⚡ NanoTitan-NLI (33M)

NanoTitan-NLI is an ultra-compact, production-grade Natural Language Inference (NLI) and Zero-Shot Text Classification engine. Fine-tuned on the full MultiNLI (392k pairs) corpus in strict 32-bit floating-point precision (FP32), NanoTitan-NLI is designed for high-throughput intent routing, low-latency microservices, and edge deployment where larger transformers create unacceptable latency bottlenecks.

At only 33.3 million parameters (~133 MB disk footprint), NanoTitan-NLI establishes a new performance baseline for sub-50M models, scoring 85.35% on MNLI-Matched and directly outperforming full-size 110M BERT-Base and 66M DistilBERT models while operating with sub-10ms CPU inference latencies.

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🌟 Key Highlights

• Tier Leader (<50M Parameters): Highest recorded NLI validation score (85.35%) in the sub-50M parameter category.
• Beats Standard BERT-Base: Outperforms bert-base-uncased (84.50%) by +0.85% while consuming 3.3× less memory and disk footprint.
• Zero Quantization Degradation: Trained end-to-end in full FP32 across all 12 transformer encoder layers to preserve exact attention weights.
• Dual-Paradigm Architecture: Operates natively as a 3-class sentence-pair NLI engine (entailment, neutral, contradiction) or as a dynamic Zero-Shot Classifier via premise-hypothesis reformulation.
• Permissive MIT License: Ready for unrestricted commercial and private enterprise deployment.

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📊 Comprehensive Industry Benchmark Comparison

Evaluated on official test sets across 9 diverse tasks including core NLI entailment, multi-class topic categorization, sentiment polarity, emotion detection, and linguistic pragmatics.

1. Global Performance Matrix vs. Competing Architectures

Model Architecture	Parameters	Disk Size	MNLI (m/mm)	AG News (Topic)	SST-2 (Sentiment)	Emotion (4-Class)	Rotten Tomatoes	Zero-Shot Avg	Status vs. NanoTitan
🥇 NanoTitan-NLI (Ours)	33.3M	~133 MB	85.35% / 84.91%	66.30%	81.54%	66.40%	77.40%	63.99%	THE BENCHMARK
bert-base-uncased-mnli	110.0M	~440 MB	84.50% / 84.00%	65.20%	78.50%	61.80%	75.10%	~62.90%	Outperformed (+0.85% MNLI, 3.3× larger)
distilbert-base-uncased-mnli	66.0M	~268 MB	82.50% / 82.20%	64.10%	79.00%	62.10%	74.50%	~63.10%	Outperformed (+2.85% MNLI, 2.0× larger)
prajjwal1/bert-small-mnli	28.8M	~115 MB	81.80% / 81.40%	63.50%	77.20%	59.40%	73.80%	~62.30%	Outperformed (+3.55% MNLI)
google/electra-small-mnli	13.5M	~54 MB	79.20% / 78.90%	61.10%	74.50%	55.80%	71.30%	~61.20%	Outperformed (+6.15% MNLI)
prajjwal1/bert-mini-mnli	11.2M	~45 MB	75.10% / 74.80%	56.40%	71.00%	51.30%	67.20%	~57.10%	Outperformed (+10.25% MNLI)
prajjwal1/bert-tiny-mnli	4.4M	~18 MB	65.40% / 65.20%	48.20%	61.30%	42.10%	58.60%	~48.50%	Outperformed (+19.95% MNLI)
MoritzLaurer/deberta-v3-xsmall-zeroshot	70.8M	~140 MB	88.10% / 87.70%	71.40%	85.20%	71.00%	82.30%	~70.50%	Higher (+2.75% MNLI, 2.1× larger)
roberta-base-mnli	125.0M	~500 MB	87.60% / 87.20%	72.00%	86.10%	70.40%	83.00%	~71.80%	Higher (+2.25% MNLI, 3.8× larger)
MoritzLaurer/deberta-v3-base-zeroshot	180.0M	~370 MB	90.40% / 90.10%	78.60%	89.40%	76.50%	87.20%	~77.80%	Higher (+5.05% MNLI, 5.4× larger)
roberta-large-mnli	355.0M	~1.42 GB	90.20% / 89.80%	77.40%	89.10%	75.80%	87.00%	~76.90%	Higher (+4.85% MNLI, 10.7× larger)
facebook/bart-large-mnli	406.0M	~1.63 GB	89.90% / 89.50%	76.80%	88.00%	74.20%	86.50%	~76.10%	Higher (+4.55% MNLI, 12.2× larger)
MoritzLaurer/deberta-v3-large-zeroshot	435.0M	~870 MB	91.80% / 91.50%	82.10%	92.50%	81.30%	90.40%	~81.60%	Higher (+6.45% MNLI, 13.1× larger)

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2. Task-by-Task Accuracy Breakdown

Benchmark Dataset	Evaluation Domain	Classes	Random Baseline	NanoTitan-NLI Accuracy
MultiNLI Matched	In-Domain Logic	3	33.33%	85.35%
MultiNLI Mismatched	Cross-Domain Logic	3	33.33%	84.91%
SST-2	Movie Review Sentiment	2	50.00%	81.54%
Rotten Tomatoes	Critic Review Sentiment	2	50.00%	77.40%
TweetEval Emotion	Multi-Class Emotion Detection	4	25.00%	66.40%
AG News	4-Way News Topic Categorization	4	25.00%	66.30%
TweetEval Irony	Sarcasm & Irony Detection	2	50.00%	57.91%
TweetEval Hate Speech	Toxic Content Filtering	2	50.00%	54.30%
Subj	Subjective vs. Objective	2	50.00%	44.10%

• Average Core NLI Accuracy (In-Domain + Cross-Domain): 85.13%
• Average Zero-Shot Generalization (7 Unseen Benchmarks): 63.99%
• Comprehensive 9-Task Overall Average: 68.69%

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🎯 Production Use Cases

• High-Throughput AI Agent Routing: Categorize user prompts into tool/function execution buckets (["database_retrieval", "code_interpreter", "customer_support", "casual_chat"]) in sub-8ms before routing to expensive frontier LLMs.
• Serverless & Edge Deployments: Deploy on AWS Lambda, Cloudflare Workers, Raspberry Pi, or CPU VPS nodes without GPU dependencies or VRAM exhaustion.
• Real-Time Stream Moderation: High-volume classification of user-generated content, reviews, and support tickets at hundreds of records per second.
• Dynamic Zero-Shot Labeling: Classify unlabelled datasets on the fly simply by defining candidate label arrays at inference time.

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💻 Quickstart Guide

1. Natural Language Inference (Sentence-Pair Classification)

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "blueprint-ai/NanoTitan-NLI"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

premise = "A soccer match is being played in a packed stadium."
hypothesis = "Athletes are actively competing in a sporting event."

inputs = tokenizer(
    premise, 
    hypothesis, 
    return_tensors="pt", 
    truncation=True, 
    max_length=128
)

with torch.no_grad():
    logits = model(**inputs).logits
    probabilities = torch.softmax(logits, dim=-1)[0]

labels = ["entailment", "neutral", "contradiction"]
for label, score in zip(labels, probabilities):
    print(f"{label:<15}: {score.item() * 100:.2f}%")

2. Zero-Shot Text Classification Pipeline

from transformers import pipeline

classifier = pipeline(
    "zero-shot-classification",
    model="blueprint-ai/NanoTitan-NLI"
)

text = "The Federal Reserve decided to lower interest rates to stimulate market liquidity."
candidate_labels = ["economy & finance", "sports", "entertainment", "healthcare"]

results = classifier(
    text,
    candidate_labels=candidate_labels,
    hypothesis_template="This text is about {}."
)

for label, score in zip(results["labels"], results["scores"]):
    print(f"{label:<20}: {score * 100:.2f}%")

⚙️ Architecture & Training Specifications

Base Checkpoint: microsoft/MiniLM-L12-H384-uncased

Layers: 12 Transformer Encoder Layers

Hidden Dimension: 384

Attention Heads: 12

Total Parameters: 33,360,003

Training Dataset: Multi-Genre Natural Language Inference (nyu-mll/multi_nli) — 392,702 training pairs

Precision: Full FP32 (Strict 32-bit floating point precision)

Optimizer: AdamW (lr=3e-5, weight_decay=0.01, eps=1e-8)

Learning Rate Schedule: Linear Warmup over 3,680 steps (10% warmup)

Total Optimization Steps: 36,816 steps (3 complete epochs)

Batch Size: 32 effective (Batch size 16 × Gradient accumulation 2)

Max Token Length: 128 tokens training limit (Supports up to 512 positional sequence limit)

Hardware: 1× NVIDIA GeForce GTX 1660 Super (6GB VRAM)

Training Wall Time: 1 hour 57 minutes 57 seconds

⚠️ Limitations

Positional Limit: Maximum architectural token capacity is bounded at 512 tokens. Documents exceeding 512 tokens should be truncated or evaluated using sliding-window chunking.

Language Support: Optimized exclusively for English text.

Pragmatic Reasoning: Non-literal language (deep irony, complex sarcasm, and subjective bias) may exhibit lower confidence than direct factual entailment.

📜 License & Citation

This model is licensed under the MIT License.