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
Download requirements.txt from AdminReal/NexusCoder: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/AdminReal/NexusCoder/resolve/main/requirements.txt
- Command line
-
hf download hf://AdminReal/NexusCoder/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/AdminReal/NexusCoder/resolve/main/requirements.txt
1.38 kB
| # Requirements for Nexus Coder v0.3 | |
| # Python 3.12.13 (strict) | |
| # Author: Hieu Louis (2026) | |
| # === Core deep learning === | |
| torch>=2.0.0 | |
| numpy>=1.24.0 | |
| # === Utilities === | |
| tqdm>=4.65.0 | |
| pyyaml>=6.0 | |
| # === Data pipeline (v0.2 + v0.3 NEW) === | |
| datasets>=2.14.0 | |
| datasketch>=1.6.0 | |
| langdetect>=1.0.9 | |
| # === Code tools (v0.2) === | |
| ruff>=0.1.0 | |
| black>=23.0.0 | |
| isort>=5.12.0 | |
| flake8>=6.0.0 | |
| sqlparse>=0.4.4 | |
| jsbeautifier>=1.14.0 | |
| # === Optimization (v0.2) === | |
| bitsandbytes>=0.41.0; platform_system == "Linux" | |
| # === Web tools (v0.2 + v0.3 NEW) === | |
| requests>=2.31.0 | |
| aiohttp>=3.9.0 | |
| websockets>=12.0 | |
| grpcio>=1.59.0 | |
| beautifulsoup4>=4.12.0 | |
| lxml>=4.9.0 | |
| # === Crypto / Security tools (v0.2 + v0.3 NEW) === | |
| cryptography>=41.0.0 | |
| pyjwt>=2.8.0 | |
| # === Database tools (v0.3 NEW) === | |
| 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 | |
| # === DevOps tools (v0.3 NEW) === | |
| paramiko>=3.4.0 | |
| kubernetes>=28.1.0 | |
| docker>=7.0.0 | |
| # === Media / Convert tools (v0.3 NEW) === | |
| Pillow>=10.0.0 | |
| reportlab>=4.0.0 | |
| markdown>=3.5.0 | |
| # === ML tools (v0.3 NEW) === | |
| scikit-learn>=1.3.0 | |
| scipy>=1.11.0 | |
| transformers>=4.35.0 | |
| accelerate>=0.24.0 | |
| peft>=0.6.0 | |
| # === Optional advanced features === | |
| # For distributed training | |
| # deepspeed>=0.12.0 | |
| # For faster attention (GPU only) | |
| # flash-attn>=2.0.0 | |
| # triton>=2.0.0 | |
| # For evaluation benchmarks | |
| # lm-eval>=0.3.0 | |