Instructions to use locailabs/Juno-N-Coder-25B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="locailabs/Juno-N-Coder-25B-A3B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("locailabs/Juno-N-Coder-25B-A3B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF 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 locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf locailabs/Juno-N-Coder-25B-A3B-GGUF: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 locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf locailabs/Juno-N-Coder-25B-A3B-GGUF: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 locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "locailabs/Juno-N-Coder-25B-A3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "locailabs/Juno-N-Coder-25B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M

SGLang

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "locailabs/Juno-N-Coder-25B-A3B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "locailabs/Juno-N-Coder-25B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "locailabs/Juno-N-Coder-25B-A3B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "locailabs/Juno-N-Coder-25B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with Ollama:
```
ollama run hf.co/locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF 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 locailabs/Juno-N-Coder-25B-A3B-GGUF 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 locailabs/Juno-N-Coder-25B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for locailabs/Juno-N-Coder-25B-A3B-GGUF to start chatting

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf locailabs/Juno-N-Coder-25B-A3B-GGUF: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": "locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf locailabs/Juno-N-Coder-25B-A3B-GGUF: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 "locailabs/Juno-N-Coder-25B-A3B-GGUF: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"

Docker Model Runner
How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Juno-N-Coder-25B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use locailabs/Juno-N-Coder-25B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf locailabs/Juno-N-Coder-25B-A3B-GGUF: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 locailabs/Juno-N-Coder-25B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Juno-N-Coder-25B

NB: This is a GGUF of Juno-N-Coder-25B

Juno-N-Coder-25B is a coding-specialised derivative of NVIDIA Nemotron 3.5 Lightning 30B, produced by structured expert pruning with our SPACE algorithm (Specialisation Pruning Algorithm for Compression of Experts). This is the first model in the Juno series developed to run on-prem on the Locai One.

Locai Labs was one of NVIDIA's early-access partners for Nemotron 3.5 Lightning, and we want to thank the NVIDIA team for giving us early access to the model and supporting our work.

The goal with Juno-N-Coder was to create a coding-specialised version of Nemotron 3.5 Lightning purely through pruning and without any recovery fine-tuning. Using SPACE, we evaluated each expert's contribution to the target capability, in this case software development, removing the bottom 25% and renormalising the router so that the gate distribution remains correctly scaled.

The results are summarised in the model's performance profile below. Juno-N retains performance across four software engineering benchmarks, while degrading in general knowledge and factual recall as measured by MMLU Redux and SimpleQA.

Benchmark	Juno-N-Coder-25B	Nemotron 3.5 Lightning 30B
LiveCodeBench v6 (`test_v6_2408_2505`)	77.09	77.97
HumanEval+	90.55	89.33
MBPP+	80.29	81.49
SciCode (subtask)	36.98	36.09
MMLU Redux	82.18	90.00
SimpleQA (rubric)	37.88	47.95

MMLU Redux subject group	Juno-N-Coder-25B	Base	Δ
Maths & CS	94.6	94.9	-0.3
Physical sciences	88.3	94.4	-6.1
Economics	78.7	87.4	-8.7
Medicine	72.7	84.4	-11.7
World facts	71.7	84.0	-12.3
Humanities	75.9	88.5	-12.6

All models were evaluated using NVIDIA NeMo Evaluator at identical settings for both models: temperature 1.0, top_p 0.95, a 65,536-token generation limit, BF16 weights, and reasoning traces separated from the response before scoring.

Usage

vllm serve locailabs/Juno-N-Coder-25B \
    --trust-remote-code \
    --max-model-len 131072 \
    --reasoning-parser ultra_v3 \
    --reasoning-parser-plugin "$PARSER" \
    --tool-call-parser qwen3_coder \
    --enable-auto-tool-choice \
    --enable-prefix-caching

Citation

@misc{juno-n-coder-25b,
  title  = {Juno-N-Coder-25B: Specialisation Pruning for Compression of Experts},
  author = {Locai Labs},
  year   = {2026},
  url    = {https://huggingface.co/locailabs/Juno-N-Coder-25B}
}

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GGUF

Model size

25B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

8-bit

16-bit

Collection including locailabs/Juno-N-Coder-25B-A3B-GGUF

Juno-N

Collection

Capability targeted compression of Nemotron 3.5 Lightning using our SPACE compression algorithm • 4 items • Updated 2 days ago