Text Generation
Transformers
Safetensors
qwen2
coder
code
agent
conversational
text-generation-inference
Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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eca5751 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | 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",
],
)
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