Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdminReal/NexusCoder with Transformers:

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

pipe = pipeline("text-generation", model="AdminReal/NexusCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdminReal/NexusCoder")
model = AutoModelForCausalLM.from_pretrained("AdminReal/NexusCoder", 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 AdminReal/NexusCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdminReal/NexusCoder

SGLang

How to use AdminReal/NexusCoder 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 "AdminReal/NexusCoder" \
    --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": "AdminReal/NexusCoder",
		"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 "AdminReal/NexusCoder" \
        --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": "AdminReal/NexusCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdminReal/NexusCoder with Docker Model Runner:
```
docker model run hf.co/AdminReal/NexusCoder
```

🧠 Nexus Coder

AI Code & Security Engine — Qwen2.5-Coder based

A deployable coding model: Qwen2.5-Coder-1.5B weights + Nexus Coder agent engine (skills & tools)

📖 Introduction

Nexus Coder is a working AI model combining:

Model weights: Qwen2.5-Coder-1.5B-Instruct (Apache 2.0) — a strong 1.5B coding model.
Agent engine: the Nexus Coder framework (nexus/) — 60+ skills and 80+ tools with automatic registration, agent planner/router/memory/safety, data pipeline, and training utilities.

🚀 Quick Usage

The model is a standard Qwen2ForCausalLM. Load it with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AdminReal/NexusCoder"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [{"role": "system", "content": "You are Nexus Coder, a helpful coding assistant."},
            {"role": "user", "content": "Write a Python function to compute fibonacci."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

CLI chat demo (from source):

python scripts/chat.py --model AdminReal/NexusCoder

📁 Repository Contents

Part	What it is	License
`model.safetensors`, `config.json`, `tokenizer.*`	Qwen2.5-Coder-1.5B-Instruct weights	Apache 2.0
`nexus/`	Agent engine source (skills, tools, agent, data, optim)	NAL-1.0
`configs/`	Experimental architecture designs (`tiny` → `423b`) — not the hosted model	NAL-1.0
`docs/`, `scripts/`, `tests/`	Documentation, CLI scripts, tests	NAL-1.0

The configs/nexus_coder_*.yaml files describe a from-scratch MoE research architecture and are independent from the Qwen2-based weights hosted in this repo.

⚖️ Licenses

Model weights: Apache 2.0 (from Qwen/Qwen2.5-Coder-1.5B-Instruct). See LICENSE.
Source code (nexus engine): NexusCoder Attribution License v1.0 (NAL-1.0). Attribution required to Hieu Louis (github.com/mhieuhonda).