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
```

NexusCoder / setup.py

AdminReal

Import NexusCoder from github.com/mhieuhonda/NexusCoder

eca5751 verified about 1 month ago

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	from setuptools import setup, find_packages

	setup(
	name="nexus-coder",
	version="0.4.0",
	description="Nexus Coder v0.4 - CyberForge edition. MoE 423B/39B + 3M context + CyberGym training.",
	long_description=open("README.md", "r", encoding="utf-8").read() if __import__("os").path.exists("README.md") else "",
	long_description_content_type="text/markdown",
	author="Hieu Louis",
	author_email="mhieuhonda@users.noreply.github.com",
	url="https://github.com/mhieuhonda/NexusCoder",
	license="NAL-1.0 (Attribution Required)",
	packages=find_packages(),
	python_requires="==3.12.13",
	install_requires=[
	"torch>=2.0.0",
	"numpy>=1.24.0",
	"tqdm>=4.65.0",
	"pyyaml>=6.0",
	"datasets>=2.14.0",
	"requests>=2.31.0",
	"cryptography>=41.0.0",
	],
	extras_require={
	"gpu": ["flash-attn>=2.0.0", "bitsandbytes>=0.41.0", "triton>=2.0.0"],
	"data": ["datasets>=2.14.0", "datasketch>=1.6.0", "langdetect>=1.0.9"],
	"tools": ["ruff>=0.1.0", "black>=23.0.0", "isort>=5.12.0",
	"sqlparse>=0.4.4", "jsbeautifier>=1.14.0"],
	"crypto": ["cryptography>=41.0.0", "pyjwt>=2.8.0"],
	"database": [
	"sqlalchemy>=2.0.0", "psycopg2-binary>=2.9.0", "pymysql>=1.1.0",
	"redis>=5.0.0", "pymongo>=4.5.0", "elasticsearch>=8.0.0",
	"kafka-python>=2.0.2", "pika>=1.3.0",
	],
	"web": ["aiohttp>=3.9.0", "websockets>=12.0", "grpcio>=1.59.0",
	"beautifulsoup4>=4.12.0", "lxml>=4.9.0"],
	"devops": ["paramiko>=3.4.0", "kubernetes>=28.1.0", "docker>=7.0.0"],
	"media": ["Pillow>=10.0.0", "reportlab>=4.0.0", "markdown>=3.5.0"],
	"ml": ["scikit-learn>=1.3.0", "scipy>=1.11.0", "transformers>=4.35.0",
	"accelerate>=0.24.0", "peft>=0.6.0"],
	"distributed": ["deepspeed>=0.12.0", "accelerate>=0.24.0", "flash-attn>=2.0.0"],
	},
	classifiers=[
	"Development Status :: 4 - Beta",
	"License :: Other/Proprietary License",
	"Programming Language :: Python :: 3.12",
	"Programming Language :: Python :: 3.12.13",
	"Topic :: Scientific/Engineering :: Artificial Intelligence",
	],
	)