Run Muse Glimmer for Native Vibe Coding with llama.cpp, DFlash, and Pi

Muse Glimmer is gaining consideration within the native AI group and is being in contrast with Qwen’s 27B-class fashions. In lots of instances, it’s performing higher, particularly for native coding and agentic workflows.

Meta seems robust within the open-model house, and with a couple of extra iterations, fashions like this might begin competing carefully with proprietary methods. As an AI fanatic, it is thrilling to have the ability to run this stage of AI domestically.

On this information, we’ll run Muse Glimmer with llama.cpp, velocity it up with DFlash, and join it to Pi for native vibe coding. Will probably be capable of construct, check, and debug a mission straight from the terminal.

1. Downloading Muse Glimmer

First, obtain the primary Muse Glimmer mannequin and its DFlash drafter from Hugging Face.

Set up the Hugging Face CLI:

curl -LsSf https://hf.co/cli/set up.sh | bash
echo 'export PATH="/root/.native/bin:$PATH"' >> ~/.bashrc
supply ~/.bashrc

Log in:

hf auth login

Create a mannequin listing:

mkdir -p /workspace/muse-glimmer

Obtain the 16.8 GB primary mannequin:

hf obtain meta-models/Muse-Glimmer-30B-GGUF 
  muse-glimmer-30B-kquant-17gb.gguf 
  --local-dir /workspace/muse-glimmer

Obtain the 1.63 GB DFlash drafter:

hf obtain meta-models/Muse-Glimmer-30B-GGUF 
  dflash-kquant.gguf 
  --local-dir /workspace/muse-glimmer

Each information will likely be saved in /workspace/muse-glimmer.

2. Putting in and Working llama.cpp

Subsequent, set up llama.cpp with CUDA help and use it to serve Muse Glimmer with the DFlash drafter.

Set up and construct llama.cpp:

cd /workspace

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
git pull origin grasp
cmake -B construct -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Launch
cmake --build construct --config Launch -j$(nproc)
ln -sf "$(pwd)/construct/bin/llama-server" /root/.native/bin/llama-server

Test the set up:

git pull origin grasp
cmake -B construct -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Launch
cmake --build construct --config Launch -j$(nproc)
ln -sf "$(pwd)/construct/bin/llama-server" /root/.native/bin/llama-server

It’s best to see the put in llama.cpp model and construct info.

Now begin Muse Glimmer with DFlash speculative decoding:

llama-server 
  -m /workspace/muse-glimmer/muse-glimmer-30B-kquant-17gb.gguf 
  -md /workspace/muse-glimmer/dflash-kquant.gguf 
  --spec-type draft-dflash 
  --spec-draft-n-max 15 
  -ngl all 
  --spec-draft-ngl all 
  -fa on 
  --ctx-size 16384 
  --alias muse 
  --host 0.0.0.0 
  --port 8080 
  --jinja

Right here, llama.cpp masses the primary mannequin and the separate DFlash drafter onto the GPU, utilizing speculative decoding to enhance technology velocity.

3. Testing Muse Glimmer

As soon as the server is operating, you’ll be able to check Muse Glimmer straight by means of the built-in llama.cpp Net UI.

Open:

http://localhost:8080/

In my preliminary testing, I used to be getting round 46 tokens/second, which is already fairly good.

Throughout longer coding duties, I noticed speeds attain round 127 tokens/second, making the mannequin really feel a lot sooner for agentic coding workflows.

The outcomes had been combined, although. Muse Glimmer created an HTML sport for me, nevertheless it did not work significantly nicely. For this sort of activity, I nonetheless discovered Qwen3.8-27B noticeably higher at producing working HTML apps and video games.

4. Putting in Pi Coding Agent

Subsequent, set up Pi and join it to the llama.cpp server operating Muse Glimmer.

Set up Pi:

curl -fsSL https://pi.dev/set up.sh | sh

Then set up Hugging Face’s llama.cpp extension:

pi set up git:github.com/huggingface/pi-llama

Restart your terminal after set up.

The pi-llama extension robotically connects to:

http://localhost:8080/v1

It detects the fashions being served by llama.cpp, so you don’t want to configure fashions.json manually.

5. Beginning Native Vibe Coding

Now create a mission and choose Muse Glimmer because the mannequin inside Pi.

Create an empty mission:

mkdir -p /workspace/glimmer-test
cd /workspace/glimmer-test

Launch Pi:

pi

Inside Pi, run:

/mannequin

Seek for:

llama-cpp

Then choose:

muse

Muse Glimmer ought to now be out there by means of Pi’s llama-cpp supplier.

6. Testing Muse Glimmer as a Coding Agent

Lastly, give Muse Glimmer an entire coding activity and let it construct, check, and debug the mission itself.

I used this immediate:

Construct an entire Python activity administration API from scratch utilizing FastAPI.

Necessities:
– Create a clear mission construction.
– Add endpoints to create, record, replace, and delete duties.
– Use SQLite for persistence.
– Add enter validation and error dealing with.
– Add pytest assessments for all endpoints.
– Create necessities.txt and README.md.
– Run the assessments your self.
– Repair any errors and rerun the assessments till all the pieces passes.

Don’t ask me to create information or run instructions for you. Construct and check the entire mission your self.

Muse Glimmer constructed the mission in round 2 minutes.

To check it domestically:

pip set up -r necessities.txt
uvicorn app.primary:app --reload

Open the API documentation at:

http://localhost:8000/docs

As an alternative of manually testing each endpoint, I additionally requested Muse Glimmer to check the entire API itself and provides me a closing report.

For native agentic coding, that is the place Muse Glimmer impressed me most. It was quick, dealt with multi-step duties nicely, and took only some seconds to establish and repair points throughout debugging.

Remaining Ideas

Muse Glimmer is an effective indicator of how far native AI coding has come, particularly when Meta gives the official mannequin information and really helpful configuration. For me, it was very simple to arrange and begin utilizing.

There are nonetheless a couple of tough edges, however as Muse Glimmer, llama.cpp, DFlash, and the encircling tooling matures, I anticipate higher outcomes, sooner speeds, and stronger agentic coding efficiency domestically.

In case you have an RTX 3090, 4090, or 5090, I might extremely advocate making an attempt both Muse Glimmer or Qwen3.8 domestically. At this level, it’s turning into tougher to justify paying for each AI coding request or sharing your code and information with third-party companies.

Native fashions are already getting surprisingly near the expertise of fashions like GLM-5.2, and I feel the subsequent few iterations will make native AI coding much more compelling.

Abid Ali Awan (@1abidaliawan) is a licensed information scientist skilled who loves constructing machine studying fashions. At present, he’s specializing in content material creation and writing technical blogs on machine studying and information science applied sciences. Abid holds a Grasp’s diploma in expertise administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college kids battling psychological sickness.