Instructions to use radiosilent/vectorstream-mbestudio-sysml-assistant 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 radiosilent/vectorstream-mbestudio-sysml-assistant 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 radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M # Run inference directly in the terminal: llama cli -hf radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M # Run inference directly in the terminal: llama cli -hf radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
Use Docker
docker model run hf.co/radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use radiosilent/vectorstream-mbestudio-sysml-assistant with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "radiosilent/vectorstream-mbestudio-sysml-assistant" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "radiosilent/vectorstream-mbestudio-sysml-assistant", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
- Ollama
How to use radiosilent/vectorstream-mbestudio-sysml-assistant with Ollama:
ollama run hf.co/radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
- Unsloth Desktop
- Pi
How to use radiosilent/vectorstream-mbestudio-sysml-assistant with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use radiosilent/vectorstream-mbestudio-sysml-assistant with Docker Model Runner:
docker model run hf.co/radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
- Lemonade
How to use radiosilent/vectorstream-mbestudio-sysml-assistant with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
Run and chat with the model
lemonade run user.vectorstream-mbestudio-sysml-assistant-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use radiosilent/vectorstream-mbestudio-sysml-assistant with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf radiosilent/vectorstream-mbestudio-sysml-assistant: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 radiosilent/vectorstream-mbestudio-sysml-assistant:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use radiosilent/vectorstream-mbestudio-sysml-assistant with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf radiosilent/vectorstream-mbestudio-sysml-assistant: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 "radiosilent/vectorstream-mbestudio-sysml-assistant: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"
MBE-30B
MBE-30B is the 30B local model that MBEstudio offers for writing SysML v2 models. It runs on a single 16 GB GPU through Ollama, and it is the model the studio's generation loop is tuned around for local use.
What this release is
This first release is not fine-tuned. It is Qwen3-Coder-30B-A3B-Instruct (30.5B parameters, 3.3B active, mixture of experts) at Q4_K_M, packaged with:
- a SysML v2 system prompt for MBEstudio's profile and the SysML v2 standard library,
- a 32k context window by default (the studio runs it at 64k) and sampling settings that match the studio.
We picked this base by evaluation. A fine-tuned release will replace these weights only if it beats this base on the same evaluation.
Evaluation
The evaluation runs MBEstudio's full generation loop: plan, write each package, compile with the MBEstudio SysML v2 compiler, and repair from the compiler's diagnostics. It runs at a 32k context window. Each brief is held out from the few-shot examples.
| Brief | Packages | Clean on first pass | Repairs | Errors at the end |
|---|---|---|---|---|
| Commuter EV | 3 | 0 of 3 | 2 | 0 |
| L4 robotaxi | 3 | 1 of 3 | 2 | 0 |
| Island dredger | 3 | 0 of 3 | 2 | 0 |
| Midsize aircraft | 3 | 1 of 3 | 9 | 4 (at 64k; see below) |
The first three briefs ended with zero compiler errors. The midsize aircraft is the largest brief: at a 32k context its parts package did not fit, so it was run at 64k, the context the studio uses for this model. It produced all three packages from the model and ended with 4 errors, all M0604: three envelope dimensions defined in terms of themselves, which the repair loop did not resolve in four attempts. That run took about 27 minutes on an RTX 5080. In an earlier run of the same evaluation, qwen3:30b, gpt-oss:20b and qwen3:14b finished 0 of 4 briefs clean.
Most first-pass errors are MBEstudio profile rules, such as metadata values written with units and imports without visibility. The repair loop fixes them. The model needs that compile-and-repair loop. Used on its own, without a compiler in the loop, expect output that does not compile.
Use with Ollama
hf download radiosilent/vectorstream-mbestudio-sysml-assistant mbe-30b-Q4_K_M.gguf Modelfile --local-dir mbe-30b
cd mbe-30b
ollama create mbe-30b -f Modelfile
ollama run mbe-30b
The Modelfile uses Ollama's built-in Qwen3-Coder renderer (RENDERER/PARSER qwen3-coder); the GGUF's own chat template does not continue an assistant prefill, which MBEstudio uses to start each code block. About 19 GB on disk. On a 16 GB GPU, part of the model runs on the CPU. Expect roughly 60 tokens/s on an RTX 5080 at a 24k to 32k context.
Use in MBEstudio
Pick mbe-30b in the studio's model menu when the studio runs against local Ollama.
License
Apache 2.0, the license of the Qwen3-Coder base model. The weights are the Q4_K_M quantization published by Unsloth, unchanged.
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Model tree for radiosilent/vectorstream-mbestudio-sysml-assistant
Base model
Qwen/Qwen3-Coder-30B-A3B-Instruct