Instructions to use john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF",
	filename="TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf john-broadway/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M

Use Docker

docker model run hf.co/john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "john-broadway/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M

Ollama
How to use john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF with Ollama:
```
ollama run hf.co/john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M
```

Unsloth Studio

How to use john-broadway/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-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/TinyLlama-1.1B-RYS-10-14-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF with Docker Model Runner:
```
docker model run hf.co/john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M
```

Lemonade

How to use john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyLlama-1.1B-RYS-10-14-GGUF-Q4_K_M

List all available models

lemonade list

TinyLlama-1.1B-RYS-10-14-GGUF

A layer-duplication ("RYS" — Repeat Your Self, David Ng) variant of TinyLlama/TinyLlama-1.1B-Chat-v1.0: layers 10–13 duplicated, 22 → 26 layers. No training, merging, or weight changes. GGUF.

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

The original card headlined an EQ jump of 4.65 → 52.50 (+47.85) as "the largest EQ gain in the corpus." Direct output inspection confirms this is a scoring artifact, not a gain in emotional intelligence.

What the model actually emits (inspected on the current stack):

Base fails to follow the rating format — on many scenarios it returns prose ("David is furious…") instead of numbers, so the probe can't parse it and scores ~0. Its low EQ is largely a parse failure.
(10,14) emits a near-constant rating vector (~`5,3,8,2`) for every scenario — the same numbers for "your spouse is cheating" as for "you received an award." It isn't reading the scenario; it just produces a parseable constant guess that averages ~60.

So the "EQ unlock" is the probe rewarding a parseable constant guess over the base model's unparseable prose — not emotional understanding. These are 16-question search probes, not a validated benchmark.

Bottom line: treat this as a normal TinyLlama-1.1B-Chat with layers 10–13 duplicated. The "EQ unlock" is a measurement artifact, confirmed by inspection — not an established capability gain.

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

probe	reported baseline	reported (10,14)	note
EQ (16 q)	4.65	52.50	verified artifact: base outputs unparseable prose (~0); (10,14) emits a constant `5,3,8,2` for all scenarios. Not emotional understanding
Reasoning (17 q)	29.41%	23.53%	down 1 question — no reasoning gain
Math (16 q)	0.296	0.296	flat

Run it

llama-server -m TinyLlama-1.1B-RYS-10-14-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 the EQ probe delta as capability is what this update corrects.

License

Apache-2.0.

Downloads last month: 56

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for john-broadway/TinyLlama-1.1B-RYS-10-14-GGUF

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Quantized

(150)

this model

Collection including john-broadway/TinyLlama-1.1B-RYS-10-14-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 May 13