Instructions to use Kq1an/AutoMiner-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Kq1an/AutoMiner-AI 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 Kq1an/AutoMiner-AI:Q4_K_M # Run inference directly in the terminal: llama cli -hf Kq1an/AutoMiner-AI:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Kq1an/AutoMiner-AI:Q4_K_M # Run inference directly in the terminal: llama cli -hf Kq1an/AutoMiner-AI:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Kq1an/AutoMiner-AI:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Kq1an/AutoMiner-AI:Q4_K_M
Use Docker
docker model run hf.co/Kq1an/AutoMiner-AI:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use Kq1an/AutoMiner-AI with Ollama:
ollama run hf.co/Kq1an/AutoMiner-AI:Q4_K_M
- Unsloth Desktop
- Pi
How to use Kq1an/AutoMiner-AI with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Kq1an/AutoMiner-AI:Q4_K_M
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": "Kq1an/AutoMiner-AI:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Kq1an/AutoMiner-AI with Docker Model Runner:
docker model run hf.co/Kq1an/AutoMiner-AI:Q4_K_M
- Lemonade
How to use Kq1an/AutoMiner-AI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Kq1an/AutoMiner-AI:Q4_K_M
Run and chat with the model
lemonade run user.AutoMiner-AI-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Kq1an/AutoMiner-AI with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Kq1an/AutoMiner-AI:Q4_K_M
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 Kq1an/AutoMiner-AI:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Kq1an/AutoMiner-AI with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Kq1an/AutoMiner-AI:Q4_K_M
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 "Kq1an/AutoMiner-AI:Q4_K_M" \ --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"
AutoMiner AI helpers
Small language models trained for the AutoMiner Minecraft mod (Fabric, 1.21.11). The mod downloads one of these files when a player picks a built-in helper, then runs it on their own PC with llama.cpp. Nothing is sent anywhere.
| Helper | File | Size | Based on |
|---|---|---|---|
| Small | AutoMiner-Small-v1-Qwen3-0.6B-Q8_0.gguf |
0.64 GB | Qwen3 0.6B |
| Medium | AutoMiner-Medium-v1-Qwen3-4B-Q4_K_M.gguf |
2.50 GB | Qwen3 4B |
| Large | AutoMiner-Large-v1-Qwen3-8B-Q4_K_M.gguf |
5.03 GB | Qwen3 8B |
What they do
The mod asks a short question with the facts it already knows, and the model answers with one JSON object:
something to tell the player (say) and, optionally, mod commands to run (do). The mod checks every command
before running it. The models were trained on about 14,000 example chats made by the mod's own generator (the Large one on about 16,000, with harder stuck-bot, redstone and team-planning examples):
- redstone (43%): explain a circuit, find what's wrong with it, predict what it does, signal strengths and timings
- moving around (30%): pick a way past an obstacle when the bot is stuck, routes over or around things, movement settings
- chat commands (15%): turn what the player types ("build this, then dig out from here to there") into commands
- teamwork (13%): team status, who needs help, safety alerts, what happened while someone was away
The Small helper learned the same examples as the Medium one, but it is a much smaller model: best for chat commands and simple questions.
Training
LoRA (rank 32) on every attention and MLP layer, 2 passes over the examples, answers only; then merged into the base model and quantised with llama.cpp b11327 (Small: Q8_0, Medium and Large: Q4_K_M). Loss on 400 held-out examples: Large 0.021, Medium 0.020, Small 0.044.
Checksums (SHA-256)
6cf0496889fcb2088c1d0c8de6a322f83a3b666944298b29c999fe8ad4156997 AutoMiner-Small-v1-Qwen3-0.6B-Q8_0.gguf
3240b58eb7ce899788be4b594a0a1cf87b2b9650482518fb2f3195b8311f7f53 AutoMiner-Medium-v1-Qwen3-4B-Q4_K_M.gguf
c81f205c6f3a7e4bc25aace1c819f15e87b6ae5e40d16777d0aaf67e03e88aab AutoMiner-Large-v1-Qwen3-8B-Q4_K_M.gguf
The mod refuses a file whose checksum doesn't match.
Licence
Apache 2.0, the same as the Qwen3 base models (Copyright Alibaba Cloud).
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