Instructions to use QuantaSparkLabs/NYXIS-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantaSparkLabs/NYXIS-Pro with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantaSparkLabs/NYXIS-Pro")
model = AutoModelForCausalLM.from_pretrained("QuantaSparkLabs/NYXIS-Pro", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantaSparkLabs/NYXIS-Pro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantaSparkLabs/NYXIS-Pro

SGLang

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

Docker Model Runner
How to use QuantaSparkLabs/NYXIS-Pro with Docker Model Runner:
```
docker model run hf.co/QuantaSparkLabs/NYXIS-Pro
```

NYXIS Logo

NYXIS Name

# NYXIS-PRO 🛡️

The all-in-one reasoning engine with the AEGIS knowledge shield.

NYXIS-PRO combines the NYXIS1.1B base model with a curated 5,000+ chunk knowledge base covering coding, math, science, history, and common knowledge. A dual‑stage retriever grounds every factual answer in verified data — zero hallucination.

🧠 What Makes It Different

Feature	Description
AEGIS Knowledge Base	5,426 curated Wikipedia, Trivia QA, and ArXiv chunks
Dual‑Stage Retriever	Dense recall (bge‑small) + Cross‑encoder rerank (ms‑marco‑MiniLM)
Emotion Engine	Detects user emotion on a 1‑10 scale and matches tone
4‑Tier Knowledge Cascade	AEGIS → External Search → Model Brain → Honest Fallback
Zero Hallucination	If it doesn't know, it says so — no fabrication

🚀 Quick Start

from huggingface_hub import snapshot_download
import sys

model_dir = snapshot_download("QuantaSparkLabs/NYXIS-Pro")
sys.path.insert(0, model_dir)

from pipeline import NYXISPro
nyxis = NYXISPro(model_dir)

result = nyxis.generate("What is a binary search tree?")
print(result["response"])

Requirements: sentence-transformers, faiss-cpu, transformers, accelerate, bitsandbytes
VRAM: ~2.1 GB (4‑bit) | Hardware: T4 or better

📦 What's Inside

Component	Model	Purpose
Base LLM	NYXIS1.1B)	Text generation
Dense Retriever	bge‑small‑en‑v1.5	Initial candidate recall
Cross‑Encoder	ms‑marco‑MiniLM‑L‑6‑v2	Precision re‑ranking
FAISS Index	5,426 chunks	Knowledge storage
Emotion Engine	Custom keyword‑based	Tone matching

🤖 Emotion Intelligence

NYXIS-PRO detects your emotional state from text and adjusts its personality:

Level	Emotion	Response Style
1‑3	Sad / Angry	Gentle, supportive, empathetic
4‑6	Neutral	Balanced, warm, helpful
7‑9	Happy / Excited	Energetic, playful
10	Overjoyed	Celebratory

🛡️ Knowledge Cascade

When you ask a factual question, NYXIS-PRO follows a strict 4‑tier protocol:

AEGIS — Searches internal verified knowledge base
External Search — Falls back to web search (user‑provided API)
Model Brain — Uses training data with honesty guard
Honest Fallback — Admits "I don't have enough information"

It never fabricates an answer.

Limitations

Knowledge base covers ~5,400 chunks — not infinite
Emotion detection is keyword‑based, not deep sentiment analysis
External search requires user‑provided API function
English‑only, 1.5B model size limits complex reasoning

License

Apache‑2.0

Built with 🛡️ by QuantaSparkLabs

Downloads last month: 27

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for QuantaSparkLabs/NYXIS-Pro

Base model

QuantaSparkLabs/NYXIS-1.1B

Finetuned

(1)

this model