Instructions to use john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF",
	filename="Qwen2.5-0.5B-RYS-3-7-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf john-broadway/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M

Use Docker

docker model run hf.co/john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M

Ollama
How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF with Ollama:
```
ollama run hf.co/john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M
```

Unsloth Studio

How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-GGUF to start chatting

How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-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/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF with Docker Model Runner:
```
docker model run hf.co/john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M
```

Lemonade

How to use john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull john-broadway/Qwen2.5-0.5B-RYS-3-7-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-0.5B-RYS-3-7-GGUF-Q4_K_M

List all available models

lemonade list

Qwen2.5-0.5B-RYS-3-7-GGUF

A layer-duplication ("RYS" — Repeat Your Self, David Ng) variant of Qwen/Qwen2.5-0.5B-Instruct: layers 3–6 duplicated, 24 → 28 layers. No training, merging, or weight changes. GGUF (imatrix Q-quants).

⚠️ Evaluation status — please read (updated 2026-06)

On a controlled re-test, the headline gains for this model do not hold up. This card is being corrected to say so.

The original card reported reasoning 35.29% → 58.82% (+23.53%) and an EQ lift (37.38 → 55.23), calling this an "EQ specialist." A re-run on a current llama.cpp build found:

Reasoning does not reproduce. The baseline reproduces exactly (35.29%), but the (3,7) config scored 29.41% — below the baseline. The original +23.5% was probe variance (the reasoning probe is 17 questions, so each one is ±5.9%).
The EQ number is not a reliable capability signal. On output inspection, the EQ probe on this model is satisfied by emitting a near-constant rating vector across different scenarios — it outscores the base model's degenerate output without actually reading the scenario. So the "EQ lift" reflects the scoring metric, not emotional understanding.

These scores come from a lightweight search probe (16 math / 16 EQ / 17 reasoning questions) used to find productive layer blocks, not a validated benchmark. A standard-harness re-eval (lm-eval-harness / EQ-Bench at full N) is in progress.

Bottom line: treat this as a normal Qwen2.5-0.5B-Instruct with layers 3–6 duplicated. Published for transparency of the RYS sweep — not as an established "EQ specialist."

Original sweep numbers (search probe — kept for the record)

probe	reported baseline	reported (3,7)	re-test note
Reasoning (17 q)	35.29%	58.82%	did not reproduce — (3,7) → 29.41% (below baseline)
EQ (16 q)	37.38	55.23	EQ probe gamed by a constant guess; not validated EQ
Math (16 q)	0.492	0.424	search-probe score

Run it

llama-server -m Qwen2.5-0.5B-RYS-3-7-Q4_K_M.gguf -ngl 99

Method · data · attribution

Method: layer duplication — Repeat Your Self (David Ng); toolkit llm-circuit-finder (alainnothere).
Raw sweep data: rys-sovereign-collection-v2.
Built by John Broadway with Claude. The method and the raw data are real; the interpretation of these probe deltas as capability is what this update corrects.