Instructions to use ayourtch/ARustyCoder93 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ayourtch/ARustyCoder93 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 ayourtch/ARustyCoder93
# Run inference directly in the terminal:
llama cli -hf ayourtch/ARustyCoder93

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ayourtch/ARustyCoder93
# Run inference directly in the terminal:
llama cli -hf ayourtch/ARustyCoder93

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 ayourtch/ARustyCoder93
# Run inference directly in the terminal:
./llama-cli -hf ayourtch/ARustyCoder93

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 ayourtch/ARustyCoder93
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ayourtch/ARustyCoder93

Use Docker

docker model run hf.co/ayourtch/ARustyCoder93

LM Studio
Jan
Ollama
How to use ayourtch/ARustyCoder93 with Ollama:
```
ollama run hf.co/ayourtch/ARustyCoder93
```

Unsloth Studio

How to use ayourtch/ARustyCoder93 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ayourtch/ARustyCoder93 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ayourtch/ARustyCoder93 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ayourtch/ARustyCoder93 to start chatting

How to use ayourtch/ARustyCoder93 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ayourtch/ARustyCoder93

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ayourtch/ARustyCoder93"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ayourtch/ARustyCoder93 with Docker Model Runner:
```
docker model run hf.co/ayourtch/ARustyCoder93
```

Lemonade

How to use ayourtch/ARustyCoder93 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ayourtch/ARustyCoder93

Run and chat with the model

lemonade run user.ARustyCoder93-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ayourtch/ARustyCoder93 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ayourtch/ARustyCoder93

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 ayourtch/ARustyCoder93

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ayourtch/ARustyCoder93 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ayourtch/ARustyCoder93

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 "ayourtch/ARustyCoder93" \
  --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"

ARustyCoder93

File size: 1,156 Bytes

fa8d379

experiment: keep-93 = the PURE UNION of the coder-grown and general-grown keep-64 expert sets
requested:  ayourtch 2026-08-16 21:24 CEST via Webex ("build the 87 expert one", coding smoke + opencode)
built:      2026-08-16T19:25:26+00:00
host:       [build host — RTX PRO 6000 workstation, hostname redacted]
source_model: /home/ayourtch/llm/ds4/gguf/DeepSeek-V4-Flash-MXFP4Experts-F16HC-F16Compressor-F16Indexer-Q8Attn-Q8Shared-Q8Out-chat-v2-mxfp4-0731.gguf
wants:      /home/ayourtch/merged-wants.tsv (same file as merge-128: normalised sum of the two censuses, +1e7 bonus on every union member)
why 93 not 87/88: GGUF needs one expert count for all layers; union size per layer is 78..93 (mean 87.3). keep=93 keeps EVERY union member in EVERY layer; layers whose union is <93 get 0-15 filler by merged rank. keep=88 would drop union members in the larger layers.
keep:       93, used 6
prune_cmd:  PRUNE_WANTS=~/merged-wants.tsv ~/hf-venv/bin/python ~/prune_experts.py SRC ~/grow-union/deepseek4-keep93.gguf 93 6
serve_cmd:  CUDA_VISIBLE_DEVICES=0 ~/llama.cpp/build-mtp/bin/llama-server -m ~/grow-union/deepseek4-keep93.gguf -ngl 99 -c 262144 -fa on