Instructions to use RalphLabsAI/ralph-crowns with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RalphLabsAI/ralph-crowns 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 RalphLabsAI/ralph-crowns
# Run inference directly in the terminal:
llama cli -hf RalphLabsAI/ralph-crowns

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RalphLabsAI/ralph-crowns
# Run inference directly in the terminal:
llama cli -hf RalphLabsAI/ralph-crowns

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 RalphLabsAI/ralph-crowns
# Run inference directly in the terminal:
./llama-cli -hf RalphLabsAI/ralph-crowns

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 RalphLabsAI/ralph-crowns
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RalphLabsAI/ralph-crowns

Use Docker

docker model run hf.co/RalphLabsAI/ralph-crowns

LM Studio
Jan

vLLM

How to use RalphLabsAI/ralph-crowns with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RalphLabsAI/ralph-crowns"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RalphLabsAI/ralph-crowns",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RalphLabsAI/ralph-crowns

Ollama
How to use RalphLabsAI/ralph-crowns with Ollama:
```
ollama run hf.co/RalphLabsAI/ralph-crowns
```
Unsloth Desktop
Docker Model Runner
How to use RalphLabsAI/ralph-crowns with Docker Model Runner:
```
docker model run hf.co/RalphLabsAI/ralph-crowns
```

Lemonade

How to use RalphLabsAI/ralph-crowns with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RalphLabsAI/ralph-crowns

Run and chat with the model

lemonade run user.ralph-crowns-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Lead with Ralph phone-first promise

by bitzic - opened 23 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+10

-5

Files changed (1) hide show

README.md +10 -5

README.md CHANGED Viewed

@@ -11,13 +11,18 @@ tags: [gguf, conversational, qwen3, quantized, compression, bittensor]
        alt="Ralph — open model compression on Bittensor SN40" width="100%">
 </p>
-# Ralph crowns — Qwen3-8B compressed in public
-**Four downloadable GGUFs. One admitted Qwen3-8B architecture. Each file holds a current bit-tier
-crown in Ralph, the open model-compression tournament on Bittensor SN40.**
-The files are published together so users have one stable place to download the current crowns,
-while the source builders and exact scored revisions remain visible.
 ## Start here: the 2.94 GB sub2 crown

        alt="Ralph — open model compression on Bittensor SN40" width="100%">
 </p>
+# The same model. Small enough for your phone.
+### Ralph crowns — Qwen3-8B compressed in public
+**Four downloadable GGUFs, from 1.16 GB to 4.61 GB. One admitted Qwen3-8B architecture. Fewer
+bits. Each file holds a current bit-tier crown in Ralph, the open model-compression tournament on
+Bittensor SN40.**
+“Same model” means the same admitted architecture and parameter structure—not identical weights,
+guaranteed identical behavior, or universal phone compatibility. The files are published together
+so users have one stable place to download the current crowns, while the source builders and exact
+scored revisions remain visible.
 ## Start here: the 2.94 GB sub2 crown