Instructions to use microtensor-archive/mt-extract-3g-r1237-5FnxoF33 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-extract-3g-r1237-5FnxoF33 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-extract-3g-r1237-5FnxoF33
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-extract-3g-r1237-5FnxoF33

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf microtensor-archive/mt-extract-3g-r1237-5FnxoF33
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-extract-3g-r1237-5FnxoF33

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-extract-3g-r1237-5FnxoF33
# Run inference directly in the terminal:
./llama-cli -hf microtensor-archive/mt-extract-3g-r1237-5FnxoF33

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-extract-3g-r1237-5FnxoF33
# Run inference directly in the terminal:
./build/bin/llama-cli -hf microtensor-archive/mt-extract-3g-r1237-5FnxoF33

Use Docker

docker model run hf.co/microtensor-archive/mt-extract-3g-r1237-5FnxoF33

LM Studio
Jan
Ollama
How to use microtensor-archive/mt-extract-3g-r1237-5FnxoF33 with Ollama:
```
ollama run hf.co/microtensor-archive/mt-extract-3g-r1237-5FnxoF33
```
Unsloth Desktop

How to use microtensor-archive/mt-extract-3g-r1237-5FnxoF33 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-extract-3g-r1237-5FnxoF33

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-extract-3g-r1237-5FnxoF33"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use microtensor-archive/mt-extract-3g-r1237-5FnxoF33 with Docker Model Runner:
```
docker model run hf.co/microtensor-archive/mt-extract-3g-r1237-5FnxoF33
```

Lemonade

How to use microtensor-archive/mt-extract-3g-r1237-5FnxoF33 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull microtensor-archive/mt-extract-3g-r1237-5FnxoF33

Run and chat with the model

lemonade run user.mt-extract-3g-r1237-5FnxoF33-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use microtensor-archive/mt-extract-3g-r1237-5FnxoF33 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-extract-3g-r1237-5FnxoF33

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-extract-3g-r1237-5FnxoF33

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use microtensor-archive/mt-extract-3g-r1237-5FnxoF33 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-extract-3g-r1237-5FnxoF33

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-extract-3g-r1237-5FnxoF33" \
  --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 · extract/mt-3g · round 1237

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: 5FnxoF33uAe4FySQHbhJsfsc3Dmgqm9EXktAW4RvqiCfsXsd
System digest: b90578e02dbc7904f3cde9b5fe81def8
Arena: extract / mt-3g
Round: 1237
Standing this round: confirmed

Measured record

Quality: 0.9672
Expected cost: 9690.0 ms per query
Replication: 2
Config hash: sha256:cfdff69101a398359bcd859b612c56f52782b54ee647af245834784178cb4b2a
Reports root: sha256:4b78a76eb3126aed630d3cf7e8cd7925adc574eb326f6b01f79eda640159edbd

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.

Downloads last month: 113

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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