neuron-tai/QUICKSTART.md

# Quickstart — Running a node and testing inference

This guide gets you from zero to a live inference request in three terminals.
Tested on: AMD Ryzen AI Max (Strix Halo APU), 124 GB RAM, Linux, CPU inference.

---

## Prerequisites

```bash
# Clone and enter repo
cd /run/media/popov/d/DEV/repos/d-popov.com/AI

# Create the virtualenv if it does not exist yet
python3 -m venv .venv

# Keep packaging tools current enough for editable installs
.venv/bin/python -m pip install --upgrade pip setuptools wheel

# Install Python packages (editable — picks up code changes immediately)
.venv/bin/pip install -e packages/tracker -e packages/node -e packages/p2p -e packages/gateway -e packages/relay

# CPU-only PyTorch (skip if you have CUDA/ROCm already)
.venv/bin/pip install torch --index-url https://download.pytorch.org/whl/cpu

# HuggingFace model libraries
.venv/bin/pip install "transformers>=5.12" accelerate
```

> **NVIDIA GPU (CUDA):** replace the torch line with `pip install torch` (default index).  
> **AMD GPU (ROCm):** `pip install torch --index-url https://download.pytorch.org/whl/rocm6.2`

### Version and library notes for Qwen3.5/3.6-MoE models

- **transformers ≥ 5.12 is required** for Qwen3.5/3.6-MoE (e.g. `Qwen3.6-35B-A3B`).
  Older versions fail at load time with
  `'Qwen3_5MoeConfig' object has no attribute 'vocab_size'`. Check with
  `python -c "import transformers; print(transformers.__version__)"` and upgrade
  with `pip install -U transformers` in the environment that runs `meshnet-node`
  (conda/miniforge users: upgrade inside that env, not a layered `.venv`).
- **Linear-attention fast path (GPU only).** Qwen3.5/3.6 use hybrid linear-attention
  layers; without optional CUDA kernels, Transformers falls back to slower pure-PyTorch
  code and prints `The fast path is not available…` at startup. That warning is
  harmless — inference still works. On native Windows, install `triton-windows` in
  the same env as `meshnet-node`; otherwise `flash-linear-attention` can fail during
  import with `Could not import module 'Qwen3_5MoeForCausalLM'`. Install the
  acceleration packages into the same env as `meshnet-node` for GPU speed; skip on
  CPU-only nodes:

  ```bash
  # Native Windows
  pip install triton-windows

  # NVIDIA (CUDA)
  pip install flash-linear-attention[cuda] causal-conv1d

  # AMD (ROCm) — match your torch index, then:
  pip install flash-linear-attention[rocm] causal-conv1d
  ```

  Restart the node after install; the warning should disappear. Expect the largest
  gain on GPU nodes serving linear-attention layers (roughly three quarters of
  Qwen3.6 layers); end-to-end chat speed still depends on the slowest hop in a
  split route.
- `pip install nvidia-ml-py` silences the pynvml deprecation warning on NVIDIA hosts.

## Bootstrap a tracker on a new machine

Use this when provisioning a fresh LAN/public tracker host. The tracker itself is
lightweight; install the relay too if nodes will connect from NAT, WSL2, mobile,
or other networks where inbound node ports are not reachable.

```bash
# 1. Get the repo onto the tracker host
git clone https://git.d-popov.com/popov/neuron-tai.git AI
cd AI

# 2. Create an isolated Python environment
python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip setuptools wheel

# 3. Install only the services needed by the tracker host
.venv/bin/pip install -e packages/tracker -e packages/relay -e packages/gateway
```

For a private LAN tracker, start only the tracker and open the selected TCP port
on the host firewall if other machines will join:

```bash
.venv/bin/meshnet-tracker start --host 0.0.0.0 --port 8080
# --starting-credit 1 --devnet-topup 10
```

Verify from the tracker host:

```bash
curl -s http://localhost:8080/v1/network/map | python3 -m json.tool
```

Verify from another LAN machine, replacing the IP with the tracker host's LAN IP:

```bash
curl -s http://192.168.0.179:8080/v1/network/map | python3 -m json.tool
```

For a public tracker with relay support, run both services. The relay listens on
`8765`; the tracker below listens on `8081` and advertises the public WebSocket
URL that nodes should use for outbound relay connections:

```bash
# Terminal 1 — relay
.venv/bin/meshnet-relay --host 0.0.0.0 --port 8765

# Terminal 2 — tracker
.venv/bin/meshnet-tracker start \
  --host 0.0.0.0 \
  --port 8081 \
  --relay-url wss://ai.neuron.d-popov.com/ws
```

If this host sits behind Nginx Proxy Manager, point `/` and `/v1/*` at tracker
port `8081`, and point `/ws` plus `/rpc` at relay port `8765` as shown in the
public tracker section below. After the proxy is configured, verify the public
bootstrap endpoint:

```bash
curl -s https://ai.neuron.d-popov.com/v1/network/map | python3 -m json.tool
```

Nodes can then join with either the LAN tracker URL or the public URL:

```bash
.venv/bin/meshnet-node start --tracker http://192.168.0.179:8080 --model Qwen/Qwen2.5-0.5B-Instruct
.venv/bin/meshnet-node start --tracker https://ai.neuron.d-popov.com --model Qwen/Qwen2.5-0.5B-Instruct
.venv/bin/meshnet-node start --tracker https://ai.d-popov.com --model Qwen/Qwen2.5-0.5B-Instruct
```

### Windows / WSL2

Run the Linux commands from WSL, not Git Bash. From the repo opened in Git Bash:

```bash
wsl
cd /mnt/d/DEV/workspace/REPOS/git.d-popov.com/neuron-tai
python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip setuptools wheel
.venv/bin/pip install -e packages/tracker -e packages/node -e packages/p2p -e packages/gateway -e packages/relay
.venv/bin/pip install torch --index-url https://download.pytorch.org/whl/cpu
.venv/bin/pip install "transformers>=5.12" accelerate
.venv/bin/meshnet-node --help
```

If `.venv/bin/meshnet-node` is missing, the editable install step did not finish
successfully. Re-run the `.venv/bin/pip install -e ...` command above inside WSL.

WSL2 sits behind Windows NAT and is **not directly reachable** from other LAN machines.
Direct cross-host hops time out. The relay path (see below) solves this: the WSL2 node
opens an outbound WebSocket to the relay server and all traffic flows through that tunnel.
No firewall rules, no `--advertise-host` needed — just point at the public tracker URL.

### Native Windows PowerShell node (not WSL)

Use this when the tracker is on another machine and you want Windows to host a
reachable node on the LAN.

#### Option A — existing conda/miniforge environment with CUDA torch (recommended if you already have it)

First, make sure the conda base environment is active so that `python` and `pip` both
resolve to the same miniforge installation:

```powershell
conda activate base
deactivate   # drop any .venv that may be layered on top; safe no-op if none active
```

Install project packages into the active conda/miniforge env:

```powershell
cd D:\DEV\workspace\REPOS\git.d-popov.com\neuron-tai

pip install -e packages\tracker -e packages\node -e packages\p2p -e packages\gateway -e packages\relay
pip install "transformers>=5.12" accelerate safetensors   # torch is already present
```

> Conda/miniforge envs often carry an older `transformers` pinned by other tools
> (aider, etc.). Qwen3.5/3.6-MoE models need **transformers ≥ 5.12** — verify with
> `python -c "import transformers; print(transformers.__version__)"`. The pip
> resolver may print dependency-conflict warnings for those other tools; they don't
> affect `meshnet-node`.

Verify torch is importable and CUDA is live **before** starting the node:

```powershell
python -c "import torch; print(torch.__version__, torch.cuda.is_available())"
# Expected: 2.x.x+cuXXX  True
```

If you get `ModuleNotFoundError: No module named 'torch'` even though `pip install torch`
says "already satisfied", the `torch/` package directory is missing while the metadata
stub remains (can happen after a conda-managed install). Force-reinstall all three
PyTorch packages together so their versions stay in sync:

```powershell
pip install --force-reinstall torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```

> **Important:** always reinstall `torch`, `torchvision`, and `torchaudio` as a group.
> Upgrading only `torch` leaves `torchvision` on an incompatible version, which causes
> `RuntimeError: operator torchvision::nms does not exist` and makes transformers fail
> to import any model class (the error surfaces as `Could not import module 'Qwen2ForCausalLM'`).

Then re-run the verify step above.

If that prints `True` but `meshnet-node` still can't find torch, the venv entry point
is shadowing the conda one. Check which binary wins:

```powershell
(Get-Command meshnet-node).Source
# Should show: C:\Users\<you>\miniforge3\Scripts\meshnet-node.exe
# If it shows .venv\Scripts\meshnet-node.exe, use the full path below instead
```

To start a node:

```powershell
$env:HF_HOME = "D:\DEV\models"
meshnet-node start --tracker https://ai.neuron.d-popov.com --model Qwen/Qwen2.5-0.5B-Instruct
```

If the wrong entry point is shadowing, invoke via the full conda path:

```powershell
C:\Users\popov\miniforge3\Scripts\meshnet-node.exe start `
  --tracker https://ai.neuron.d-popov.com `
  --model Qwen/Qwen2.5-0.5B-Instruct
```

#### Option B — isolated virtualenv (fresh machine, no existing torch)

1. Install prerequisites on Windows:
   - Python 3.11 or 3.12 from <https://www.python.org/downloads/windows/>
   - Git for Windows from <https://git-scm.com/download/win>

2. Open **PowerShell** in the cloned repo and install the node packages:

```powershell
# Example repo path; adjust to wherever you cloned it
cd D:\DEV\workspace\REPOS\git.d-popov.com\neuron-tai

python -m venv .venv
.\.venv\Scripts\python.exe -m pip install --upgrade pip setuptools wheel
.\.venv\Scripts\pip.exe install -e packages\tracker -e packages\node -e packages\p2p -e packages\gateway -e packages\relay

# CPU-only PyTorch. For NVIDIA CUDA, use `pip install torch` instead.
.\.venv\Scripts\pip.exe install torch --index-url https://download.pytorch.org/whl/cpu
.\.venv\Scripts\pip.exe install "transformers>=5.12" accelerate

.\.venv\Scripts\meshnet-node.exe --help
```

For `start`-specific flags, run:

```powershell
.\.venv\Scripts\meshnet-node.exe start --help
```

3. Find the Windows LAN IP address:

```powershell
ipconfig
```

Use the IPv4 address on the active Ethernet/Wi-Fi adapter, for example
`192.168.0.42`. Avoid WSL/Docker/Hyper-V adapter addresses like `172.16.x.x`,
`172.17.x.x`, or other virtual adapter IPs.

4. Allow inbound traffic for the node port in Windows Firewall. Run PowerShell as
Administrator once:

```powershell
New-NetFirewallRule `
  -DisplayName "Meshnet node 8005" `
  -Direction Inbound `
  -Action Allow `
  -Protocol TCP `
  -LocalPort 8005
```

5. Start the Windows node from normal PowerShell. Replace the tracker and
advertised host values with your actual LAN addresses:

```powershell
$env:HF_HOME = "D:\DEV\models"

.\.venv\Scripts\meshnet-node.exe start `
  --tracker http://192.168.0.179:8081 `
  --model Qwen/Qwen2.5-0.5B-Instruct `
  --shard-start 12 --shard-end 23 `
  --quantization bfloat16 `
  --host 0.0.0.0 `
  --advertise-host 192.168.0.42 `
  --port 8005
```

One-line variants (direct LAN — node must be reachable by IP from other machines):

```powershell
.\.venv\Scripts\meshnet-node.exe start --tracker http://192.168.0.179:8081 --model Qwen/Qwen2.5-0.5B-Instruct --advertise-host 192.168.0.20
```

Via public hostname with relay (works from behind NAT, WSL2, 5G — no `--advertise-host` needed):

```powershell
.\.venv\Scripts\meshnet-node.exe start --tracker https://ai.neuron.d-popov.com --model Qwen/Qwen2.5-0.5B-Instruct
```

WSL (same relay path — no `--advertise-host`):

```bash
.venv/bin/meshnet-node start --tracker https://ai.neuron.d-popov.com --model Qwen/Qwen2.5-0.5B-Instruct
.venv/bin/meshnet-node start --tracker http://192.168.0.179:8081 --model Qwen/Qwen2.5-0.5B-Instruct
```

`--host 0.0.0.0` binds the node to all Windows interfaces. `--advertise-host`
is what the tracker gives to other nodes for direct connections; omit it when using
the relay path since all traffic flows through the relay tunnel instead.

If you want verbose per-hop pipeline logs while debugging a split model, add
`--debug`. Leave it off for normal runs; otherwise every generated token logs
lines like:

```text
  [node] pipeline hop 0: http://127.0.0.1:8005 start_layer=22
  [node] pipeline hop 0 returned text=' token'
  [node] pipeline hop 1: wss://ai.neuron.d-popov.com/rpc/abc123 relay start_layer=12
```

6. From the tracker machine, verify Windows is reachable:

```bash
curl http://192.168.0.42:8005/v1/health
```

If that endpoint returns 404 or 501, that is okay: it still proves the TCP
connection reached the node process. If it times out or connection-refuses, check
the Windows Firewall rule, `--host 0.0.0.0`, the selected LAN IP, and that the
node is still running.

---

## Public tracker + relay (internet / NAT nodes)

This setup lets nodes connect from anywhere — behind home NAT, 5G, WSL2, or
on a different continent — without opening firewall ports.

### Architecture

```
Client → HTTPS → ai.neuron.d-popov.com (nginx)
                  ├─ /v1/*   → meshnet-tracker :8081
                  ├─ /ws     → meshnet-relay :8765  (node persistent outbound WS)
                  └─ /rpc/*  → meshnet-relay :8765  (caller opens WS per hop)
```

### Nginx Proxy Manager (Docker)

Use **one** proxy host for the domain. Do not create a second host for the same
domain to reach another port — path routing is done with **Custom locations** on
that same host.

**1. Details tab** (default `/` → tracker)

| Field | Value |
|-------|--------|
| Domain Names | `ai.neuron.d-popov.com` |
| Scheme | `http` |
| Forward Hostname / IP | LAN IP of the tracker machine (e.g. `192.168.0.179`) |
| Forward Port | `8081` |
| Websockets Support | ON |

This serves `/v1/network/map`, `/v1/chat/completions`, and the rest of the tracker API.

**2. Custom locations tab** (sub-paths → relay)

The Custom locations form has **no separate Websockets toggle** — only location,
scheme, forward host, optional sub-folder path, and port. Add **two** locations
(both pointing at the relay process on port `8765`). Leave **“Add a path for
sub-folder forwarding”** empty so the full URI reaches the relay
(`/ws`, `/rpc/<peer_id>`).

Location A — persistent node connections:

| Field | Value |
|-------|--------|
| Define location | `/ws` |
| Scheme | `http` |
| Forward Hostname / IP | `192.168.0.179` |
| Forward Port | `8765` |
| Sub-folder path | *(leave empty)* |

Location B — per-hop RPC:

| Field | Value |
|-------|--------|
| Define location | `/rpc` |
| Scheme | `http` |
| Forward Hostname / IP | `192.168.0.179` |
| Forward Port | `8765` |
| Sub-folder path | *(leave empty)* |

Nginx matches the longer prefixes first: `/ws` and `/rpc/…` go to relay; everything
else stays on `8081`.

**3. SSL tab**

Use your existing Let’s Encrypt certificate (unchanged).

**4. Advanced tab** (only if WebSocket upgrade fails on `/ws` or `/rpc`)

Custom locations do not expose a Websockets checkbox. If nodes show
`Relay configured but not connected yet` while `/v1/network/map` works, add this
snippet on the **proxy host** Advanced tab:

```nginx
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $http_connection;
proxy_read_timeout 3600s;
proxy_send_timeout 3600s;
```

**5. Verify routing**

```bash
# Tracker (8081 via default location)
curl -s https://ai.neuron.d-popov.com/v1/network/map | python3 -m json.tool

# Relay paths should not 502/404 from the tracker — check response headers/status
curl -sI https://ai.neuron.d-popov.com/ws
curl -sI https://ai.neuron.d-popov.com/rpc/test-peer
```

After NPM is correct, start relay and tracker on the LAN machine:

```bash
# Terminal 1 — relay
.venv/bin/meshnet-relay --host 0.0.0.0 --port 8765

# Terminal 2 — tracker (advertises relay to nodes)
.venv/bin/meshnet-tracker start \
  --host 0.0.0.0 \
  --port 8081 \
  --relay-url wss://ai.neuron.d-popov.com/ws
```

Nodes using `https://ai.neuron.d-popov.com` should then log:

```text
Relay advertised by tracker — using outbound tunnel wss://ai.neuron.d-popov.com/ws
  Relay connected — wss://ai.neuron.d-popov.com/rpc/<peer_id>
```

The `--relay-url` flag embeds the relay address in `/v1/network/map`. Every node
queries that endpoint on startup and auto-connects if a relay URL is present.

### Start a node (any machine, any network)

No `--advertise-host`, firewall rule, port forwarding, relay URL, or peer URL is
needed on the node. The public tracker is the only bootstrap URL the user types.
The node queries the tracker for `/v1/network/map`, discovers the relay URL, and
opens a persistent outbound WebSocket. If the relay connection drops, the node
keeps retrying it.

```bash
.venv/bin/meshnet-node start \
  --tracker https://ai.neuron.d-popov.com \
  --model Qwen/Qwen2.5-0.5B-Instruct
```

No authentication is required in the prototype. The first public node for a model
must still choose that model with `--model` or a saved wizard config. After at
least one HF model node is registered, later nodes can join the public network
with only the tracker URL; the tracker assigns an uncovered shard if one exists:

```bash
.venv/bin/meshnet-node start --tracker https://ai.neuron.d-popov.com
```

Use the public tracker to verify registration and routing:

```bash
curl -s "https://ai.neuron.d-popov.com/v1/network/map" | python3 -m json.tool
curl -s "https://ai.neuron.d-popov.com/v1/route?model=qwen2.5-0.5b" | python3 -m json.tool
```

Expected startup output (relay path):

```
  Auto-detected 24 layers → shard 0–23
  Relay connected — wss://ai.neuron.d-popov.com/rpc/abc1def2ef3f4567
================================
meshnet-node ready
  Wallet:       <address>
  Model ID:     Qwen/Qwen2.5-0.5B-Instruct
  Shard:        layers 0–23; 24 of 24
  Quantization: bfloat16
  Endpoint:     http://172.29.104.23:7001
  Node ID:      <id>
  Hardware:     CPU
================================
```

The `Endpoint` shown is the local IP (unreachable from outside). Other nodes reach
this one via `wss://ai.neuron.d-popov.com/rpc/<peer_id>` instead.

### How relay hops work

When node A needs to forward activations to node B (behind NAT):

1. Tracker injects `X-Meshnet-Route` with `relay_addr` for each behind-NAT hop.
2. Node A opens a WebSocket to `wss://relay/rpc/{peer_id_B}`.
3. Relay forwards the `relay-http-request` envelope to Node B's persistent connection.
4. Node B processes `/forward` locally, returns `relay-http-response`.
5. Relay sends the response back to Node A over the same WebSocket.
6. Node A closes the WebSocket and continues the pipeline.

Binary activation tensors (bfloat16) are Base64-encoded through the relay JSON
protocol and decoded on both sides — no precision loss.

If the relay hop fails (relay down, peer disconnected), the node logs a warning and
falls back to a direct HTTP attempt before returning an error.

### Test from WSL2 using the public tracker

In WSL2 (which gets a `172.x.x.x` virtual IP — unreachable from other machines):

```bash
# WSL2 Terminal 1 — head node (layers 0–11, handles chat requests)
.venv/bin/meshnet-node start \
  --tracker https://ai.neuron.d-popov.com \
  --model Qwen/Qwen2.5-0.5B-Instruct \
  --shard-start 0 --shard-end 11

# WSL2 Terminal 2 — tail node (layers 12–23)
.venv/bin/meshnet-node start \
  --tracker https://ai.neuron.d-popov.com \
  --model Qwen/Qwen2.5-0.5B-Instruct \
  --shard-start 12 --shard-end 23
```

Both nodes connect to the relay automatically. When a chat request arrives at Node A,
it forwards activations to Node B via `wss://ai.neuron.d-popov.com/rpc/{peer_id_B}`.

Send inference through the tracker (which picks the head node and injects the route):

```bash
curl -s https://ai.neuron.d-popov.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-mesh-<your-key>" \
  -d '{
    "model": "Qwen/Qwen2.5-0.5B-Instruct",
    "messages": [{"role": "user", "content": "What is 7 times 8?"}],
    "stream": false
  }' | python3 -m json.tool
```

Or send directly to Node A's local port (within WSL):

```bash
curl -s http://localhost:7001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen/Qwen2.5-0.5B-Instruct", "messages": [{"role": "user", "content": "Hi"}]}'
```

## Accounts, API keys, and credit (billing-enabled trackers)

Public trackers run with billing on: `/v1/chat/completions` requires a real
API key from a registered account. Unknown bearer strings get `401`; a key
with no balance gets `402 insufficient balance`.

**Dashboard flow (easiest):** open `https://<tracker>/dashboard`, register with
an email + password, then click **+ new key**. The key (`sk-mesh-…`) shows its
balance next to it. If the tracker was started with `--starting-credit`, your
first key arrives pre-funded (Caller Credit, once per account). If it was
started with `--devnet-topup`, every key row has a **+$N (devnet)** button to
refill during testing.

**Curl flow:**

```bash
# 1. Register (once)
curl -s https://<tracker>/v1/auth/register \
  -H "Content-Type: application/json" \
  -d '{"email": "you@example.com", "password": "hunter22-or-better"}'
# → {"session_token": "...", ...}

# 2. Create an API key (session token from step 1)
curl -s https://<tracker>/v1/account/keys -X POST \
  -H "Authorization: Bearer <session_token>"
# → {"api_key": "sk-mesh-...", "caller_credit_granted": true}

# 3. Check balance / usage
curl -s https://<tracker>/v1/account -H "Authorization: Bearer <session_token>"

# 4. (devnet trackers only) top up a key
curl -s https://<tracker>/v1/account/topup -X POST \
  -H "Authorization: Bearer <session_token>" \
  -H "Content-Type: application/json" \
  -d '{"api_key": "sk-mesh-..."}'
```

Operator side: both features default to 1 USDT (`--starting-credit` /
`--devnet-topup`). Set both to 0 on mainnet deployments — real deposits flow
through the on-chain USDT treasury watcher instead.

---

## Step 1 — Start the tracker (Terminal 1)

```bash
cd /run/media/popov/d/DEV/repos/d-popov.com/AI
.venv/bin/meshnet-tracker start --port 8080
```

Expected output:
```
Tracker listening on 0.0.0.0:8080
```

Keep this terminal open.

---

## Step 2 — Start a node (Terminal 2)

### Recommended model: Qwen2.5-0.5B-Instruct

- 0.5B parameters, ~1 GB in BF16
- No HuggingFace account or license required
- Downloads once to `~/.meshnet/models/`, cached for future runs
- 24 transformer layers (auto-detected — no need to specify)

```bash
cd /run/media/popov/d/DEV/repos/d-popov.com/AI
HF_HOME=/run/media/popov/d/DEV/models \
.venv/bin/meshnet-node start \
  --model Qwen/Qwen2.5-0.5B-Instruct \
  --quantization bfloat16 \
  --tracker http://localhost:8080 \
  --port 8001
```

Shard range is **auto-detected** from the curated catalog (no network call for known
models). For unknown repos, the node fetches only `config.json` (~1 KB) to read
`num_hidden_layers`. You can still pass `--shard-start` / `--shard-end` explicitly
to run a partial shard on one machine.

Expected output (after model loads):
```
  Auto-detected 24 layers → shard 0–23
================================
meshnet-node ready
  Wallet:       <address>
  Model ID:     Qwen/Qwen2.5-0.5B-Instruct
  Shard:        layers 0–23
  Quantization: bfloat16
  Endpoint:     http://<host>:8001
  Hardware:     CPU
================================
```

### Other model options (all CPU-friendly)

| Model | HF repo | Layers | BF16 size | Notes |
|-------|---------|--------|-----------|-------|
| Qwen2.5-0.5B | `Qwen/Qwen2.5-0.5B-Instruct` | 24 | ~1 GB | Fastest, no gating |
| Qwen2.5-1.5B | `Qwen/Qwen2.5-1.5B-Instruct` | 28 | ~3 GB | Better quality |
| Phi-3-mini | `microsoft/Phi-3-mini-4k-instruct` | 32 | ~7.5 GB | Best CPU quality |
| Llama-3.2-1B | `meta-llama/Llama-3.2-1B-Instruct` | 16 | ~2 GB | Requires HF login |
| Llama-3.2-3B | `meta-llama/Llama-3.2-3B-Instruct` | 28 | ~6 GB | Requires HF login |

For gated models (Llama), run `huggingface-cli login` first.

---

## Step 3 — Send an inference request (Terminal 3)

If you started the node with `--port 8001`, send the request directly to that
head node:

```bash Qwen2.5-0.5B-Instruct
curl -s http://localhost:8001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-0.5b",
    "messages": [{"role": "user", "content": "What is 7 times 8? Answer in one word."}],
    "stream": false
  }' | python3 -m json.tool
```

If you did not pass `--port`, `meshnet-node start` uses the first free port at
or above `7000`. Use the `Endpoint:` printed by the node instead of `8001`.

To test tracker routing/proxying, send the same OpenAI-compatible request to the
tracker, using either the full HuggingFace repo or the quick alias:

```bash
curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-0.5b",
    "messages": [{"role": "user", "content": "What is 7 times 8? Answer in one word."}],
    "stream": false
  }' | python3 -m json.tool
```

Or use the test script:

```bash
.venv/bin/python scripts/test_lan_inference.py \
  --tracker http://localhost:8080 \
  --gateway http://localhost:8001
```

---

## Two-node split (same machine, two terminals)

Split Qwen2.5-0.5B's 24 layers across two node processes to test the sharded pipeline:

**Node A — layers 0–11 (tracker mode, serves chat completions):**
```bash
HF_HOME=/run/media/popov/d/DEV/models \
.venv/bin/meshnet-node start \
  --model Qwen/Qwen2.5-0.5B-Instruct \
  --shard-start 0 --shard-end 11 \
  --quantization bfloat16 \
  --tracker http://localhost:8080 \
  --port 8001
```

**Node B — layers 12–23:**
```bash
HF_HOME=/run/media/popov/d/DEV/models \
.venv/bin/meshnet-node start \
  --model Qwen/Qwen2.5-0.5B-Instruct \
  --shard-start 12 --shard-end 23 \
  --quantization bfloat16 \
  --tracker http://localhost:8080 \
  --port 8002
```

Send the request to Node A — it tokenizes, runs layers 0–11, passes binary
activations to Node B, and streams the final response back.

---

## Two-machine LAN test (Linux + Windows/WSL2)

See `docs/TWO_MACHINE_TEST.md` (created by US-018).

For WSL2 nodes, registration only proves the node can reach the tracker
outbound. Tracker-routed inference also requires the tracker to reach the node's
advertised endpoint inbound. Either run the node in native Windows PowerShell,
configure Windows port forwarding into WSL for the node port, or start the
tracker with a relay URL so the node registers a `relay_addr`.

---

## Browse available models

```bash
# Show curated list with VRAM requirements
.venv/bin/meshnet-node models

# Browse HuggingFace Hub top-20 text-generation models
.venv/bin/meshnet-node models --browse
```

---

## Start with the interactive wizard

```bash
# First run: wizard detects GPU, shows model list, saves config
.venv/bin/meshnet-node

# Subsequent runs: starts directly from saved config
.venv/bin/meshnet-node

# Re-run wizard even with saved config
.venv/bin/meshnet-node --reset-config
```

---

## Run all tests

```bash
.venv/bin/python -m pytest -q
```