{ "id": "octo-scripts-finetune-py", "name": "Octo: Configured policy fine-tuning", "summary": "Pinned Octo fine-tuning entrypoint that loads a configured pretrained policy and trains it with selected target-domain data and fine-tuning settings. Statically reviewed source only; not run and no Hub preparation adapter.", "framework": "octo", "kind": "official_example", "source_registry": "official-robot-workflows-v1", "capabilities": [ "policy-training", "robot-workflow" ], "robot_types": [ "source-defined" ], "robot_scope": "Source-configured workflow. The selected dataset, checkpoint, simulator/controller and observation/action schema determine applicability; no specific robot names, compatibility combinations or Hub support are inferred.", "source": { "repository": "https://github.com/octo-models/octo", "commit": "241fb3514b7c40957a86d869fecb7c7fc353f540", "tag": "main", "path": "scripts/finetune.py", "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/scripts/finetune.py", "raw_url": "https://raw.githubusercontent.com/octo-models/octo/241fb3514b7c40957a86d869fecb7c7fc353f540/scripts/finetune.py", "sha256": "76e6c7520947a2b2e32d2dd1cd227fc455f4b2205c00a25bf59772a6b72151d0", "bytes": 12544, "authors": [ "Octo Model Team", "Robotic AI & Learning Lab Berkeley" ] }, "license": { "spdx": "MIT", "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/LICENSE", "sha256": "8c891a7dd06fdaa873aa011ff0b2773a3ccfe6d123f6b9a9fc589db718aec1d1", "scope": "Selected first-party Python workflow source only. Preserve upstream copyright and per-file notices. Model weights, datasets, third-party code, robot drivers/firmware and services retain separate terms; no rights for those assets are inferred.", "bytes": 1091 }, "access": { "mode": "source_reference", "hub_prepare": false, "instructions": "Fetch the pinned repository, verify this source checksum, and use upstream installation and invocation. This record does not install or execute code.", "setup_url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/README.md", "setup_evidence": [ { "path": "README.md", "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/README.md", "sha256": "057e41b4866d4c1c19eaf8882c5f1551594476f1b2193f52ec626833381de262", "bytes": 11627, "purpose": "Octo attribution, Python3.10/JAX GPU or TPU setup, train/fine-tune and rollout usage" }, { "path": "pyproject.toml", "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/pyproject.toml", "sha256": "bfc9541c6293829194ef392d5d5cdf506848708c40bc96d1999182a9579af252", "bytes": 286, "purpose": "Formatting Python target only, not an inferred runtime minimum" }, { "path": "requirements.txt", "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/requirements.txt", "sha256": "e50f53de0de6182cf4cae61c07a42ec7d94f9d2dc549c8065dd4022051208095", "bytes": 587, "purpose": "JAX/Flax/TensorFlow and W&B dependency declarations" }, { "path": "examples/README.md", "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/examples/README.md", "sha256": "6f5fd78fbbe3e61d669dd0029f0418d9650b77813d25aabc16637b1e4aaa26a3", "bytes": 1704, "purpose": "Explicit fine-tune, simulated rollout and real WidowX evaluator roles" }, { "path": "examples/envs/README.md", "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/examples/envs/README.md", "sha256": "7fb9fbe17280d73eb80a30c3e5b7086aa3b9f6ae3be95d964f0faab351438982", "bytes": 836, "purpose": "Source-defined observation keys and Gym interface scope" } ] }, "requirements": { "python": "Python3.10 in README setup; no packaging minimum asserted.", "packages": [ "Pinned Octo checkout", "JAX0.4.20, Flax0.7.5 and TensorFlow2.15.0 from requirements.txt", "Per-example simulator/robot-controller dependencies and reviewed checkpoints/data" ], "display": "Hardware: Uses JAX devices, sharding and device-count divisibility checks. README supplies GPU/TPU setup. No per-workflow GPU-memory minimum or CPU validation is declared. Weights: Requires configured pretrained_path, loaded with OctoModel.load_pretrained; README examples use separately hosted Octo checkpoints. Datasets: Requires selected target-domain dataset settings. Data names and observation/action definitions remain configuration-specific. Services/auth: Initializes W&B; --debug is documented as no W&B logging. Authentication method/key variable is not specified in the selected file.", "execution_domain": "computation", "source_imports": [ "absl", "datetime", "flax", "flax.traverse_util", "functools", "jax", "jax.sharding", "jax_smi", "ml_collections", "octo.data.dataset", "octo.model.octo_model", "octo.utils.jax_utils", "octo.utils.spec", "octo.utils.train_callbacks", "octo.utils.train_utils", "optax", "os", "tensorflow", "tqdm", "wandb" ], "notes": "This configurable full script and the separate didactic observation/action adaptation example remain distinct source documents; neither represents a new policy behavior claim. Code licensing does not establish model, checkpoint, dataset or service permission.", "declared_requirements": { "python": "Python3.10 in README setup; no packaging minimum asserted.", "weights": "Requires configured pretrained_path, loaded with OctoModel.load_pretrained; README examples use separately hosted Octo checkpoints.", "datasets": "Requires selected target-domain dataset settings. Data names and observation/action definitions remain configuration-specific.", "hardware": "Uses JAX devices, sharding and device-count divisibility checks. README supplies GPU/TPU setup. No per-workflow GPU-memory minimum or CPU validation is declared.", "external_services_and_auth": "Initializes W&B; --debug is documented as no W&B logging. Authentication method/key variable is not specified in the selected file.", "evidence": [ { "anchor": "OctoModel.load_pretrained", "line": 129 }, { "anchor": "Debug config (no wandb logging)", "line": 46 }, { "anchor": "Mesh(jax.devices()", "line": 92 } ], "license_scopes": { "code": "MIT for selected first-party workflow source.", "weights": "README links separately hosted rail-berkeley Octo checkpoints. This bounded repository review did not inspect model cards or establish a weight license; repository MIT is limited to selected code.", "datasets": "README references Open X-Embodiment and an example ALOHA simulation dataset. Their individual terms were not inspected and are not covered by the code-license assertion.", "third_party_code": "External dependencies retain their own licenses." }, "interpretation": "Pinned upstream declarations only; no dependency resolution, model/data access, compatibility inference or runtime validation was performed." } }, "invocation": { "entrypoint": "python scripts/finetune.py --config.pretrained_path= ", "notes": "This configurable full script and the separate didactic observation/action adaptation example remain distinct source documents; neither represents a new policy behavior claim." }, "popularity": { "metric": "github_stars", "repository_stars": 1807, "checked_at": "2026-10-17", "meaning": "Dated repository interest, not robot-skill usage, compatibility or runtime success." }, "evidence": { "level": "source_reviewed", "runtime_verification": "not_run", "checked_at": "2026-10-17", "inspection": "Verified exact source, applicable license and author/setup document bytes at a full Git commit; parsed the Python AST and reviewed this guarded operational entrypoint and its explicit license notices. No upstream code, installation, robot command, dataset, mesh or checkpoint was executed or loaded.", "entrypoint_kind": "python_main", "inspection_method": "automated_static" }, "artifact": { "kind": "workflow", "format": "python", "workflow_role": "policy-training" } }