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QuantaSparkLabs
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Model Details
Model Provider
QuantaSparkLabs
Model Tree
QuantaSparkLabs/NYXIS-1.1B
Input Modalities
Output Modalities
Supported Functionality
| 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 |
python
from huggingface_hub import snapshot_downloadimport sys model_dir = snapshot_download("QuantaSparkLabs/NYXIS-Pro")sys.path.insert(0, model_dir) from pipeline import NYXISPronyxis = 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
| 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 |
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 |
When you ask a factual question, NYXIS-PRO follows a strict 4‑tier protocol:
It never fabricates an answer.
Apache‑2.0
Built with 🛡️ by QuantaSparkLabs
from huggingface_hub import snapshot_downloadimport sys model_dir = snapshot_download("QuantaSparkLabs/NYXIS-Pro")sys.path.insert(0, model_dir) from pipeline import NYXISPronyxis = NYXISPro(model_dir) result = nyxis.generate("What is a binary search tree?")print(result["response"])