Generated banner for Tao Finetune Cosmos Embed
Generated logo for Tao Finetune Cosmos Embed

Tao Finetune Cosmos Embed

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Medium risk

From the author

Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "search videos with text".
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The tao-finetune-cosmos-embed skill provides a structured interface for interacting with the Cosmos-Embed1 model. It supports training, evaluation, inference, and model export (ONNX or HuggingFace format). Designed for video-text retrieval and semantic search, this skill handles the containerized execution of the model, including configuration for LoRA-based parameter-efficient fine-tuning and data preparation for MSR-VTT datasets.

What it does

  • Fine-tunes Cosmos-Embed1 models using LoRA for parameter-efficient training.
  • Performs text-to-video and video-to-video retrieval and search.
  • Exports trained models to ONNX or HuggingFace formats for deployment.
  • Evaluates model performance on video-text datasets.
  • Handles semantic deduplication of video content.
  • Manages containerized execution with specific environment and dependency requirements.

When to use it

  • When you need to fine-tune the Cosmos-Embed1 model on custom video datasets.
  • When performing text-to-video retrieval or video-to-video search tasks.
  • When you need to export a trained model for production deployment.
  • When you need to perform semantic deduplication on a video library.

Advantages

  • Provides a standardized CLI for complex video-text embedding tasks.
  • Supports efficient fine-tuning via LoRA integration.
  • Includes pre-configured templates for training, evaluation, and export.
  • Facilitates seamless model conversion to ONNX and HuggingFace formats.

Usage examples

“Fine-tune Cosmos-Embed1 on my video dataset.”
The agent will execute the training command using the provided training specification and local data mount.
“Run inference to search for videos matching 'a man is singing on stage'.”
The agent will trigger the inference action, returning the top-k results based on the provided text query.
“Export my trained Cosmos-Embed1 model to ONNX.”
The agent will run the export command, converting the specified checkpoint into an ONNX file for deployment.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-finetune-cosmos-embed/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-finetune-cosmos-embed.zip -d .agents/skills

Documentation by NVIDIA Corporation

Cosmos-Embed

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Cosmos-Embed1 is a joint video-text embedder for text-to-video retrieval, video-to-video search, zero-shot/kNN classification, and semantic deduplication. The packaged CLI is cosmos-embed1 and supports train, evaluate, inference, and export.

Container image and per-action commands are in references/skill_info.yaml. Compact starting specs are in references/spec_template_*.yaml.

Train Action Policy

AutoML is not packaged for this model skill because there are no Cosmos-Embed schemas under schemas/. Always use the direct model skill actions for train, evaluate, inference, and export, even when a higher-level request includes automl_policy: on. Do not route Cosmos-Embed through workflow or AutoML skills until model-specific train schemas and templates are added.

Non-train actions such as evaluate, inference, export, and deploy flows stay in this model skill. The per-run automl_policy override does not change model metadata.

Quick Start

Use the published Cosmos-Embed container pinned below (also declared in references/skill_info.yaml). Do not build from the private Cosmos-Embed1 source tree for normal skill use; build from source only when developing the container itself.

COSMOS_EMBED_IMAGE_DEFAULT=nvcr.io/nvidia/tao/tao-toolkit:7.3.0-cosmos-embed  # versions-key: images.tao_toolkit.cosmos_embed
COSMOS_EMBED_IMAGE="${COSMOS_EMBED_IMAGE:-$COSMOS_EMBED_IMAGE_DEFAULT}"
docker pull "$COSMOS_EMBED_IMAGE"

Expected local workspace layout:

workspace/
├── data/
│   ├── msrvtt_test_1k.json
│   └── video/
│       ├── video7020.mp4
│       └── ...
├── model/
│   └── Cosmos-Embed1-224p/        # optional if using HF repo id
├── specs/
│   ├── train.yaml
│   ├── evaluate.yaml
│   ├── inference.yaml
│   ├── export_onnx.yaml
│   └── export_hf.yaml
└── results/

Use these Docker options for all actions unless the local Docker/platform skill gives a stricter environment-specific command:

set -a; source /path/to/.env; set +a   # omit if already exported
COSMOS_EMBED_IMAGE_DEFAULT=nvcr.io/nvidia/tao/tao-toolkit:7.3.0-cosmos-embed  # versions-key: images.tao_toolkit.cosmos_embed
COSMOS_EMBED_IMAGE="${COSMOS_EMBED_IMAGE:-$COSMOS_EMBED_IMAGE_DEFAULT}"
RUN_ROOT="${RUN_ROOT:-$PWD}"
COSMOS_EMBED_PRETRAINED_CACHE="${COSMOS_EMBED_PRETRAINED_CACHE:-$RUN_ROOT/pretrained_checkpoints}"
mkdir -p "$COSMOS_EMBED_PRETRAINED_CACHE"
DOCKER_COMMON=(
  --rm --gpus all --shm-size=8g --network=host
  --shm-size=64g
  --ulimit memlock=-1
  --ulimit stack=67108864
  -w /results
  -e HF_TOKEN
  -e WANDB_DISABLED=true
  -e WANDB_MODE=disabled
  -e HUGGINGFACE_HUB_CACHE=/hf_cache
  -v "$RUN_ROOT/data:/data:ro"
  -v "$RUN_ROOT/model:/model"
  -v "$RUN_ROOT/specs:/specs:ro"
  -v "$RUN_ROOT/results:/results"
  -v "$RUN_ROOT/hf_cache:/hf_cache"
  --mount "type=bind,source=$COSMOS_EMBED_PRETRAINED_CACHE,target=/usr/local/lib/python3.12/dist-packages/pretrained_checkpoints"
)

The pinned 7.1.0 image downloads the BERT Q-Former component under /usr/local/lib/python3.12/dist-packages/pretrained_checkpoints, even when Hugging Face cache variables point elsewhere. The dedicated bind mount above must be writable by the container UID selected by the platform skill; create it as the launching user before submission. Keep this mount when applying the platform's non-root UID/GID and writable HOME settings. It permits normal runtime downloads without modifying the image or making all of site-packages writable. For another image version, verify its cache location before reusing this image-specific destination.

Use a writable per-run results directory as the container working directory. With a cold Triton cache in this image, launching from a directory containing specs/ can make GCC read ./specs as a compiler spec file and fail with cannot read spec file './specs': Is a directory. Keep the inference spec's absolute results_dir unchanged; changing the working directory is sufficient.

For Cosmos-Embed images that ship protobuf==7.x, run a small startup preamble before every action:

python -m pip install "protobuf<7"

The image contains wandb==0.21.0 with protobuf==7.x; importing W&B fails before training/evaluation unless protobuf is pinned below 7. Use WANDB_DISABLED=true and WANDB_MODE=disabled for smoke or offline runs. Cosmos-Embed may still download the public google-bert/bert-base-uncased Q-Former component even when the model checkpoint is disabled, so pass HF_TOKEN as an environment variable or mount a persistent HuggingFace cache. Do not write the token into specs, logs, or reports.

Train:

docker run "${DOCKER_COMMON[@]}" "$COSMOS_EMBED_IMAGE" \
  bash -lc "python -m pip install 'protobuf<7' && cosmos-embed1 train -e /specs/train.yaml results_dir=/results"

Evaluate:

docker run "${DOCKER_COMMON[@]}" "$COSMOS_EMBED_IMAGE" \
  bash -lc "python -m pip install 'protobuf<7' && cosmos-embed1 evaluate -e /specs/evaluate.yaml results_dir=/results"

Inference:

docker run "${DOCKER_COMMON[@]}" "$COSMOS_EMBED_IMAGE" \
  bash -lc "python -m pip install 'protobuf<7' && cosmos-embed1 inference -e /specs/inference.yaml \
  'inference.query.input_texts=[\"a man is singing on stage\"]' \
  inference.k=5 \
  results_dir=/results"

Export ONNX:

docker run "${DOCKER_COMMON[@]}" "$COSMOS_EMBED_IMAGE" \
  bash -lc "python -m pip install 'protobuf<7' && cosmos-embed1 export -e /specs/export_onnx.yaml \
  export.checkpoint=/results/train/checkpoints/iter_000000001.pt \
  export.onnx_file=/results/export/cosmos_embed1_combined.onnx \
  results_dir=/results"

Export HuggingFace format:

docker run "${DOCKER_COMMON[@]}" "$COSMOS_EMBED_IMAGE" \
  bash -lc "python -m pip install 'protobuf<7' && cosmos-embed1 export -e /specs/export_hf.yaml \
  export.checkpoint=/results/train/checkpoints/iter_000000001.pt \
  export.hf_output_dir=/results/export_hf/cosmos_embed1_hf \
  results_dir=/results"

Smoke Overrides

For a small functional check, keep the same specs and override the expensive knobs:

train.max_iter=1
train.validation_iter=2
train.checkpoint_iter=1
train.optim.optim=adamw
train.optim.warmup_steps=0
train.optim.lr_decay_iters=1
dataset.train_dataset.batch_size=1
dataset.val_dataset.batch_size=1
dataset.train_dataset.workers=0
dataset.val_dataset.workers=0

When shortening the cosine scheduler for smoke runs, keep train.optim.lr_decay_iters greater than train.optim.warmup_steps, or set train.optim.warmup_steps=0 as shown above. The scheduler divides by lr_decay_iters - warmup_steps, so equal values fail before the checkpoint is written.

If no local Cosmos-Embed1 pretrained checkpoint is available, set model.pretrained_model_path=null for a plumbing-only smoke train. The model quality is meaningless in that mode, but the train/evaluate/inference/export action paths can still be exercised. In the current container, the Q-Former path can still fetch google-bert/bert-base-uncased; provide HF_TOKEN or a mounted HuggingFace cache for fresh ephemeral containers.

For evaluation and inference smoke tests on a tiny subset:

evaluate.callbacks.embedding_visualization=false
evaluate.callbacks.max_eval_samples=8
dataset.test_dataset.batch_size=1
dataset.test_dataset.workers=0
inference.k=2
dataset.inference_dataset.batch_size=1
dataset.inference_dataset.workers=0

Data Format

The MSR-VTT path expects a local video glob and a JSON metadata file:

dataset:
  train_dataset:
    dataset_type: msrvtt
    mp4_urls: /data/video/*.mp4
    metadata: /data/msrvtt_test_1k.json

List-format metadata rows must include at least video and caption:

{"video_id": "video7020", "video": "video7020.mp4", "caption": "a woman creating a fondant baby and flower"}

The dataset loader derives the video id from the local .mp4 filename and filters to videos present in the metadata. If a run finds zero videos, check that mp4_urls points to a container-local glob and that metadata video names match the filenames.

Model Weights

  • Local HF directory: mount it under /model and set model.pretrained_model_path=/model/Cosmos-Embed1-224p.

  • HuggingFace repo: set model.pretrained_model_path=nvidia/Cosmos-Embed1-224p and pass HF_TOKEN if access is gated.

  • Fine-tuned checkpoint: set downstream actions to the resolver-selected /results/train/checkpoints/iter_#########.pt file.

Training writes full checkpoints under results/train/checkpoints/iter_#########.pt, updates results/train/checkpoints/latest_checkpoint.txt, and creates a cosmos_embed1_model_latest.pth symlink. For evaluate.checkpoint, inference.checkpoint, export.checkpoint, and train.resume_training_checkpoint_path, resolve and pass the exact iter_#########.pt file for the intended iteration. The action spec templates intentionally leave these checkpoint fields null so the model-skill runner or the user must provide the resolver-selected checkpoint. Use the latest symlink only when the user explicitly asks for latest.

For single-GPU resume/retrain from a consolidated checkpoint, set model.fsdp_shard_size: 1. The container default is 8, which sends resumed training through an FSDP apply path that Cosmos-Embed1 does not implement for this model class.

Variants:

VariantResolutionFramesEmbedding dim
Cosmos-Embed1-224p224 x 2248256
Cosmos-Embed1-336p336 x 3368768
Cosmos-Embed1-448p448 x 4488768

Keep model.network.embed_dim, model.input_hw, and model.network.spatial_resolution aligned with the selected variant.

Important Parameters

ParameterNotes
train.num_gpus1 for single GPU, >1 auto-launches torchrun, -1 auto-detects visible GPUs.
train.max_iterMain training length. Use 1 only for smoke testing.
train.optim.optimfused_adamw is faster when available; adamw is safer for smoke and portability.
model.lora.enabledEnables LoRA. Set model.network.visual_encoder.transformer_engine=false when LoRA is on.
model.lora.lora_rankLoRA rank. Start with 8; try 4, 8, or 16 for manual or AutoML-style sweeps.
model.lora.lora_alphaLoRA scaling factor. Start with 16; keep near 2 * lora_rank unless experiments show otherwise.
model.lora.lora_dropoutLoRA dropout. Start with 0.1; sweep 0.0, 0.05, and 0.1 for small datasets.
model.lora.biasBias policy: none, all, or lora_only. Keep none unless intentionally training biases.
model.lora.use_rslora / use_doraOptional LoRA variants. Enable one at a time and record the setting with the checkpoint.
model.lora.target_modulesOptional module-name patterns for LoRA injection. Leave empty for the default ViT + Q-Former attention/MLP targets.
model.lora.modules_to_saveOptional modules to keep fully trainable alongside LoRA. Leave empty unless preserving a task-specific head.
evaluate.load_dataset_pkl / save_dataset_pklCache evaluation embeddings.
inference.load_dataset_pkl / save_dataset_pklCache the search database for repeated retrieval.
export.modevideo, text, combined, or huggingface.
export.on_cpuRecommended for export to avoid device mismatch issues.
LoRA and AutoML Notes

For parameter-efficient fine-tuning, set model.lora.enabled=true and keep model.network.visual_encoder.transformer_engine=false; TAO Core's Cosmos-Embed1 config notes that PEFT cannot inject adapters into Transformer Engine layers. Treat the LoRA fields above as the first candidate parameters for manual tuning or AutoML-style search before unfreezing larger model blocks. Avoid changing target_modules or modules_to_save unless the user explicitly needs custom adapter placement.

S3 Staging

The Cosmos-Embed1 CLI consumes local paths and Python globs, not raw s3://.../*.mp4 URIs. For S3-backed runs, first stage a subset or full dataset to the execution host/container filesystem, then use local paths such as /data/video/*.mp4 in the spec.

Recommended S3 layout for staged MSR-VTT data:

s3://bucket/path/cosmos-embed/msrvtt-subset/
├── msrvtt_test_1k.json
└── video/
    ├── video7020.mp4
    └── ...

After downloading/syncing that prefix into the mounted data/ directory, use the same Docker commands above.

Outputs

results/
├── train/
│   ├── cosmos_embed1_model_latest.pth
│   ├── cosmos_embed1_model_<iter>.pth
│   └── experiment.yaml
├── evaluate/
│   ├── metrics.json
│   └── experiment.yaml
├── inference/
│   ├── results.json
│   └── experiment.yaml
├── export/
│   ├── cosmos_embed1_combined.onnx
│   └── export_config.yaml
└── export_hf/
    └── cosmos_embed1_hf/

Known Pitfalls

SymptomCauseFix
MSRVTTDataset: 0 videos foundmp4_urls is not a local glob or metadata filenames do not match videos.Mount data into the container and set mp4_urls=/data/video/*.mp4.
HF download/auth failureMissing or invalid HF_TOKEN, or model agreement not accepted.Accept the model terms and pass -e HF_TOKEN.
cannot import name 'Imports' from 'wandb.proto.wandb_telemetry_pb2'wandb==0.21.0 in the container is incompatible with protobuf==7.x.Run python -m pip install "protobuf<7" in the container before invoking cosmos-embed1.
Resume fails with Model does not implement 'apply_fsdp'Single-GPU resume loaded a consolidated checkpoint while model.fsdp_shard_size stayed at the default 8.Set model.fsdp_shard_size=1 for local single-GPU resume/retrain.
LoRA injection failureTransformer Engine visual encoder is enabled.Set model.network.visual_encoder.transformer_engine=false.
ONNX/HF export complains about missing componentsExport checkpoint is partial or adapter-only.Use a full checkpoint or configure pretrained visual/text sources before export.
CUDA OOMBatch/resolution too high for the GPU.Reduce batch size, use 224p, enable LoRA, or use more GPUs.

Files

LICENSE

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

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: Declares allowed tools, Needs a command-line tool, Needs Python

The author says: Requires docker + nvidia-container-toolkit, the published Cosmos-Embed TAO container (pinned in this skill), and a HuggingFace token when downloading pretrained `nvidia/Cosmos-Embed1-*` weights.

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/tao-finetune-cosmos-embed · 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 tao-finetune-cosmos-embed.zip