| --- |
| license: mit |
| language: |
| - en |
| library_name: numpy |
| tags: |
| - semantic-parsing |
| - tool-calling |
| - numpy |
| - cpu |
| - synthetic-data |
| - research |
| --- |
| |
| # Tiny Codemode CNN |
|
|
| **A 67,485-parameter research model for bounded English tool requests.** It maps |
| an instruction and an explicit tool catalog to a typed plan, then deterministically |
| compiles the plan into JavaScript. It does **not** execute tools and is not an |
| agent, general language model or reliable authorization system. |
|
|
| This release is the original CNN checkpoint, **not** a later attention candidate. |
| Unsupported instructions can be incorrectly accepted. Read the limitations before |
| using any generated plan. |
|
|
| ## Quickstart |
|
|
| Download this model repository, preserving its directory structure. With Python |
| 3.10+ and [uv](https://docs.astral.sh/uv/getting-started/installation/) 0.9+ installed, |
| run from the downloaded directory: |
|
|
| ```sh |
| uv sync --locked --no-dev |
| uv run --locked --no-dev tiny-codemode --help |
| uv run --locked --no-dev tiny-codemode --js < examples/request.jsonl |
| ``` |
|
|
| These commands install NumPy-only inference, not Torch. `--js` returns generated |
| JavaScript as text; it does not execute it. Input is JSONL: one complete JSON |
| object per line. Output is a plan (and optional JavaScript), or a `diagnostic`. |
| `python -m pip install .` is also supported but is not a locked installation. |
|
|
| Python API, after installation: |
|
|
| ```python |
| from core import Diagnostic, compile_js |
| from model import Model |
| |
| request = { |
| "instruction": 'Call list_users with team="blue"; return the last result.', |
| "tools": [ |
| { |
| "name": "list_users", |
| "arguments": {"team": "string"}, |
| "required": ["team"], |
| "returns": {"kind": "array", "properties": ["id", "name"]}, |
| } |
| ], |
| } |
| |
| try: |
| plan = Model().predict(request) |
| print(plan) |
| print(compile_js(plan, request["tools"])) |
| except Diagnostic as error: |
| print({"diagnostic": str(error)}) |
| ``` |
|
|
| The expected plan for this example is: |
|
|
| ```json |
| { |
| "mode": "single", |
| "calls": [{ "tool": "list_users", "args": { "team": "blue" } }], |
| "output": { "kind": "last" } |
| } |
| ``` |
|
|
| This is custom NumPy inference, not a Transformers/`AutoModel` checkpoint. No |
| `trust_remote_code` flag, hosted inference widget or generic text-generation API |
| is required or promised. The bundled Python code runs locally; review it before |
| use. The NPZ contains numeric arrays and is loaded without pickle. |
|
|
| ## Scope and model details |
|
|
| The [contract](SPEC.md) allows 1–8 catalog tools, 1–3 calls, up to four primitive |
| arguments per tool and 128 input tokens. It supports single, sequential or |
| parallel schedules and all/last outputs. Single-array projection and prefix limits |
| are bounded. Dependencies, mixed schedule groups, loops, branching, nested |
| arguments and arbitrary JavaScript requests are outside the contract. |
|
|
| Architecture: 1,024 fixed hash buckets, a learned 32-channel embedding, 12 |
| mechanical schema/primitive features, three width-5 48-channel ReLU convolutions, |
| and learned token-role, schedule, output and rejection heads. Catalog/literal |
| identities are normalized for the model; exact names and values are retained by |
| the deterministic frontend/compiler. No pretrained model was used. |
|
|
| - Parameters: **67,485**, float32 (269,940 raw parameter bytes). |
| - Weight file: `tiny_codemode_weights/model.npz` (253,781 bytes compressed). |
| - SHA-256: `a6b944d56580b64dfb526da5b3888a137b6783ea29d8a719a0fea3f4e3f7ee76`. |
| - Inference: Python + NumPy; CPU only is sufficient. |
| - License: [MIT](LICENSE), supplied with the code and checkpoint. |
|
|
| `SHA256SUMS` covers every exported file except itself. `release.json` identifies |
| the checkpoint and scope. The manifest checks integrity, not publisher authenticity. |
|
|
| ## Training and data |
|
|
| The selected CNN was randomly initialized and CPU-trained on **26,000 synthetic |
| requests**, with **1,600 validation requests**. Plans and token-role spans were |
| AI-authored/generated before rendering instructions; no real-user traffic is |
| represented. Synthetic schemas and phrasing are not a representative sample of |
| English tool use. There is no external pretrained base model or Hub dataset ID. |
| The full training corpus and experiment infrastructure are not part of this |
| inference-only upload; it is not a standalone training-reproduction package. |
|
|
| Seed 715, Adam learning rate 0.002, batch size 96, four CPU threads, 55 epochs. |
| The earliest best validation full-decision checkpoint was selected at epoch 12 |
| (99.0625% validation full-decision accuracy). Training used 25% neutral-wrapper |
| hash-bucket perturbation; no runtime wrapper stripping is performed. Historical |
| training took 137.60 seconds for the selected run on its recorded CPU environment; |
| this is not a hardware-independent speed guarantee. |
|
|
| ## Evaluation |
|
|
| See [EVALUATION.md](EVALUATION.md) for counts, denominators, definitions and |
| limitations, and [evaluation.json](evaluation.json) for the curated numeric summary. |
| These are **historical results bound to the exact shipped checkpoint**, not new |
| benchmarks performed while packaging. They include false accepts and rejected |
| supported requests, not only successful examples. |
|
|
| The authored bank scored 40/50 supported plans, with **13/70 negative requests |
| falsely accepted**. Its 120 requests are transformations of only 24 AI-authored |
| semantic bases—not 120 independent users. All reported banks are now exposed; |
| they must not be relabeled as future held-out evaluation. |
|
|
| ## Intended use, risks and limitations |
|
|
| Use for research, education and constrained offline demonstrations. Do not use as |
| an autonomous tool executor, policy gate, authorization mechanism or production |
| natural-language parser. Structural validity does not establish that a plan |
| matches the user's intent or permission. |
|
|
| Known failures include unfamiliar prohibitions, mixed ordering, unknown projected |
| fields, loop/sorting contexts and valid unseen phrasings. The model can reject |
| valid requests or silently drop unsupported intent. Neither a successful plan nor |
| a diagnostic should be treated as a security guarantee. Independent authorization, |
| semantic review and appropriate execution isolation are required before real |
| side effects. Mock execution tests are not a security sandbox. |
|
|
| No broad-English, multilingual, human-population, demographic fairness, worst-case |
| latency or production-safety evaluation has been established. Dataset diversity |
| and labels are synthetic and limited. Later experimental architectures were not |
| promoted into this release, and their scores must not be attributed to this CNN. |
|
|
