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

Libraries

How to use locailabs/Juno-N-Coder-25B-A3B 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", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("locailabs/Juno-N-Coder-25B-A3B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("locailabs/Juno-N-Coder-25B-A3B", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use locailabs/Juno-N-Coder-25B-A3B 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use locailabs/Juno-N-Coder-25B-A3B 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

GeorgeDraysonLocai commited on 2 days ago

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+---
+license: other
+license_name: nvidia-open-model-license
+license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+base_model: nvidia/NVIDIA-Nemotron-3.5-Lightning-30B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - code
+  - moe
+  - pruning
+  - space
+---
+![Juno](juno.png)
+# Juno-N-Coder-25B
+**Juno-N-Coder-25B** is a coding-specialised derivative of
+[NVIDIA Nemotron 3.5 Lightning 30B](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B), 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](https://locailabs.com/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.
+![Spider diagram](spider.png)
+| 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
+```bash
+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
+```bibtex
+@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}
+}
+```