Instructions to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt
# Run inference directly in the terminal:
./llama-cli -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt
# Run inference directly in the terminal:
./build/bin/llama-cli -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Use Docker

docker model run hf.co/microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

LM Studio
Jan
Ollama
How to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with Ollama:
```
ollama run hf.co/microtensor-archive/mt-guard-4g-r1241-5FjVGEKt
```
Unsloth Desktop

How to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "microtensor-archive/mt-guard-4g-r1241-5FjVGEKt"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with Docker Model Runner:
```
docker model run hf.co/microtensor-archive/mt-guard-4g-r1241-5FjVGEKt
```

Lemonade

How to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Run and chat with the model

lemonade run user.mt-guard-4g-r1241-5FjVGEKt-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use microtensor-archive/mt-guard-4g-r1241-5FjVGEKt with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf microtensor-archive/mt-guard-4g-r1241-5FjVGEKt

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "microtensor-archive/mt-guard-4g-r1241-5FjVGEKt" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Microtensor archive · guard/mt-4g · round 1241

This repository is an archival copy of a system submitted to the Microtensor subnet (Bittensor netuid 92) and certified by its validators. The figures below were measured by the network on reference hardware. They are not self-reported.

Miner hotkey: 5FjVGEKtVtTWyP12eKYGKXsCyMEvh9W8BXZv7njRouMNHmrg
System digest: 7594639c3912a3d8e78f494b83e2a32b
Arena: guard / mt-4g
Round: 1241
Standing this round: confirmed

Measured record

Quality: 0.321
Expected cost: 159940.0 ms per query
Replication: 2
Config hash: sha256:068aa6a275fae6ae49cc91faf2b1b4fb252e2907d34ce5ccdbe0202497bf7e82
Reports root: sha256:4d11c097261bf455d6d481ac3ec8b0d8dd86a6132c63f9eb25fa7ab5fe332e2d

The full signed record is in certificate.json. It is recomputable from the round's published reports.

The manifest in manifest.json is the submission exactly as the miner shipped it; this repository's contents hash to the digest committed on chain for this round.

Licence

Released under mit, inherited from the base model microsoft/Phi-4-mini-instruct@cfbefacb99257ffa30c83adab238a50856ac3083 this system was built on. Submitting granted the network the right to retain, archive and redistribute this artifact, with emissions as the consideration. Anyone may serve it, including commercially, on the terms of that licence.

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