Datasetpublic

Johnny5b/portable-ai-runner

CORTEX Portable AI Drive - the complete map This repo IS the drive. Download the whole tree (or let your agents walk it) and it runs offline on any machine with Python 3 - no setup step, no internet, weights included. Total: %s across %d top-level entries. Generated %s from the live repo. THE MAP - what lives where (measured, not estimated) Path Size What it is index.html browser app: chat, model loader, mesh roles, symbol retrieval, trace meter… See the full description on the dataset page: https://huggingface.co/datasets/Johnny5b/portable-ai-runner.

sourceHugging Faceupdated 7d agoView on Hugging Face

0likes1.1kdownloads

Dataset Card

CORTEX Portable AI Drive - the complete map

This repo IS the drive. Download the whole tree (or let your agents walk it) and it runs offline on any machine with Python 3 - no setup step, no internet, weights included.

Total: %s across %d top-level entries. Generated %s from the live repo.

THE MAP - what lives where (measured, not estimated)

Path	Size	What it is
`index.html`	browser app: chat, model loader, mesh roles, symbol retrieval, trace meter
`serve.py`	server: files + COOP/COEP + POST /api/ledger + optional LAN mesh :8081
`setup.py`	one-time fetcher (NOW OPTIONAL - weights ship in the repo)
`models/cores.json`	the three pinned models + measured tok/s + amendment directives
`models/weights-manifest.json`	pinned repo ids + filenames + sha256 for every weight
`models/models.json`	what the Models tab loads (Ornith first)
`models/knowledge_graph.json`	the model's directional self-map
`models/gguf/`	THE WEIGHTS: Ornith-1.5-9B-Q4KM (5.8 GB) + Qwen3-0.6B-Q8_0 (639 MB)
`vendor/wllama/`	llama.cpp compiled to WASM - the engine the app runs on
`symbols/symbol_index.json`	the directory: every intent -> symbol -> file star (incl. reserved %%knowledge, %%proposals)
`symbols/symbol_evolution_index.json`	symbol -> module -> upstream commit -> schema -> manifest
`symbols/schemas/`	AST-extracted signatures/contracts: %d files (md + json + manifest per module)
`modules/`	%d mirrored skill repos (PYGGI, TinyverseGP, DEAP, EvoTorch, EVA, isanlp_rst, e2e-microtexts) with provenance
`knowledge_vault/`	%d real content files: computerscience, naturalsciences, healthcaresafety, cognitivereasoning
`memories/`	identity.json, audit_ledger.jsonl (every trace), proposals/, questions/, learned/
`scripts/`	builders, verifiers, cortexlivetrial.py (the runnable three-phase trial)
`stack/`	docker-compose: code-server :8443 + runner :8080 + ollama
`docs/`	OWNER-INTENT-CANON, CORTEX-POINTER-ARCHITECTURE, USB-ONE-STEP, MEASUREMENTS, runbooks
`CORTEX-ZIP-MANIFEST.json`	(if present) sha256 of every file at zip build time

HOW IT EXECUTES - the actual flow, file by file

1.python serve.py - starts ThreadingHTTPServer on :8080 (app + weights + POST /api/ledger); LAN mesh on :8081 only if the optional websockets package is installed.
2.Chrome opens http://localhost:8080 -> index.html imports vendor/wllama/esm/index.js (drive, offline; CDN only a fallback), engine pill reads drive (offline).
3.Models tab -> Scan -> reads models/models.json -> Load -> wllama streams models/gguf/Ornith-1.5-9B-Q4_K_M.gguf FROM THE FOLDER (allowOffline, parallelDownloads 3).
4.Every message you send: awakeRetrieve() reads symbols/symbol_index.json, matches your text against intents, READS the mapped vault/schema files, injects them as system context (per request, never per token) -> [TRACE:AWAKE] symbols=... files_read=... lookup_ms=...
5.Generation streams through llama.cpp WASM; tokens, first-token latency and tok/s are counted -> [TRACE:GEN] tok=... tok_s=... -> POST /api/ledger -> appended to memories/audit_ledger.jsonl.
6.Optional LAN mesh: other devices pick Wi-Fi Client, the host runs the model, tokens relay over ws :8081 (Bluetooth LE via Nordic UART also supported).
7.stack/ docker-compose is the workshop variant: code-server :8443 + runner + ollama.

WHAT IT WORKS BEST IN - measured, not vibes

Runner	Use when	Numbers
This app (wllama WASM in Chrome)	zero install, USB drive, thin clients	browser tok/s UNMEASURED - the app measures it per reply and appends to the ledger
LM Studio (desktop)	towers/mini-PCs, GPU offload	Ornith-1.5-9B measured 76.7 tok/s on a10g GPU (job 6aad81ec)
llama.cpp server / stack/	GPU tier, headless	Ornith-9B 76.7 - Ornith-35B-A3B 122.7 - Qwen3-Coder-30B 146.3 tok/s (all measured)
Ollama	quick pull on machines WITH internet	works, but first pull online - less offline-clean

RAM honesty: Ornith-9B in the browser wants ~16 GB RAM; 8 GB machines use Qwen3-0.6B in-browser, or run Ornith on the host with any of the runners above.

TRACES - how you know it is working

Every reply shows its own measurement line in the chat and writes it to the ledger:

[TRACE:AWAKE] symbols=%%EVOOPT:DEAP filesread=2 lookupms=3 | [TRACE:GEN] tok=214 firsttokms=890 toks=11.4

Read the raw ledger: memories/audit_ledger.jsonl.

HONEST LIMITS

—Browser-tier tok/s: unmeasured until tonight's first run - never quote GPU numbers for it.
—The fine-tuned CORTEX model (trace-protocol-native patches) is designed but untrained: docs/CUSTOM-MODEL-BUILD-PLAN.md.
—Two historical measurement scripts still import the banned library - owner decision pending (port or documented exemption).