Instructions to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra 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 Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra 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 Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra: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 Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra: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 Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
Use Docker
docker model run hf.co/Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
- Ollama
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra with Ollama:
ollama run hf.co/Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
- Unsloth Studio
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra 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 Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra 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 Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra to start chatting
- Pi
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra: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": "Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra with Docker Model Runner:
docker model run hf.co/Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
- Lemonade
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
Run and chat with the model
lemonade run user.muse-glimmer-xrpl-bug-triage-ultra-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra: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 Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Krypto-Whitehat/muse-glimmer-xrpl-bug-triage-ultra:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Muse Glimmer 30B โ XRPL Bug Triage & Verification (v4, agentic policy)
v4 of the QLoRA fine-tune. It keeps everything v3 learned (mechanism-based triage: no verdict from report style, verdicts name the protocol gate) and adds an agentic source policy oriented at the current rippled develop branch:
- No verdict from stale weights for novel mechanisms. Reports whose killing/confirming mechanism is not among the code-verified hard gates get an honest UNPROVEN instead of a guess โ the intended next step is a host-side fetch (raw.githubusercontent.com develop files, XRPL-Standards) or a read of the local clones, after which the model verdicts on the supplied source text.
- Never invents absence. Develop-only features (ConfidentialTransfer with ConfidentialMPTSend, BatchV1_1, Sponsor, PermissionDelegationV1_1, DynamicMPT, TokenEscrow) are known to exist on develop; "not on Mainnet" is treated as a deployment note, not a refutation. If no source is reachable the model answers UNPROVEN with pointers instead of "does not exist".
- Direct verdicts for verified gates. Batch spfSponsorReserve/sfSponsor (temINVALID_FLAG), match-time domain re-check (offerInDomain), XLS-80 4.2 domain-delete design, invariant-rollback, self-DoS, privilege-required and remix-of-refuted-premises are verdicted immediately with the gate named.
Training (v4.1)
- 79 mechanism-labeled policy samples + 5 agentic fetch traces (FETCH -> TOOL_RESULT -> VERDICT, including a network-dead -> local-clone trace), agentic traces repeated 4x (~20% of steps)
- Harmonized agentic system prompt in every sample; multi-turn label masking (every assistant turn learned); prompt-left truncation that never cuts an assistant segment
- QLoRA r=16 alpha=32, 3 epochs, lr 2e-5, merged onto the pristine BF16 base
- Labels re-derived from real maintainer triage decisions of the 15 XRPL maintainers (mvadari, JoelKatz, vinniefalco, ahbritto, nbougalis, seelabs, ximinez, scottschurr, HowardHinnant, miguelportilla, mellery451, rec, bthomee, mathbunnyru, justmoon) with authority phrases removed
Gate results (llama-server --jinja, LM-Studio-equivalent chat calls)
- E1 Sponsored Batch + MPT DEX stale offers -> FALSE_POSITIVE (preflight + match-time gates) โ correct without any maintainer comment
- E2 PermissionedDomainDelete dangling sfDomainID -> FALSE_POSITIVE (XLS-80 4.2)
- Remix, invariant-rollback, API/build, spec-gap, self-DoS, privilege-required -> all correctly FALSE_POSITIVE; speculation -> UNPROVEN
- Real VALID finding in the verified-gate family -> correctly VALID_BUG with the confirming gate named
- Novel-mechanism report without source -> UNPROVEN "no source consulted" (agentic-honest); offline existence question -> UNPROVEN with pointers, no absence hallucination
Known limitations (honest): subtle VALID findings outside the verified-gate family can be refuted with a misapplied gate or hedged as UNPROVEN; the model does not emit an explicit FETCH list, it answers UNPROVEN directly โ a host agent should treat UNPROVEN as the fetch trigger.
Quantizations
| File | Format | Size |
|---|---|---|
MuseGlimmer-30B-XRPL-Q4_K_M.gguf |
Q4_K_M | 16,935,291,488 bytes |
MuseGlimmer-30B-XRPL-Q5_K_M.gguf |
Q5_K_M | 19,811,375,712 bytes |
Both carry tokenizer.ggml.eos_token_id = 200008 (<|eot|>); the official
Muse Glimmer ATEM chat template is embedded unchanged.
Usage
LM Studio (no tools): expect direct verdicts on the verified-gate families and UNPROVEN on novel mechanisms โ that is the intended honesty. With a host agent (ZCode, MCP, or any RAG over the develop branch + XLS specs): supply the fetched source text as a TOOL_RESULT-style user turn and the model will verdict against it. Use the system prompt from the repository discussion (v4.1 agentic policy). max_tokens high enough (reasoning channel counts); temperature 0.2.
Intended use & limitations
Triage support for XRPL security review on report text. Fine-tuning carries the policy, not the live codebase โ pair with retrieval over rippled develop and XRPL-Standards for current-state verification. Not a replacement for a security audit.
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