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https://github.com/ruvnet/RuView
synced 2026-08-02 19:11:46 +00:00
fix: load JSONL models and streamline README
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ruv
@@ -37,6 +37,43 @@ Every WiFi router already fills your space with radio waves. When people move, b
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- **Environment mapping** — RF fingerprinting identifies rooms, detects moved furniture, spots new objects
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- **Sleep quality** — overnight monitoring with sleep stage classification and apnea screening
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**Also included:**
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- **Camera-free pose** — estimate 17 body keypoints from WiFi CSI
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- **Built-in model workflow** — record CSI, train models, load RVF files, and switch LoRA profiles
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- **Local automation** — HOMECORE provides state, history, automations, signed Wasm plugins, voice hooks, and HomeKit support
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- **Unified RF world model** — combine WiFi CSI, radar, UWB, and cellular sensing in one privacy-bounded scene model; accuracy is still synthetic until real-data validation
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- **Governed evidence** — attach privacy policy, uncertainty, provenance, and witness records to sensing events
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- **RuView MetaHarness** — use an AI operator to onboard, calibrate, train, verify, and check sensing claims
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<details>
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<summary><strong>RuView MetaHarness</strong> — guided operation for humans and AI agents</summary>
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The RuView-specific metaharness we created is published as [`@ruvnet/ruview`](harness/ruview/README.md). It provides source-cited guidance, guarded Claude Code/Codex agents, deterministic verification, and an honesty check for accuracy claims.
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```bash
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# Check the local setup and get source-cited guidance
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npx @ruvnet/ruview@0.3.1 doctor
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npx @ruvnet/ruview@0.3.1 guidance --topic sensing --query "model loading"
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# Run a read-only RuView agent through Codex
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npx @ruvnet/ruview@0.3.1 agent run --host codex --repo . \
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--prompt "Find the nearest tests and cite the source files"
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# Search or verify the reviewed contributor brain
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npx @ruvnet/ruview@0.3.1 brain search --query "calibration"
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npx @ruvnet/ruview@0.3.1 brain verify --repo .
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# Check claims, replay the deterministic proof, or expose the MCP server
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npx @ruvnet/ruview@0.3.1 claim-check --file REPORT.md
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npx @ruvnet/ruview@0.3.1 verify
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npx @ruvnet/ruview@0.3.1 mcp start
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```
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Agent runs are read-only by default. Workspace writes require both `--allow-write` and `--confirm`; retrieved brain content is evidence, not authority.
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</details>
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Built on [RuVector](https://github.com/ruvnet/ruvector/) and [Cognitum Seed](https://cognitum.one), RuView runs entirely on edge hardware — an ESP32 mesh (as low as $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required.
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The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.
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@@ -79,6 +116,9 @@ RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the
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>
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> 🤗 **Pretrained weights**: download from [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — see [Loading the pretrained model](#loading-the-pretrained-model) below for one-command setup.
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<details>
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<summary><strong>Quick start options</strong> — Docker, ESP32-S3/C6, Cognitum Seed, and Python</summary>
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```bash
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# Option 1: Docker (simulated data, no hardware needed)
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docker pull ruvnet/wifi-densepose:latest
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@@ -124,6 +164,8 @@ pip install "ruview[client]" # or: pip install "wifi-densepose[clie
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# from ruview.client import SensingClient, RuViewMqttClient
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```
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</details>
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[](https://pypi.org/project/ruview/) [](https://pypi.org/project/wifi-densepose/)
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> [!NOTE]
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@@ -135,7 +177,7 @@ pip install "ruview[client]" # or: pip install "wifi-densepose[clie
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> |--------|----------|------|----------|-------------|
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> | **ESP32 + Cognitum Seed** (recommended) | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | Yes | Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary — first-cut on-device model, see [Model weights: what's real, what's not](#model-weights-whats-real-whats-not)), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy |
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> | **ESP32 Mesh** | 3-6× ESP32-S3 + WiFi router | ~$54 | Yes | Same capabilities as above without the persistent-memory features |
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> | **ESP32-C6 research node** ([ADR-110](docs/adr/ADR-110-esp32-c6-firmware-extension.md), [witness](docs/WITNESS-LOG-110.md), [reviewer guide](docs/ADR-110-REVIEW-GUIDE.md), [firmware v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0-esp32)) | ESP32-C6-DevKit ($6–10) | ~$10 | Yes (Wi-Fi 6 capable) | Same CSI pipeline as S3 with the dual-target firmware. **Firmware-side ADR-110 substrate now closed** (v0.7.0): ESP-NOW cross-board mesh quantified at **99.56 % match / 104 µs smoothed offset stdev / 3.95× EMA suppression** over a 5-min two-board soak (witness §A0.10), 32-byte UDP sync packet with operator-tunable cadence (§A0.12), ADR-018 byte 19 bit 4 wire-fix sourced from the working ESP-NOW path (§A0.13). Wire format ready for HE-LTF PPDU tagging in ADR-018 bytes 18-19 (firmware encoder + Rust + Python decoders verified end-to-end across 23 unit tests). LP-core motion-gate RISC-V program and Wi-Fi 6 soft-AP with TWT Responder both ship as opt-in code paths (default off). **Hardware-gated for measurement**: HE-LTF live subcarrier capture needs an 11ax AP (IDF v5.4 doesn't expose AP-side HE config — §A0.6); ~5 µA LP-core hibernation needs an INA meter to capture; 802.15.4 raw RX is broken in IDF v5.4 (workaround: ESP-NOW transport, shipped + measured). See witness log for the empirical / claimed split. |
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> | **ESP32-C6 research node** ([ADR-110](docs/adr/ADR-110-esp32-c6-firmware-extension.md), [witness](docs/WITNESS-LOG-110.md), [reviewer guide](docs/ADR-110-REVIEW-GUIDE.md), [firmware v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0-esp32)) | ESP32-C6-DevKit ($6–10) | ~$10 | Yes (Wi-Fi 6 capable) | Dual-target CSI with **99.56% measured ESP-NOW sync match** and measured HE-LTF capture on IDF 5.5.2. TWT and ~5 µA operation still need hardware validation. |
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> | **Research NIC** | Intel 5300 / Atheros AR9580 | ~$50-100 | Yes | Full CSI with 3x3 MIMO |
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> | **Qualcomm CSI beta** ([ADR-268](docs/adr/ADR-268-qualcomm-atheros-csi-platform.md)) | QCA9300 now; QCN9074/QCN9274 experimental | ~$30-200 | Simulator now; hardware adapter gated | Rust `QCS1` codec, deterministic replay, UDP/API integration; modern ath11k/ath12k profiles do not claim public CSI export |
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> | **Vendor provider beta** ([ADR-270](docs/adr/ADR-270-vendor-rf-sensing-integration-program.md)) | Origin, Plume, Mist, NETGEAR, Electric Imp, RF Solutions, Luma, Nest, Linksys, Wifigarden | Varies | Capability-dependent | Bounded Rust adapters and deterministic fixtures; telemetry/network-only/unsupported states cannot masquerade as CSI |
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@@ -183,9 +225,9 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/wif
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|----------|-------------|--------|
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| Python training / evaluation / embedding extraction | `model.safetensors` | ✅ Works — load with `safetensors.torch.load_file` |
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| Inspect / re-export the bundle | `model.rvf.jsonl` (line-by-line JSON) | ✅ Works — plain JSONL |
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| Sensing-server `--model <PATH>` flag | binary RVF (`RVFS` magic) | ⚠️ Loader does not yet accept the JSONL container |
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| Sensing-server `--model <PATH>` flag | native RVF, `model.safetensors`, or `model.rvf.jsonl` | ✅ Native RVF loads directly; safetensors and JSONL auto-convert in memory |
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**Known gap:** the HF model ships in JSONL RVF format, but `v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs` only parses the binary RVF segment format. Pointing `--model` at `model.rvf.jsonl` currently errors with `invalid magic at offset 0: expected 0x52564653, got 0x7974227B` and the live pipeline degrades to null output rather than falling back to heuristic mode — so for the live sensing-server, run **without** `--model` until a JSONL adapter lands (or the model is re-published as binary RVF). Use the weights from Python / training in the meantime.
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**Loader scope:** `--model` now accepts native RVF and auto-converts the published safetensors or JSONL files. The quantized `model-q*.bin` files still need a compatible reader, and loading weights does not supply the matching pose-decoder architecture or establish end-to-end pose accuracy.
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**Quantization choices** (all in the HF repo): `model-q2.bin` (4 KB) · `model-q4.bin` ⭐ recommended (8 KB) · `model-q8.bin` (16 KB) · `model.safetensors` full (48 KB)
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@@ -7013,11 +7013,12 @@ fn diagnose_model_load_error(path: &std::path::Path, data: &[u8], err: &str) ->
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/// HuggingFace formats when the native RVF loader rejects them (issue #894).
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///
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/// Order of operations:
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/// 1. Try the native RVF `ProgressiveLoader` (the only format with `RVFS` magic).
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/// 2. On failure, **auto-detect** the format. If it is convertible
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/// 1. **Detect the format before constructing the lazy progressive loader.**
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/// 2. Load native RVF directly (the only format with `RVFS` magic).
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/// 3. If the format is convertible
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/// (`safetensors` / `model.rvf.jsonl`), convert it in-memory to RVF and load
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/// that — so the published `model.safetensors` becomes loadable here.
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/// 3. If it is a non-convertible format (quantized blob / unknown), return the
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/// 4. If it is a non-convertible format (quantized blob / unknown), return the
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/// typed, actionable [`model_format::ModelLoadError`] message — never the
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/// opaque "invalid magic …" string.
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///
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@@ -7028,11 +7029,6 @@ fn load_or_convert_model(
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) -> Result<ProgressiveLoader, String> {
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use model_format::{convert_to_rvf, detect_format, ModelFormat};
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// 1. Native RVF.
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if let Ok(loader) = ProgressiveLoader::new(data) {
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return Ok(loader);
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}
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let name = path
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.file_name()
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.and_then(|n| n.to_str())
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@@ -7044,7 +7040,13 @@ fn load_or_convert_model(
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.unwrap_or("converted-model");
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match detect_format(data, &name) {
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// 2. Convertible formats: convert in-memory, then load.
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// Native RVF is the only format passed straight to the lazy loader.
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// Detect first: ProgressiveLoader::new intentionally defers parsing and
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// can otherwise accept JSONL as an empty container until a later layer.
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ModelFormat::Rvf => ProgressiveLoader::new(data).map_err(|e| {
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model_format::classify_load_failure(data, &name, &e).to_string()
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}),
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// Convertible formats: convert in-memory, then load.
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ModelFormat::Safetensors | ModelFormat::JsonlManifest => {
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match convert_to_rvf(data, &name, model_id) {
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Ok(rvf_bytes) => {
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@@ -9098,9 +9100,19 @@ mod mqtt_bridge_tests {
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#[cfg(test)]
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mod model_load_diagnostic_tests {
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use super::diagnose_model_load_error;
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use super::{diagnose_model_load_error, load_or_convert_model};
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use std::path::Path;
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#[test]
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fn jsonl_model_loads_through_model_flag_path() {
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let data = b"{\"model_id\":\"published\"}\n{\"weights\":[1.0,2.0]}\n";
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let mut loader = load_or_convert_model(Path::new("model.rvf.jsonl"), data)
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.expect("--model must auto-convert JSONL");
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loader.load_layer_a().expect("Layer A");
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let layer_c = loader.load_layer_c().expect("Layer C");
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assert_eq!(layer_c.all_weights, vec![1.0, 2.0]);
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}
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#[test]
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fn safetensors_is_named_and_points_at_894() {
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// 8-byte LE header length then '{' — the safetensors signature.
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