Rtvi Vlm Customize Model
NVIDIAOfficialCreated by NVIDIA CORPORATION
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a comprehensive procedure for updating the VLM configuration in the VSS Alerts Blueprint. It covers the three distinct VLM consumers—rtvi-vlm, vlm-as-verifier, and vss-agent—and details two primary deployment methods: using an OpenAI-compatible endpoint or running a vLLM-based model directly inside the RTVI container. The guide emphasizes correct port wiring, environment variable management, and verification steps to ensure all components are correctly synchronized.
What it does
- Explains how to repoint the VSS Alerts Blueprint to different VLM endpoints.
- Details the configuration of three separate VLM consumers: rtvi-vlm, vlm-as-verifier, and vss-agent.
- Provides instructions for both OpenAI-compatible and in-container vLLM deployment methods.
- Outlines necessary port and URL wiring requirements for local deployments.
- Includes health check procedures to verify model readiness and inference capabilities.
- Provides guidance on handling model identity differences between deployment methods.
When to use it
- When you need to change the VLM endpoint used by the VSS Alerts Blueprint.
- When you want to run rtvi-vlm as a standalone service.
- When you need to troubleshoot configuration mismatches between the verifier, agent, and RTVI service.
- When you are setting up a new VLM model and need to ensure all consumers are correctly aligned.
Advantages
- Ensures consistent configuration across all VLM-dependent services.
- Provides clear, actionable steps for both external and internal model hosting.
- Includes robust verification commands to confirm successful deployment.
- Helps avoid common pitfalls like stale credentials or port mismatches.
Usage examples
- “Point the VSS Alerts Blueprint at a host-side NIM on http://host.docker.internal:30082 for all RTVI-VLM calls.”
- The user updates the .env file with the specified endpoint and model name, then reruns the profile to apply the changes to all three VLM consumers.
- “Run rtvi-vlm standalone with Qwen3-VL-8B-Instruct served inside the container.”
- The user configures the standalone Compose file with the vllm-compatible method and the correct model path, ensuring the container handles the model loading.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/rtvi-vlm-customize-model/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip rtvi-vlm-customize-model.zip -d .agents/skills
Documentation by NVIDIA CORPORATION
VLM Customization — VSS Alerts Blueprint
When to use
Use this skill when the user wants to:
repoint the VSS Alerts Blueprint to a different VLM endpoint,
run
rtvi-vlmstandalone with either an OpenAI-compatible endpoint or the in-container vLLM path,fix the assumption that changing one VLM config automatically updates
rtvi-vlm,vlm-as-verifier, andvss-agent.
Do not use this skill for CV detector swaps inside vss-rt-cv; use rtvi-cv-customize-model for those.
Instructions
Treat
rtvi-vlm,vlm-as-verifier, andvss-agentas three separate VLM consumers. Do not imply that changing onlyRTVI_VLM_*automatically repoints the verifier or the agent UI.Treat all paths in this skill as relative to a checkout of the public VSS Blueprint repository. Blueprint deployment files live under
deploy/docker/...; customer-accessible RT-VLM source and standalone deployment files live underservices/rtvi/rt-vlm/.... None of these paths resolve inside the DeepStream repository.For blueprint-side OpenAI-compatible routing, cover all three surfaces:
RTVI_VLM_*env vars,${VSS_PROFILE_DIR}/vlm-as-verifier/configs/config.yml, and thevss-agentVLM_MODEL_TYPE/VLM_NAME/VLM_BASE_URLsettings, then mention force-recreate plus log checks.For standalone
vllm-compatible, setVLM_MODEL_TO_USE=vllm-compatible, pointMODEL_PATHat the weights source, mentionHF_TOKENonly when the model actually needs auth. Do not treat a log line as proof of a working deployment: v3.2.1 logsWarmup VlmProcess-0 doneeven when warm-up raised an exception. Require the absence of anError during warmupline, readiness, and one successful/v1/chat/completionsresponse.Never paste credentials into chat or logs; capture with a silent prompt (
read -rsp),chmod 600the env file, and never commitgenerated.env.
Examples
"Point the VSS Alerts Blueprint at a host-side NIM on
http://host.docker.internal:30082for all RTVI-VLM calls.""Run
rtvi-vlmstandalone with Qwen3-VL-8B-Instruct served inside the container.""I changed only
RTVI_VLM_ENDPOINTingenerated.env. That should repointvlm-as-verifierandvss-agenttoo, right?"
Source locations
This skill is documentation-only — no VSS sources ship here. Use a VSS v3.2.1 or compatible checkout and run every command from its root. Clone/LFS, Alerts profile paths, RT-VLM tree, pinned images, and VSS_* vars: references/vss-source-layout.md.
Which modes this applies to
| Mode flag | Workflow | VLM role |
|---|---|---|
--mode real-time (2d_vlm) | Real-time VLM alerts | Primary — every alert is VLM-driven |
--mode verification (2d_cv) | CV + VLM verification | Verifier — VLM confirms each CV incident |
VLM customization applies to both modes. Three services each make their own VLM calls with separate configuration:
| Service | When used | Config location |
|---|---|---|
| rtvi-vlm | Real-time alert generation; rtvi_vlm_alert tool calls from agent UI | RTVI_VLM_* vars in ${VSS_PROFILE_DIR}/.env / generated.env |
vlm-as-verifier (alert-bridge) | Post-processing: confirms each mdx-incidents event (2d_cv only) | ${VSS_PROFILE_DIR}/vlm-as-verifier/configs/config.yml |
| vss-agent | Interactive agent UI queries | VLM_MODEL_TYPE, VLM_BASE_URL, VLM_NAME in ${VSS_PROFILE_DIR}/.env |
All three can point at the same model endpoint — they don't have to.
alert-bridge and vss-agent are closed-source; their pinned images (and the
rtvi-vlm image tag) are listed in
references/vss-source-layout.md.
RTVI-VLM: Two Deployment Methods
The implementation is selected by VLM_MODEL_TO_USE (standalone) or
RTVI_VLM_MODEL_TO_USE (VSS blueprint).
Method A: OpenAI-Compatible Endpoint (openai-compat)
The RTVI container makes HTTP calls to an external inference server; it does not load the model itself. This covers local or remote NIM, external vLLM, and OpenAI.
Method B: vLLM Inside the RTVI Container (vllm-compatible)
The container downloads and serves the model using its bundled vLLM engine. No external inference server is needed.
Hard constraint: The model architecture must be supported by the vLLM
version shipped in the selected RTVI image. Check the supported-model guidance
in services/rtvi/rt-vlm/README.md. If the model requires a newer vLLM, use Method A with an external container instead.
Configuring RTVI-VLM via VSS Blueprint
The VSS blueprint .env / generated.env maps most RTVI_VLM_* variables to container-native names. The model or deployment identifier is the important exception: v3.2.1 maps VLM_NAME to VIA_VLM_OPENAI_MODEL_DEPLOYMENT_NAME. RTVI_VLM_PORT is also exceptional: it is required by Compose to publish the service and must be explicitly supplied when generated.env does not contain it.
Required port configuration — all Blueprint workflows
Add RTVI_VLM_PORT and update the two hardcoded 8018 lines in the Alerts
profile .env, then rerun dev-profile.sh. Edit .env, not generated.env —
the generator overwrites it.
# ${VSS_PROFILE_DIR}/.env
RTVI_VLM_PORT=8018 # add this line
RTVI_VLM_BASE_URL=http://${HOST_IP}:${RTVI_VLM_PORT} # update: was http://${HOST_IP}:8018
RTVI_VLM_ENDPOINT=http://${HOST_IP}:${RTVI_VLM_PORT}/v1 # update: was http://${HOST_IP}:8018/v1Keep the value at 8018 unless that port is unavailable. Variable ownership,
the VLM_BASE_URL generator overwrite, and the patch required for any other
port are in
references/port-and-url-wiring.md.
Method A — OpenAI-compatible endpoint
In the v3.2.1 blueprint Compose file, these host variables map as follows:
| Blueprint variable | Container variable |
|---|---|
RTVI_VLM_MODEL_TO_USE | VLM_MODEL_TO_USE |
RTVI_VLM_ENDPOINT | VIA_VLM_ENDPOINT |
RTVI_VLM_API_KEY | VIA_VLM_API_KEY |
VLM_NAME | VIA_VLM_OPENAI_MODEL_DEPLOYMENT_NAME |
RTVI_VLM_MODEL_PATH | MODEL_PATH |
After applying the shared port configuration above, configure the active profile environment:
# ${VSS_PROFILE_DIR}/.env
RTVI_VLM_MODEL_TO_USE=openai-compat
RTVI_VLM_ENDPOINT=http://host.docker.internal:30082/v1
VLM_NAME=<model-or-deployment-id>
OPENAI_API_KEY=<your-api-key> # client fallback/default credential
RTVI_VLM_API_KEY=<your-api-key> # mapped to preferred VIA_VLM_API_KEY
# RTVI_VLM_IMAGE_TAG=3.2.1 # pin to a specific image tag; defaults to 3.2.1For an authenticated Method A endpoint, set OPENAI_API_KEY and
RTVI_VLM_API_KEY to the same endpoint-specific credential. The stock Compose
file maps RTVI_VLM_API_KEY to VIA_VLM_API_KEY, and the OpenAI-compatible
client prefers VIA_VLM_API_KEY over OPENAI_API_KEY. Leave both unset only
when the endpoint intentionally accepts unauthenticated requests.
Examples:
# NIM on the Docker host. Stock Compose maps this name through host-gateway.
RTVI_VLM_ENDPOINT=http://host.docker.internal:30082/v1
VLM_NAME=nvidia/cosmos-reason2-8b
# NVIDIA API catalog (set keys via silent prompt)
RTVI_VLM_ENDPOINT=https://integrate.api.nvidia.com/v1
VLM_NAME=nvidia/cosmos-reason2-8b
OPENAI_API_KEY=<your-nvidia-api-key>
RTVI_VLM_API_KEY=<your-nvidia-api-key>
# OpenAI (set keys via silent prompt)
RTVI_VLM_ENDPOINT=https://api.openai.com/v1
VLM_NAME=gpt-4o
OPENAI_API_KEY=<your-openai-api-key>
RTVI_VLM_API_KEY=<your-openai-api-key>Migrating from Method B: Replace
RTVI_VLM_API_KEYbefore recreatingrtvi-vlm; do not leave the previous NGC credential in that variable. IfRTVI_VLM_API_KEYis empty, stock Compose falls back toNGC_CLI_API_KEYwhen populatingVIA_VLM_API_KEY. Because the client prefersVIA_VLM_API_KEY, a stale NGC key would be sent to the new Method A endpoint instead ofOPENAI_API_KEY. This is both an authentication failure and a credential-disclosure risk.
RTVI_VLM_OPENAI_MODEL_DEPLOYMENT_NAME is not consumed by the public v3.2.1
blueprint Compose file. Use VLM_NAME unless the deployed Compose file has
been intentionally customized.
Verify the configured model identifier from inside the RTVI container. The
conditional Authorization header keeps keyless local endpoints working while
authenticating endpoints such as OpenAI and the NVIDIA API Catalog. Endpoints
may advertise multiple models, so confirm the configured ID appears anywhere in
the /models response:
ALERTS_ENV=developer-profiles/dev-profile-alerts/generated.env
# Container-native name for `VLM_NAME`.
EXPECTED_MODEL="$(docker compose --env-file "${ALERTS_ENV}" exec -T rtvi-vlm \
printenv VIA_VLM_OPENAI_MODEL_DEPLOYMENT_NAME | tr -d '\r')"
test -n "${EXPECTED_MODEL}"
docker compose --env-file "${ALERTS_ENV}" exec -T rtvi-vlm sh -lc \
'set --
if [ -n "${VIA_VLM_API_KEY:-}" ]; then
set -- -H "Authorization: Bearer ${VIA_VLM_API_KEY}"
fi
curl -fsS "$@" "${VIA_VLM_ENDPOINT%/}/models"' \
| jq -e --arg model "${EXPECTED_MODEL}" 'any(.data[]; .id == $model)' \
|| { echo "Endpoint does not advertise '${EXPECTED_MODEL}'" >&2; exit 1; }jq -e exits non-zero when the assertion is false, so the check passes only
when the configured model appears somewhere in the list. To see which models
the endpoint offers, drop the jq filter and read the raw response.
Treat HTTP 401 from this request as an authentication failure. Only treat a
successful response that fails the jq assertion as a model-name mismatch.
Method B — vLLM inside RTVI container
RTVI_VLM_MODEL_TO_USE=vllm-compatible
RTVI_VLM_MODEL_PATH=git:https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct
# With HuggingFace token:
# HF_TOKEN=<your-hf-token>
# From NGC:
# RTVI_VLM_MODEL_PATH=ngc:nim/nvidia/cosmos-reason2-8b:hf-1208MODEL_PATH prefix conventions (see public
services/rtvi/rt-vlm/src/vlm_pipeline/ngc_model_downloader.py):
ngc:<registry>/<org>/<model>:<version>— download from NGCgit:<url>— clone from HuggingFace or any git LFS repo/path/to/local/dir— use pre-downloaded local weights
A bad MODEL_PATH aborts startup during download, before the server binds its
port, so the container exits non-zero. v3.2.1 raises a plain Exception:
Failed to download model <name> from <url> for git: paths, and
Model download failed with status code <code>, Could not authenticate with NGC.,
or Could not find the model. for ngc: paths.
Configuring RTVI-VLM Standalone
When running services/rtvi/rt-vlm/docker/compose.yaml from the public VSS checkout, use the container-native names:
# services/rtvi/rt-vlm/docker/.env
BACKEND_PORT=8000
# Method A
VLM_MODEL_TO_USE=openai-compat
VIA_VLM_ENDPOINT=http://host.docker.internal:30082/v1
VIA_VLM_OPENAI_MODEL_DEPLOYMENT_NAME=nvidia/cosmos-reason2-8b
VIA_VLM_API_KEY=<your-api-key> # if required; set via silent prompt
# Method B
VLM_MODEL_TO_USE=vllm-compatible
MODEL_PATH=git:https://huggingface.co/Qwen/Qwen3-VL-8B-InstructThe standalone Compose file defaults to nvcr.io/nvidia/vss-core/vss-rt-vlm:3.2.1 and requires BACKEND_PORT to be set. All supported VLM_MODEL_TO_USE values: openai-compat, vllm-compatible, cosmos-reason1, cosmos-reason2, cosmos-reason3, custom.
Both stock Compose definitions use bridged networking and map
host.docker.internal through host-gateway. Choose the endpoint host by
where the inference server runs:
Docker host:
host.docker.internalSame Compose network: the inference server's Compose service name
Remote machine: a routable hostname or IP address
localhost: only when the RTVI container explicitly uses host networking
For standalone Method A, verify connectivity from inside rtvi-server:
docker compose exec -T rtvi-server sh -lc \
'set --
if [ -n "${VIA_VLM_API_KEY:-}" ]; then
set -- -H "Authorization: Bearer ${VIA_VLM_API_KEY}"
fi
curl -fsS "$@" "${VIA_VLM_ENDPOINT%/}/models"'See the environment-variable reference in public
services/rtvi/rt-vlm/README.md.
Configuring vlm-as-verifier (2d_cv mode only)
vlm-as-verifier runs as the alert-bridge service and is separate from
RTVI-VLM. Its config is bind-mounted from
${VSS_PROFILE_DIR}/vlm-as-verifier/configs/config.yml.
Keep the config parameterized:
# ${VSS_PROFILE_DIR}/vlm-as-verifier/configs/config.yml
vlm:
base_url: ${VLM_BASE_URL}/v1
model: ${VLM_NAME}
max_tokens: 4096For the v3.2.1 local-VLM Alerts flow, dev-profile.sh --vlm-device-id ...
populates VLM_BASE_URL with http://<host-ip>:8018 and sets VLM_NAME in
the generated environment. Remote mode likewise writes the selected endpoint.
Only hardcode these fields when deliberately overriding the profile-generated
values.
The 8018 here is literal in the generator, not derived from
RTVI_VLM_PORT. If RTVI-VLM is published on any other port, alert-bridge
still dials 8018 until you patch VLM_BASE_URL in generated.env
(references/port-and-url-wiring.md).
After editing the config or environment, recreate the service:
cd "${VSS_DEPLOY_DIR}"
docker compose \
--env-file developer-profiles/dev-profile-alerts/generated.env \
up -d --force-recreate alert-bridgeConfiguring vss-agent
vss-agent is another separate consumer. The v3.2.1 Alerts profile selects a
block in ${VSS_PROFILE_DIR}/vss-agent/configs/config.yml using
VLM_MODEL_TYPE. The local RTVI-VLM flow uses rtvi:
rtvi_vlm: # VLM_MODEL_TYPE=rtvi
base_url: ${RTVI_VLM_BASE_URL}/v1
nim_vlm: # VLM_MODEL_TYPE=nim
base_url: ${VLM_BASE_URL}/v1
openai_vlm: # VLM_MODEL_TYPE=openai
base_url: ${VLM_BASE_URL}/v1
vllm_vlm: # VLM_MODEL_TYPE=vllm
base_url: ${VLM_BASE_URL}/v1For a v3.2.1 local Alerts deployment, keep the generated values, which are normally equivalent to:
VLM_MODEL_TYPE=rtvi
VLM_NAME=<model-id>
RTVI_VLM_PORT=8018 # from .env
RTVI_VLM_BASE_URL=http://<host-ip>:${RTVI_VLM_PORT} # from .env
VLM_BASE_URL=http://<host-ip>:8018 # written by dev-profile.shRTVI_VLM_PORT is required by the rtvi-vlm Compose port mapping and has no
Compose default. Define it in the Alerts profile .env and regenerate
generated.env before using any of these Compose commands. The two URLs have
different owners and agree only at the default port; see
references/port-and-url-wiring.md.
For a remote NIM, OpenAI-compatible endpoint, or external vLLM, select the
matching nim, openai, or vllm profile and set VLM_BASE_URL without a
trailing /v1 because the config appends it. Recreate vss-agent after
changing these values.
Health Checks
After changing VLM configuration, recreate the affected service and inspect it through the same Alerts Compose model:
cd "${VSS_DEPLOY_DIR}"
ALERTS_ENV=developer-profiles/dev-profile-alerts/generated.env
# Required when an existing `generated.env` does not yet contain the port.
# The persistent fix is to add it to the profile `.env` and regenerate.
export RTVI_VLM_PORT=8018
# Recreate RTVI-VLM and wait up to 10 minutes for its Compose healthcheck.
docker compose --env-file "${ALERTS_ENV}" \
up -d --force-recreate --wait --wait-timeout 600 rtvi-vlm
# The container must still be running. A failed weights download exits here.
CID="$(docker compose --env-file "${ALERTS_ENV}" ps -aq rtvi-vlm)"
test "$(docker inspect -f '{{.State.Status}}' "${CID}")" = running || {
docker logs --tail 50 "${CID}" >&2; exit 1; }
# Require the absence of a download or warm-up failure. `Warmup ... done` is
# logged on both paths in v3.2.1, so only the error lines are conclusive.
docker logs "${CID}" 2>&1 | grep -Eq \
'Failed to download model|Model download failed|Could not find the model|Could not authenticate with NGC|Error during (model )?warmup' \
&& { echo 'rtvi-vlm failed to download weights or warm up' >&2; exit 1; }
# Confirm the readiness endpoint from inside the container.
docker compose --env-file "${ALERTS_ENV}" exec -T rtvi-vlm \
curl -fsS http://localhost:8000/v1/health/ready
# Capture the implementation and the model ID advertised by the live service.
VLM_METHOD="$(docker compose --env-file "${ALERTS_ENV}" exec -T rtvi-vlm \
printenv VLM_MODEL_TO_USE | tr -d '\r')"
ACTUAL_MODEL="$(docker compose --env-file "${ALERTS_ENV}" exec -T rtvi-vlm \
curl -fsS http://localhost:8000/v1/models | jq -er '.data[0].id')"
test -n "${ACTUAL_MODEL}"
# Readiness and /v1/models only report metadata. Require one real inference; a
# text-only chat request needs no media asset and works on both methods.
docker compose --env-file "${ALERTS_ENV}" exec -T rtvi-vlm \
curl -fsS --max-time 120 -X POST http://localhost:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d "$(jq -nc --arg m "${ACTUAL_MODEL}" '{model: $m, max_tokens: 16, stream: false,
messages: [{role: "user", content: "Reply with: ok"}]}')" \
| jq -e '((.choices[0].message.content // "") | length) > 0' > /dev/null \
|| { echo 'inference smoke test returned no content' >&2; exit 1; }Readiness is built from is_alive() per process, so it reports healthy: true
for a live process whose model never warmed up, and /v1/chat/completions
returns HTTP 200 with empty content when the pipeline produced no output. The
jq assertion on non-empty content is what makes the last check meaningful.
Model identity differs by method
The two methods advertise their model ID differently, so the follow-up check is not the same:
Method A (
openai-compat) — verify that the configuredVIA_VLM_OPENAI_MODEL_DEPLOYMENT_NAMEappears anywhere in the/v1/modelslist using theany(.data[]; .id == $model)check from the Method A endpoint verification section. Treat HTTP 401 as an authentication failure; treat a 200 response where the model is absent as a model-name mismatch.Method B (
vllm-compatible) — do not compare the advertised ID againstVLM_NAMEorVIA_VLM_OPENAI_MODEL_DEPLOYMENT_NAME. The resolved model directory basename becomes the advertised/v1/modelsID, songc:nim/nvidia/cosmos-reason2-8b:hf-1208is served asnim_nvidia_cosmos-reason2-8b_hf-1208. That difference is expected for Method B and does not indicate a deployment failure. Point downstream consumers (vlm-as-verifier,vss-agent) at that advertised ID, never at the NGC or Hugging Face path.
Run the per-method verification commands from references/health-checks.md — covers the Method A identity check, the Method B inspection, warm-up diagnostics, and querying a specific external endpoint.
Files
- .gitkeep0 B
- ATTRIBUTION.mdAdded by AutoGineer976 B
- BENCHMARK.md4.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md19.5 KiB
- evals/evals.json12.3 KiB
- references/health-checks.md5.1 KiB
- references/port-and-url-wiring.md2.5 KiB
- references/vss-source-layout.md3.6 KiB
- skill-card.md4.4 KiB
- skill.oms.sig5.4 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs network access, Needs an API key, Needs a command-line tool
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA CORPORATION
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/rtvi-vlm-customize-model · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA CORPORATION or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download rtvi-vlm-customize-model.zip