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
- llama.cpp
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
The same model. Small enough for your phone.
Ralph crowns โ 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.
โSame modelโ means the same admitted architecture and parameter structureโnot 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
ralph-qwen3-8b-sub2.gguf is the first file we recommend for a phone test.
| Exact size | 2,937,263,168 bytes (2.94 GB decimal) |
| Code bits / weight | 2.2626 |
| Crowned | Round 7 |
| Embedded chat-template key | Yes |
| SHA-256 | 9c45bf0ac486e793bd761a99ce4b9d965ec12e70f2d420e02af16797c71f6b50 |
hf download RalphLabsAI/ralph-crowns ralph-qwen3-8b-sub2.gguf --local-dir ./ralph-crowns
llama-cli -m ./ralph-crowns/ralph-qwen3-8b-sub2.gguf -cnv --jinja -p "Explain one practical use of low-bit quantization."
Find it in PocketPal
- Open Models โ Add from Hugging Face.
- Search for RalphLabsAI, then open ralph-crowns.
- Choose ralph-qwen3-8b-sub2.gguf.
Physical-iPhone device lab
Join Device Lab #1 โ share a success or failure. These are Ralph team-authored tests. Add your own device, app version, settings, and result to the pinned discussion; a failed load is useful evidence too.
One configured 2.94 GB Round 7 sub2 crown completed the same normal PocketPal chat on four physical cloud iPhones through the local llama.cpp Metal runtime.
| Physical device | OS | Normal PocketPal chat | TTFT |
|---|---|---|---|
| iPhone 15 | iOS 26.6 | 10.58 tok/s | 838 ms |
| iPhone 16 | iOS 18.6 | 12.36 tok/s | 439 ms |
| iPhone 17 | iOS 26.6 | 16.77 tok/s | 427 ms |
| iPhone 17 Pro Max | iOS 26.6 | 18.54 tok/s | 400 ms |
Watch the 30-second matrix ยท Inspect all four raw receipts ยท Verify the public payload
Test configuration: PocketPal 1.17.3 (147), context 2,048, fixed prompt, one connected session per device. The expected model digest was verified against Ralph's local mirror, not computed on-device. Every device displayed PocketPal's Memory tight warning and nevertheless loaded and completed the prompt.
Physical BrowserStack devices; connected test. BrowserStack re-signing strips PocketPal's increased-memory and extended-virtual-addressing entitlements, so these are local-Metal backend/throughput observationsโnot App Store memory-fit, offline, universal-compatibility, or broad-quality certification. The dataset includes full settings, exact responses, screenshots, checksums, and untouched recordings.
Current crowns โ through Round 7
| File | Tier | Code bits / weight | Exact size | Crown status | Retention at crown | Builder | Chat-template key |
|---|---|---|---|---|---|---|---|
ralph-qwen3-8b-binary.gguf |
binary | 1.0 | 1,158,964,160 B | New in Round 7 | 0.263616 | ArizonaZZZ | No |
ralph-qwen3-8b-ternary.gguf |
ternary | 1.714 | 2,477,212,736 B | Crowned in Round 5; held in Round 7 | 0.253732 | tensor-tailor | Yes |
ralph-qwen3-8b-sub2.gguf |
sub2 | 2.2626 | 2,937,263,168 B | New in Round 7 | 0.282457 | boweizh1204 | Yes |
ralph-qwen3-8b-sub4.gguf |
sub4 | 4.0 | 4,614,304,896 B | New in Round 7 | 0.320923 | boweizh1204 | Yes |
Exact checksums
05c568169fc180067172cbdd38c1a9f5c249a556ffdef1dacd7172bad40fab58 ralph-qwen3-8b-binary.gguf
c643cb42575a7a85b7518fd935973202c5686db270ef8b593afa8616a986154a ralph-qwen3-8b-ternary.gguf
9c45bf0ac486e793bd761a99ce4b9d965ec12e70f2d420e02af16797c71f6b50 ralph-qwen3-8b-sub2.gguf
b2ec80dce90258bbe9555a67983dea595d91f35cae48ca0d54c714eae8100edf ralph-qwen3-8b-sub4.gguf
The same filenames, byte counts, hashes, pinned sources, and chat-template flags are available in
crowns.json.
What the crown means
Ralph retention is a within-protocol compression-fidelity measurement: it measures how closely an artifact reproduces the pinned comparison parent's effect on configured judge models, including the protocol's live language/depth floors. It is not a general capability benchmark, an intelligence percentage, or evidence of device speed.
Round 7 uses three recorded judges and a fresh public exam. A challenger takes a crown only through the protocol's recorded point-estimate margin or persistence path and must satisfy the per-judge floor rule. A positive paired-bootstrap lower bound can affect the candidate weight vector without changing the crown.
Provenance
The crown publisher fetches the miner's full behavior-affecting package at the pinned revision
in the round record and recomputes its content hash. It mirrors the GGUF only when that hash matches
the record's model_id; the GGUF is uploaded without conversion or repacking.
For this release, the source owners subsequently added artifact-level Apache-2.0 declarations and notices. Hugging Face LFS metadata was checked again on 2026-09-14: every current source GGUF, scored revision, and mirrored file has the same SHA-256 shown above. The documentation commits did not change the models.
Credit for the weights belongs to the builders named above and the miner hotkeys recorded in
crowns.json. Ralph provides a content-verified mirror and tournament record; it is not the origin
of the weights.
Admission checks architecture and tensor shapes against Qwen/Qwen3-8B. They do not
cryptographically prove lineage from the parent's weights; base_model identifies the pinned
comparison and admission target.
License
Each exact GGUF listed by checksum above is released under the Apache License 2.0 by its source
owner. This repository includes the license text in LICENSE and preserves the four
source notices in NOTICE.
All four are compressed derivatives of Qwen/Qwen3-8B,
Copyright 2024 Alibaba Cloud, Apache-2.0. The binary crown also incorporates bit-exact signs from
prism-ml/Bonsai-8B-unpacked, Apache-2.0.
The source notices describe each modification, data provenance, and MIT-licensed llama.cpp
tooling. Preserve LICENSE, NOTICE, source attribution, and modification statements when
redistributing these files.
The grant is tied here to the exact listed files and hashes. A future crown change requires a new artifact-level license check; this card does not grant terms on behalf of a future builder.
Running the other crowns
For the ternary and sub4 files, replace the filename in the sub2 commands above. Runtime support depends on the exact quantization type, runtime build, backend, available memory, and context settings. Test the exact file on the target hardware before making compatibility or performance claims.
The binary file has no embedded chat-template key, so this card does not prescribe a canonical chat command for it. Establish and report a tested external template/runtime combination without repacking or replacing the canonical bytes.
Help build the device evidence
If you test a crown, share both successes and failures in the pinned Device Lab discussion. A useful receipt includes device model, OS and app version, exact GGUF filename, checksum status, context size, runtime settings, load result, prompt-processing speed, generation speed, and the exact failure if it did not run. Remove notifications, account identifiers, serial numbers, and other private device information from screenshots.
Audit levels
- L0 verifies signatures and recomputes arithmetic, crown decisions, and candidate vectors from published measurements.
- L1 re-derives item selection and checks the pinned pool.
- L2 re-runs a recorded judge over frozen text; complete multi-judge coverage requires every recorded judge.
- L3 loads artifacts and regenerates their steps, binding frozen text to model execution.
L2/L3 require the recorded inputs and compatible runtimes. The records and verifier distinguish what can be checked from data alone from what requires inference hardware.
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We're not able to determine the quantization variants.