Instructions to use microtensor-archive/mt-classify-3g-r1239-5CkuRmNC 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-classify-3g-r1239-5CkuRmNC 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-classify-3g-r1239-5CkuRmNC
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-classify-3g-r1239-5CkuRmNC

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf microtensor-archive/mt-classify-3g-r1239-5CkuRmNC
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-classify-3g-r1239-5CkuRmNC

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-classify-3g-r1239-5CkuRmNC
# Run inference directly in the terminal:
./llama-cli -hf microtensor-archive/mt-classify-3g-r1239-5CkuRmNC

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-classify-3g-r1239-5CkuRmNC
# Run inference directly in the terminal:
./build/bin/llama-cli -hf microtensor-archive/mt-classify-3g-r1239-5CkuRmNC

Use Docker

docker model run hf.co/microtensor-archive/mt-classify-3g-r1239-5CkuRmNC

LM Studio
Jan
Ollama
How to use microtensor-archive/mt-classify-3g-r1239-5CkuRmNC with Ollama:
```
ollama run hf.co/microtensor-archive/mt-classify-3g-r1239-5CkuRmNC
```
Unsloth Desktop

How to use microtensor-archive/mt-classify-3g-r1239-5CkuRmNC 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-classify-3g-r1239-5CkuRmNC

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-classify-3g-r1239-5CkuRmNC"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use microtensor-archive/mt-classify-3g-r1239-5CkuRmNC with Docker Model Runner:
```
docker model run hf.co/microtensor-archive/mt-classify-3g-r1239-5CkuRmNC
```

Lemonade

How to use microtensor-archive/mt-classify-3g-r1239-5CkuRmNC with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull microtensor-archive/mt-classify-3g-r1239-5CkuRmNC

Run and chat with the model

lemonade run user.mt-classify-3g-r1239-5CkuRmNC-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use microtensor-archive/mt-classify-3g-r1239-5CkuRmNC 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-classify-3g-r1239-5CkuRmNC

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-classify-3g-r1239-5CkuRmNC

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use microtensor-archive/mt-classify-3g-r1239-5CkuRmNC 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-classify-3g-r1239-5CkuRmNC

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-classify-3g-r1239-5CkuRmNC" \
  --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 · classify/mt-3g · round 1239

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: 5CkuRmNC5RouXwYTGqnKizeWqmG1XmuoeEQdMYaNa4D52a4V
System digest: 2fa6cfe7c56096912a2f90e42fb6f9f7
Arena: classify / mt-3g
Round: 1239
Standing this round: confirmed

Measured record

Quality: 1.0
Expected cost: 848.0 ms per query
Replication: 3
Config hash: sha256:95e6d70bc601ce8db6691e3d5baf3a6df985ada42fd1534503ceef210345d315
Reports root: sha256:6be6966f3e39e53f2c4bf6dd58226293b66c60184bbd558b866c41ecfbeb83ad

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 apache-2.0, inherited from the base model Qwen/Qwen2.5-0.5B-Instruct@7ae557604adf67be50417f59c2c2f167def9a775 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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