| --- |
| license: mit |
| language: |
| - en |
| library_name: pytorch |
| tags: |
| - yaml |
| - kubernetes |
| - semantic-parsing |
| - safetensors |
| - cpu |
| - research |
| --- |
| |
| # yaml-edit |
|
|
| A **12,867-parameter experimental model** that turns a plain-language request into |
| a proposed edit to an existing Kubernetes Deployment scalar. A separate, |
| deterministic editor validates the proposal and preserves the source document's |
| comments and formatting outside the edited value. |
|
|
| **Research release, not a trusted editor.** The model can misunderstand requests, |
| choose the wrong field, or propose an edit when it should refuse. The larger |
| synthetic evaluation produced **17 wrong edits among 47 proposals**; a small public |
| YAML pilot produced **2 wrong edits among 4 proposals**. Always review the diff. |
| Publication does not change the experimental, default-off status of learned |
| inference. |
|
|
| ## Quickstart |
|
|
| Download this repository with its directory structure intact. Python 3.12+ and |
| [uv](https://docs.astral.sh/uv/) 0.9+ are required for the commands below. From the |
| downloaded directory: |
|
|
| ```sh |
| uv sync --locked --extra ml |
| work=$(mktemp -d) |
| |
| uv run --locked --extra ml yaml-edit suggest examples/deployment.yaml \ |
| --instruction 'Set api memory limit to 512Mi' \ |
| --backend learned --allow-experimental --device cpu \ |
| --config model.json --weights model.safetensors \ |
| --output "$work/proposal.json" |
| ``` |
|
|
| This example should propose changing the `api` container's memory limit from |
| `256Mi` to `512Mi`. Suggestions never modify the source file. Other requests may |
| return `unsupported` or `needs_clarification`, with no proposal file. |
|
|
| If a proposal exists, inspect the diff. Only after deciding it is correct, write a |
| new YAML file with an explicit confirmation: |
|
|
| ```sh |
| uv run --locked --extra ml yaml-edit preview examples/deployment.yaml \ |
| --proposal "$work/proposal.json" |
| |
| uv run --locked --extra ml yaml-edit apply examples/deployment.yaml \ |
| --proposal "$work/proposal.json" --output "$work/edited.yaml" --yes |
| ``` |
|
|
| The original file stays unchanged. Existing output files are never overwritten. |
| These commands do not contact a Kubernetes cluster or apply a deployment. |
|
|
| The lock uses CPU PyTorch wheels on Linux/Windows and the normal PyPI wheel on |
| macOS. No extra GPU is required. `--device mps` is an explicit alternative on |
| supported Macs; there is no silent CPU/MPS fallback. Dependency installation may |
| use the network; inference itself is local. |
|
|
| This is a custom PyTorch model, not a Transformers or `AutoModel` checkpoint. There |
| is no hosted text-generation widget, automatic model download or requirement to |
| use `trust_remote_code`. Review the included source before running it. |
|
|
| ## Supported edits and boundaries |
|
|
| Only existing scalar fields in supported, single-document Kubernetes Deployments: |
|
|
| - Replica count. |
| - A container image. |
| - Container CPU and memory requests and limits. |
|
|
| The learned interpreter proposes **one edit per instruction**. Field creation, |
| deletion, arbitrary paths, document regeneration, compound natural-language edits, |
| other Kubernetes resource kinds and multi-document YAML are outside this scope. |
| The separate explicit batch-edit CLI is not a learned compound-instruction model. |
|
|
| Learned input bounds include 64 KiB UTF-8 YAML, one 512-byte instruction line, 64 |
| actual candidates and 256 candidate/value attempts and retained actions. The |
| loader validates strict JSON and bounded F32 safetensors; it does not load pickle. |
|
|
| The editor checks source binding, allowed targets and values, reparses the result, |
| and verifies the one-value semantic invariant. These mechanical checks cannot |
| prove that an instruction was understood or authorized. Kubernetes-wide schema, |
| registry existence, capacity, organizational policy and operational correctness |
| are not established by this model. |
|
|
| ## Model and training |
|
|
| A 384-to-32 MLP encodes fixed SHA-256 character n-gram/word features. A three-way |
| disposition head selects supported, clarification or unsupported; a masked action |
| scorer ranks a bounded list of candidates using 13 action features. The learned |
| weights do not generate YAML. Parsing, field discovery, value extraction, action |
| enumeration, validation and source-preserving editing remain deterministic. |
|
|
| - **12,867 F32 parameters**, 51,468 raw parameter bytes. |
| - `model.safetensors`: **51,924 bytes**. |
| - `model.json`: architecture and calibrated thresholds, **367 bytes**. |
| - Seed: 202503; random initialization, no pretrained base model. |
| - Training/validation: 120/60 rows from a 240-row synthetic dataset; 60 test rows. |
| - CPU training, one thread; learning rate 0.01, batch size 24, weight decay 0.0001. |
| - 32 epochs completed; epoch 12 selected by validation loss, with patience 20. |
| - Disposition threshold 0.9; action threshold 0.5, selected using validation only. |
|
|
| The original small training run took approximately 7.55 seconds in its recorded |
| local environment. This is not an inference-latency, memory-footprint or portable |
| training-time guarantee; the PyTorch runtime is much larger than the weights. |
|
|
| The data are tiny, synthetic and correlated, not representative human-user |
| traffic. The challenge instructions were separately agent-authored. Some test |
| instruction text had been seen during development inspection; no score-based |
| post-test tuning was reported, but these results must not be described as fully |
| blinded. All reported evaluation banks are now exposed. This download is an |
| inference package, not a complete training-reproduction or dataset release. |
|
|
| ## Historical evaluation of these exact weights |
|
|
| | Evaluation | Correct disposition | Exact supported edits | Wrong edits / proposals | |
| | --- | ---: | ---: | ---: | |
| | Synthetic test, 60 requests | 44/60 | 26/42 | 0/26 | |
| | Separately authored challenge, 11 requests | 7/11 | 1/3 | 1/2 | |
| | Larger synthetic suite, 120 requests | 62/120 | 30/60 | 17/47 | |
| | Public YAML pilot, 20 instruction cases | 5/20 | 2/12 | 2/4 | |
|
|
| Disposition means the supported/clarification/unsupported classification. Exact |
| supported edits use **all supported requests** as the denominator, including |
| refusals. Wrong edits include proposals with the wrong target/value, or a proposal |
| where refusal or clarification was required. These columns have different |
| denominators and must not be combined into one accuracy figure. |
|
|
| The larger suite was agent-authored and safety-heavy; ten conditional cases were |
| informed by an exposed failure category. The public pilot used 17 public manifests |
| across 25 related cases, with agent-authored instructions—not actual user traffic. |
| Five additional structural pilot cases were rejected correctly (5/5), but document |
| boundary checks are **not** natural-language accuracy and are excluded from the |
| 20-case instruction row. Reused documents and correlated cases do not justify |
| population failure estimates or IID confidence intervals. |
|
|
| Known failures include proposing an edit for “If approved, set api memory limit |
| to 512Mi,” ignoring negation, confusing requests with limits, choosing a field for |
| an ambiguous instruction, and reducing a compound request to an unintended single |
| edit. Valid, source-preserving YAML is not evidence of correct semantic intent. |
| The rules baseline refused every case in the larger suite and public instruction |
| pilot; its zero wrong edits reflected zero proposal coverage, not superiority. |
|
|
| These are retained historical measurements of the exact released checkpoint, not |
| new capability results from publication checks. No later candidate's scores are |
| attributed to these weights. No quality promotion, broad-English reliability, |
| production-safety or general advantage over other models is claimed. |
|
|
| ## Integrity, license and contents |
|
|
| - Config SHA-256: |
| `33032cb184803208523863cb6215d5a9b2fea07d80d86259621eb89d18657524`. |
| - Weights SHA-256: |
| `616c150e2feedd139c50249f8215cb2cb1a431eda1d99394705fee47f3293498`. |
|
|
| `SHA256SUMS` covers the supplied files except itself. Checksums establish integrity, |
| not publisher authenticity. The checkpoint is loaded only through explicit paths. |
|
|
| Code and model weights are [MIT licensed](LICENSE). Dependencies retain their own |
| licenses. This repository contains public model information, inference-only code, |
| one example, dependencies, config and weights. It deliberately excludes internal |
| design documents, raw experiment reports, training datasets and orchestration logs. |
|
|
