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 / nexus /data /__init__.py

AdminReal

Import NexusCoder from github.com/mhieuhonda/NexusCoder

eca5751 verified about 1 month ago

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	"""
	Nexus Data Module - v0.2 NEW
	============================
	Pipeline thu thập và xử lý training data.

	Sources:
	- GitHubCollector: Code từ public GitHub repos
	- HuggingFaceCollector: Datasets từ HuggingFace Hub
	- ArxivCollector: Scientific papers
	- WikipediaCollector: General knowledge
	- StackOverflowCollector: Q&A pairs

	Processors:
	- TextCleaner: Làm sạch text
	- CodeFormatter: Format code samples
	- Deduplicator: Loại bỏ duplicates (MinHash)
	- QualityFilter: Lọc low-quality samples
	"""

	from .collectors.github_collector import GitHubCollector
	from .collectors.huggingface_collector import HuggingFaceCollector
	from .collectors.arxiv_collector import ArxivCollector
	from .collectors.wikipedia_collector import WikipediaCollector
	from .collectors.stackoverflow_collector import StackOverflowCollector
	from .processors.cleaner import TextCleaner
	from .processors.deduplicator import Deduplicator
	from .processors.quality_filter import QualityFilter
	from .processors.code_formatter import CodeFormatter
	from .curriculum import CurriculumLearning

	__all__ = [
	"GitHubCollector",
	"HuggingFaceCollector",
	"ArxivCollector",
	"WikipediaCollector",
	"StackOverflowCollector",
	"TextCleaner",
	"Deduplicator",
	"QualityFilter",
	"CodeFormatter",
	"CurriculumLearning",
	]