verbose \
  --model "$MODEL_PATH" \
  --framework atom \
  --tp 1 --conc 64 --isl 1024 --osl 1024 \
  --precision fp8 \
  --target-gain 50 --max-hours 12 \
  --phase-budget-framework-pct 0.43 --phase-budget-kernel-pct 0.42 \
  --launch-info-file "$LAUNCH_INFO_FILE" \
  > "$RUN_LOG" 2>&1 < /dev/null

Detach through the existing harness-aware launch path: when CLAW_SESSION_ID is set and the Bash tool supports run_in_background, use it without shell-level detachment; otherwise prefix the optimize command with setsid nohup and append &. Follow the packaged skill's separate health check and launch-info/PID reconciliation; a shell wrapper PID is not the optimizer PID. Do not create a second launcher or watchdog for baremetal.

ATOM resume

After the first launch, never start another fresh optimize to recover a failed run. Diagnose it, obtain explicit approval, and pass --resume-from "$SESSION_DIR" for that same session instead of a new model launch. Repeat the ATOM execution-shell preamble and Python selection, retain phase fractions .43/.42, and follow the ATOM prior workload cleanup for any remaining ATOM workers before relaunching. Do not assume resume rewrites launch-info: verify the current process and session state rather than using an old PID. A final stop_reason ends the run; do not automatically restart it.

ATOM status checks

Report the chosen mode and Python together with the launch plan. Confirm the recorded framework and backend immediately after launch, not just the shell exports:

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grep -o '"framework": *"[^"]*"' "$SESSION_DIR/state.json"
grep -o '"kernel_optimizer": *"[^"]*"' "$SESSION_DIR/state.json"

Expect atom and geak (or the backend the operator explicitly kept). Report mismatches without silently replacing the session.