Conversational AI & Hybrid Intelligence

Hierarchical Hybrid Intelligence: Neural Flagship (1.00B / 999.38M) + In-House NLP Engine + Knowledge Grounding

🤗 Download Model (1B & 10M) ↗ Engine Online
🚀 Run COLLISION-1B Locally with transformers or Ollama 100% CPU-READY
Sub-5ms latency • Dual-Process System 1/System 2 Dialectic Reasoning • 11-in-1 Industrial NLP Suite
Colab 🤗 View Model Card
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COLLISION Hybrid Assistant Flagship 1B NLP

Hello! I am Collision-1B (999.38M parameters, 24 transformer layers), powered by an industrial-strength in-house NLP Engine and tri-modal grounded neural intelligence. You can ask me questions, test reasoning, analyze text, or try any of the NLP tools in the sidebar.

Try asking:

🔍 Live Inference Inspector

Predicted Intent & Route
General Query
Salient Keyphrases
collision-10m nlp
Tone & Formality
Formality 78%
Latency Metric
~1.2 ms

🏷️ TextRank Keyphrase & Keyword Extractor

Graph-based ranking algorithm that extracts both unigram keywords and multi-word keyphrases using co-occurrence graphs with damping.

🔑 Top Multi-Word Keyphrases

📊 Individual Ranked Tokens

📊 Multi-Domain Topic Classification

Classifies text across 10 specialized knowledge domains: Artificial Intelligence, Technology, Science, Mathematics, Finance, Medicine, Politics, Law, Arts & Literature, and Philosophy.

🏆 Top Predicted Domain

FINANCE
Confidence: 94.2%

📈 Full Distribution (10 Domains)

🎭 Tone, Formality & Subjectivity Analysis

Analyzes register formality (0–100%), emotional valence / sentiment polarity, and objectivity vs subjectivity.

Formality Score
92%
Highly Formal / Academic
Sentiment Polarity
+0.65
Positive
Subjectivity
0.18
Objective / Factual
Register Class
Academic
Lexical Sophistication

✍️ Grammar & Spell Proofreader

Identifies spelling mistakes, phonetic typos, duplicate words, capitalization issues, and suggests normalized corrections.

🔍 Detected Errors (0)

✅ Corrected Output

📈 Readability & Text Complexity Metrics

Computes classic linguistic readability standards including Flesch Reading Ease, Flesch-Kincaid Grade Level, and Gunning Fog Index.

Flesch Reading Ease
28.4
Difficult / College Level
Flesch-Kincaid Grade
15.2
University / Postgraduate
Gunning Fog Index
17.8
Complex Technical Prose
Avg Sentence Length
24.0
Words per sentence

🔍 Extractive Reading Comprehension (Context QA)

SQuAD-style question answering that extracts exact answer spans from a given reference passage based on query semantics and token alignment.

🎯 Extracted Answer Span:
999.38 million parameters (999,376,128 exact) across 24 attention layers
Confidence Score: 0.98 • Sentence Rank #1

🔢 Deterministic Problem Solver

Zero-hallucination exact computational solvers for arithmetic, geometry, statistical analysis, and physical unit conversions.

Problem Type
Geometry: Circle Area
Exact Formula
A = π × r² = 3.14159 × (12.5)²
Computed Result
490.8739

💻 Python SDK & API Quickstart

Use the COLLISION NLP Engine in Python directly from the repository or Hugging Face Hub.

1. Complete NLP Engine Quickstart 
from collision.nlp import CollisionNLPEngine

# Initialize the NLP suite
engine = CollisionNLPEngine()

# 1. TextRank Keyphrase Extraction
res = engine.extract_keyphrases("Transformers and natural language processing revolutionize modern AI.")
print("Keyphrases:", res.keyphrases)

# 2. Topic Classification
topic = engine.classify_topic("The Federal Reserve adjusted monetary interest rates.")
print("Topic:", topic.primary_topic, topic.confidence)

# 3. Tone & Formality Analysis
tone = engine.analyze_tone("Consequently, empirical evidence supports the hypothesis.")
print("Formality:", tone.formality_score, "Sentiment:", tone.sentiment_polarity)

# 4. Proofreading & Spelling Correction
corrected = engine.proofread("The artifical inteligence model gave an amazeing answer.")
print("Corrected:", corrected.corrected_text)

# 5. Extractive Reading Comprehension
qa = engine.answer_question(
    context="Collision-1B has 999.38 million parameters across 24 layers.",
    question="How many parameters does the model have?"
)
print("Answer:", qa.answer)
2. Load from Hugging Face Model Hub 
from huggingface_hub import hf_hub_download
import torch

# Download model checkpoint and tokenizer from Hugging Face
model_path = hf_hub_download(repo_id="collision-10M/Collision-1B", filename="model.pt")
print(f"Collision-1B model loaded from {model_path}")