modelpulse 0.2.1

End-to-end partial-weight transfer pipeline.

ModelPulse 🚀

End-to-end partial-weight transfer pipeline for edge LLM inference.

ModelPulse enables a unique "Zero-Disk" inference strategy: Device A (Server) serves model shards over the network, while Device B (Client/Bridge) reconstructs the model entirely in RAM and runs inference via llama.cpp without ever writing the full GGUF to physical storage.

Data Flow Diagram

┌─────────────────────────────────────────────────────────────┐
│                     Server (Device A)                       │
│                  FastAPI @ 0.0.0.0:8000                     │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  WebSocket /ws (Control Plane)   HTTP (Data Plane)          │
│  ├─ MODEL_READY                  ├─ GET /manifest           │
│  ├─ PING/PONG                    ├─ GET /shards/*           │
│  ├─ METRICS                      └─ POST /metrics           │
│  └─ ACK/BYE                                                 │
│                                                             │
│  /models/upload (Multipart)                                 │
│  └─ Accept manifest.json + *.shard files                    │
│                                                             │
└─────────────────────────────────────────────────────────────┘
         ↑                              ↑
         │                              │
         │ WS connect                   │ HTTP GET/POST
         │ + MODEL_READY signal         │ + shard stream
         │                              │
┌────────┴──────────────────────────────┴─────────────────────┐
│                   Client (Device B)                         │
│                       Bridge CLI                            │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  1. Connect WebSocket → Send HELLO                          │
│  2. Receive MODEL_READY → Fetch manifest (HTTP)             │
│  3. Download shards (HTTP streaming)                        │
│  4. Assemble GGUF in /dev/shm                               │
│  5. Load with llama.cpp                                     │
│  6. Run inference                                           │
│  7. Send METRICS → Loop back to step 2                      │
│     (no restart, listen for next model)                     │ 
│                                                             │
└─────────────────────────────────────────────────────────────┘

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✨ Key Features

• 🛡️ Zero-Disk Strategy: Models are assembled in tmpfs (/dev/shm), ensuring no persistent GGUF footprint on the client's disk.
• 🔄 Dynamic Model Swapping: Upload new models to the server at runtime; connected clients automatically unload, pull, and reload the new model without a restart.
• 📊 Real-time Telemetry: Detailed inference metrics (TTFT, tok/s, RAM delta, CPU temp) are streamed back to the server for centralized monitoring.
• 🛠️ Integrated Benchmarking: Built-in suite to stress-test edge devices and validate performance across different quantization levels.
• 🌐 Network Agnostic: Works seamlessly over local networks, Tailscale, or any HTTP/WS-capable connection.

---

📦 Installation

Install ModelPulse from PyPI:

pip install modelpulse

Alternatively, install directly from the repository for the latest dev features:

pip install git+https://github.com/MdSufiyan005/ModelPulse.git

Note: Ensure you have llama-cpp-python dependencies installed on your system (e.g., build-essential, python3-dev).

---

🔄 Workflow

1. Prepare Shards

Convert a monolithic .gguf file into a shard directory using the companion tool:

python tools/gguf_to_shards.py convert my_model.gguf ./my-shards/

2. Start the Server

Start the control plane on Device A. It will default to using ./models-storage for storing model data.

modelpulse server run --host 0.0.0.0 --port 8000

3. Run the Bridge

Connect your edge device to the server. It will wait for a model to be assigned.

modelpulse bridge run http://<server-ip>:8000

4. Dynamic Upload

Upload your prepared shards to the server. All connected bridges will instantly receive the update.

./upload_model.sh "qwen-3.5-2b" "./my-shards/"

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📋 Command Reference

modelpulse server run

Start the FastAPI control plane.

Option	Default	Description
--shard-dir, -d	./models-storage	Root directory for model storage
--host	127.0.0.1	Bind address
--port	8000	Listening port
--metrics-log	metrics.jsonl	File to append received telemetry

modelpulse bridge run

Connect to a server and enter the inference loop.

Option	Default	Description
host	(Required)	Server URL (e.g., http://100.64.0.5:8000)
--prompt, -p	(Interactive)	Run a single prompt and exit
--benchmark, -b	false	Run the standard benchmark suite
--max-tokens, -m	256	Token generation limit
--temperature	0.7	Sampling temperature
--n-ctx	2048	Context window size

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📁 Project Layout

modelpulse/
├── modelpulse/             # Core package
│   ├── server/             
│   │   └── server.py       # FastAPI + WebSocket control plane
│   ├── client/             # Bridge (Device B) logic
│   │   ├── cli.py          # Claude-inspired terminal UI
│   │   ├── bridge.py       # RAM GGUF assembly & llama.cpp loading
│   │   ├── shard_client.py # Async HTTP downloader for shards
│   │   └── benchmarks.py   # Built-in performance testing suite
│   ├── shared/             # Cross-component protocol definitions
│   │   ├── ws_protocol.py  # WebSocket message schemas
│   │   └── models.py       # ShardManifest & InferenceMetrics models
│   └── main.py             # Unified CLI entry point
├── tools/                  # Model preparation utilities
│   ├── gguf_to_shards.py   # GGUF → Shard converter (tensor-level)
│   └── gguf_parser.py      # Low-level GGUF format metadata reader
├── upload_model.sh         # Script for dynamic model assignment
├── TEST_WORKFLOW.md        # Step-by-step end-to-end testing guide
├── pyproject.toml          # Project metadata & dependencies
└── metrics.jsonl           # Appends log for inference telemetry

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💾 The Zero-Disk Strategy

ModelPulse leverages the Linux tmpfs (RAM-backed filesystem) to satisfy llama.cpp's requirement for a file path while keeping the actual data off physical storage:

1. Pull: Bridge fetches manifest.json.
2. Stream: Bridge pulls .shard files (tensor by tensor) into memory.
3. Assemble: Bridge calculates GGUF layout and writes bytes to /dev/shm/sb_<pid>.gguf.
4. Load: llama-cpp-python loads the model via mmap from the RAM-backed file.
5. Clean: Once the model is unloaded, the virtual file is unlinked and memory is reclaimed.

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📡 Networking (Tailscale)

For easy cross-device connectivity without port forwarding, Tailscale is highly recommended:

# Get IP on Server
tailscale ip  # e.g., 100.66.170.100

# Connect Bridge
modelpulse bridge run http://100.66.170.100:8000

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Built with ❤️ for Edge AI and Decentralized Inference.