Instructions to use john-broadway/SmolLM2-135M-RYS-18-22-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use john-broadway/SmolLM2-135M-RYS-18-22-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="john-broadway/SmolLM2-135M-RYS-18-22-GGUF",
	filename="SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf john-broadway/SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf john-broadway/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M

Use Docker

docker model run hf.co/john-broadway/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use john-broadway/SmolLM2-135M-RYS-18-22-GGUF with Ollama:
```
ollama run hf.co/john-broadway/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M
```

Unsloth Studio new

How to use john-broadway/SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-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/SmolLM2-135M-RYS-18-22-GGUF to start chatting

Docker Model Runner
How to use john-broadway/SmolLM2-135M-RYS-18-22-GGUF with Docker Model Runner:
```
docker model run hf.co/john-broadway/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M
```

Lemonade

How to use john-broadway/SmolLM2-135M-RYS-18-22-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull john-broadway/SmolLM2-135M-RYS-18-22-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-135M-RYS-18-22-GGUF-Q4_K_M

List all available models

lemonade list

SmolLM2-135M-RYS-18-22

SmolLM2-135M-Instruct with layers 18-21 duplicated. The late-stack reasoning + EQ circuit runs twice on every forward pass.

30 base layers → 34 after duplication. No training, no merging, no weight changes.

Reasoning 17.65% → 35.30% (+17.65). EQ 44.53 → 57.58 (+13.05). Math 0.315 → 0.303 (−1.20).

Results

Metric	Baseline	RYS (18,22)	Delta
Math	0.315	0.303	−1.20
EQ	44.53	57.58	+13.05
Reasoning	17.65%	35.30%	+17.65

The tiniest responder. SmolLM2-135M is the smallest model in the v2 corpus by an order of magnitude. RYS lifts both reasoning (+17.65 absolute) AND EQ (+13.05) simultaneously — the response is unremarkable on its own, but the comparison to sibling SmolLM2-1.7B (which lifts zero percent on reasoning) makes this card load-bearing: it falsifies the "SmolLM2 training recipe doesn't work with RYS" hypothesis. The 1.7B negative result is uniquely anomalous within the family, not architectural.

Pick this when you want the smallest possible model with reasoning + EQ lift. At 110MB Q4_K_M, this is the lightest RYS-applied checkpoint in the collection.

Usage

llama-server -m SmolLM2-135M-RYS-18-22-Q4_K_M.gguf -ngl 99

Full sweep data

40 configurations tested. (18,22) block-4 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.

v2 — cross-architecture corpus. 21 model variants across 10 architecture families. Inverse correlation (r = −0.726): weak baselines lift more, in their weakest dimension. 13 deployable RYS-applied weight repos covering every non-zero-lift variant.

SmolLM2 family picture (all Q4_K_M):

135M (this card) — baseline reasoning 17.65%, peak Δ +17.65% (responds)
SmolLM2-360M-RYS-12-15-GGUF — baseline reasoning 29.41%, peak Δ +23.53% (responds)
SmolLM2-1.7B-RYS-eval — baseline reasoning 58.82%, peak Δ +0.00% (does NOT respond; eval-only — no RYS-applied weights since no lift to deliver)

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.