Instructions to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF 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 john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M

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 john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M

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 john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M

Use Docker

docker model run hf.co/john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF with Ollama:
```
ollama run hf.co/john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M
```
Unsloth Desktop

How to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF: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": "john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF with Docker Model Runner:
```
docker model run hf.co/john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M
```

Lemonade

How to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Granite-3.1-1B-A400M-RYS-12-15-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF: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 john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF: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 "john-broadway/Granite-3.1-1B-A400M-RYS-12-15-GGUF: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"

Granite-3.1-1B-A400M-RYS-12-15

Granite-3.1-1B-A400M-Instruct (MoE, 400M active per token) with layers 12-14 duplicated. Mid-stack expert chain runs twice on every forward pass.

24 base layers → 27 after duplication. No training, no merging, no weight changes.

Reasoning 23.53% → 76.47% (+52.94). EQ 84.41 → 70.89 (−13.52). Math 0.394 → 0.4707 (+7.67).

Results

Metric	Baseline	RYS (12,15)	Delta
Math	0.394	0.4707	+7.67
EQ	84.41	70.89	−13.52
Reasoning	23.53%	76.47%	+52.94

The MoE reasoning unlock. First MoE in the v2 corpus. Duplicating layers 12-14 lifts reasoning by +52.94 — but EQ degrades on every one of the 32 swept configurations (range −6.76 to −47.07). Hypothesis: layer duplication preserves a localized reasoning circuit (a tight expert chain) but disrupts EQ-relevant experts that route across more diverse paths. The within-family dense comparison (Granite-3.1-2B-RYS-19-24, 40 layers) shows weaker reasoning lift (+11.76%) and stable EQ — consistent with the MoE-specific hypothesis.

31 of 32 swept configs boost reasoning >5%. Pick this when reasoning gain matters more than the EQ trade-off.

Usage

llama-server -m Granite-3.1-1B-A400M-RYS-12-15-Q4_K_M.gguf -ngl 99

Full sweep data

32 configurations tested. (12,15) block-3 is the best-combined pick. Full per-config sweep + cross-architecture analysis: v2 dataset.

Part of the RYS Sovereign Collection v2.

Where this sits in the Sovereign Collection

v1 — Qwen2.5 cross-scale + Qwen3-32B headline crossover. 5 model repos: 0.5B EQ specialist / 1.5B daily driver / 7B math specialist (+ AWQ) / Qwen3-32B "Big Boy."

v2 — cross-architecture corpus. 21 model variants across 10 architecture families. Inverse correlation (r = −0.726): weak baselines lift more, in their weakest dimension. Three mechanisms identified: under-training scale (Llama-3.2-1B), MoE routing inefficiency (this model), specialization training trade-off (Qwen2.5-Coder-1.5B). Plus EQ-amplifier extreme (TinyLlama-1.1B) and a first published negative result (SmolLM2-1.7B). 13 deployable RYS-applied weight repos covering every non-zero-lift variant.

Within-family sibling: john-broadway/Granite-3.1-2B-RYS-19-24-GGUF — the dense 2B sibling. The MoE-vs-dense contrast within the Granite family is one of the cleanest mechanism-isolating cases in the corpus.

Credit

John Broadway, with collaboration from Claude (Opus 4.6 in April 2026 sweep generation and build pipeline; Opus 4.7 in May 2026 cross-architecture analysis and publication). Original RYS method by David Ng on Qwen2-72B; sweep + probe toolkit by alainnothere.