Instructions to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="john-broadway/Llama-3.2-1B-RYS-10-13-GGUF",
	filename="Llama-3.2-1B-RYS-10-13-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/Llama-3.2-1B-RYS-10-13-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/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf john-broadway/Llama-3.2-1B-RYS-10-13-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/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M

Use Docker

docker model run hf.co/john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with Ollama:
```
ollama run hf.co/john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M
```

Unsloth Studio new

How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF 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 john-broadway/Llama-3.2-1B-RYS-10-13-GGUF 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 john-broadway/Llama-3.2-1B-RYS-10-13-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for john-broadway/Llama-3.2-1B-RYS-10-13-GGUF to start chatting

Pi new

How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M

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": "john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf john-broadway/Llama-3.2-1B-RYS-10-13-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/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with Docker Model Runner:
```
docker model run hf.co/john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M
```

Lemonade

How to use john-broadway/Llama-3.2-1B-RYS-10-13-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull john-broadway/Llama-3.2-1B-RYS-10-13-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-1B-RYS-10-13-GGUF-Q4_K_M

List all available models

lemonade list

Llama-3.2-1B-RYS-10-13

Llama-3.2-1B-Instruct with layers 10-12 duplicated. The late-stack reasoning circuit runs twice on every forward pass.

16 base layers → 19 after duplication. No training, no merging, no weight changes.

Reasoning 0.00% → 64.71% (+64.71). EQ 27.11 → 90.12 (+63.01). Math 0.536 → 0.7112 (+17.52). Peak reasoning Δ across the full 22-config sweep is +76.47%; (10,13) is the best-combined pick.

Results

Metric	Baseline	RYS (10,13)	Delta
Math	0.536	0.7112	+17.52
EQ	27.11	90.12	+63.01
Reasoning	0.00%	64.71%	+64.71

The reasoning unlock. Llama-3.2-1B-Instruct has the lowest baseline reasoning in the v2 corpus (0.00% — the model fails every reasoning probe). Duplicating layers 10-12 lifts reasoning by 64.71 points at the best-combined config, and 76.47 points at the peak-reasoning config — the most dramatic RYS result in the entire 21-model corpus. The mechanism is under-training scale: latent reasoning circuitry that did not reach reliable behavior during base training, surfaced by giving the activations a second pass through the same circuit.

All 22 swept configurations boost reasoning >5%. Pick this when you want a tiny model that thinks.

Usage

llama-server -m Llama-3.2-1B-RYS-10-13-Q4_K_M.gguf -ngl 99

Full sweep data

22 configurations tested. (10,13) 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 distinct mechanisms identified: under-training scale (this model), MoE routing inefficiency (Granite-3.1-1B-A400M), 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/Llama-3.2-3B-RYS-21-24-GGUF — the math-amplifier case at the high-baseline end. Together with this model, the Llama-3.2 family spans the entire baseline-vs-magnitude curve in the v2 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.

Downloads last month: 184

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

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Model tree for john-broadway/Llama-3.2-1B-RYS-10-13-GGUF

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

(375)

this model

Collection including john-broadway/Llama-3.2-1B-RYS-10-13-GGUF

RYS Sovereign Collection v2

Collection

Qwen3-family cohort (0.6B/1.7B/8B) + cross-architecture sweep corpus (21 variants, 10 families). v1 collection: Qwen2.5 cross-scale + Qwen3-32B. • 18 items • Updated 11 days ago