Antiplex-Instruct-3B

A compact, instruction-tuned large language model optimized for Text Generation, Intent Classification, and SQL Reasoning.

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📋 Overview

Antiplex-Instruct-3B is a high-performance instruction-tuned language model developed by QuantaSparkLabs. Released in 2026, this model is engineered for dual-task capability, delivering accurate identity alignment, reliable SQL generation, and strong general reasoning, while remaining lightweight and efficient.

The model is fine-tuned using LoRA (PEFT) on curated datasets emphasizing identity consistency and structured reasoning, making it ideal for edge deployment and specialized assistant roles.

✨ Core Features

🎯 Task Versatility	⚡ Performance Optimized
Text Generation: SQL/NLP, creative writing, technical explanations.	LoRA Fine-tuning: Efficient parameter adaptation.
Classification: Intent detection, task routing, safety filtering.	Identity Alignment: Consistent persona across interactions.
Dual-Mode: Single model handling generation + classification.	Lightweight: ~3.8B parameters, edge-friendly VRAM footprint.

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📊 Performance Benchmarks

🏆 Accuracy Metrics

Task	Accuracy	Confidence
Identity Verification	100%	⭐⭐⭐⭐⭐
SQL Generation	100%	⭐⭐⭐⭐⭐
General Reasoning	90%	⭐⭐⭐⭐

🔬 Reliability Assessment

21-Test Internal Validation Suite

• Passed: 16 tests (76.2%)
• Failed: 5 tests (23.8%)
• Overall Grade: B (Good)

  

📈 View Detailed Test Categories

Category	Tests	Passed	Rate
Identity Tasks	7	7	100%
SQL Generation	6	6	100%
Reasoning	5	3	60%
Classification	3	2	66.7%

Test Dataset: QuantaSparkLabs/antiplex-test-suite

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🏗️ Model Architecture

Training Pipeline

graph TD
    A[Base Model Phi-3-mini] --> B[LoRA Fine-tuning]
    B --> C[Task-Specific Heads]
    C --> D[Text Generation Head]
    C --> E[Classification Head]
    D --> F[Generation Output]
    E --> G[Classification Output]
    H[Instruction Dataset] --> B
    I[SQL Dataset] --> B
    J[Identity Dataset] --> B

Inference Flow

User Prompt → Tokenization → Antiplex Core → Task Router 
                ↓
       [Generation/Classification] → Post-processing → Output

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🔧 Technical Specifications

Parameter	Value
Base Model	unsloth/Phi-3-mini-4k-instruct-bnb-4bit
Fine-tuning	LoRA (PEFT)
Rank (r)	16
Alpha (α)	32
Optimizer	AdamW (β₁=0.9, β₂=0.999)
Learning Rate	2e-4
Batch Size	8
Epochs	3
Total Parameters	~3.8B

Dataset Composition

Dataset Type	Samples	Purpose
Identity Alignment	30	Consistent persona training
SQL Generation	300	Structured query training
Instruction Tuning	2,500	General capability enhancement
Classification	1,000	Intent detection training

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💻 Quick Start

Installation

pip install transformers torch accelerate

Basic Usage (Text Generation)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "QuantaSparkLabs/Antiplex-instruct-3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Write an SQL query to fetch users created in the last 30 days."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Classification Mode

# Intent classification example
classification_prompt = """[CLASSIFY]
User Query: "I need to reset my account password"
Categories: account_issue, technical_support, billing, general_inquiry
"""

inputs = tokenizer(classification_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=64,
    temperature=0.3,
    do_sample=False
)

detected_intent = tokenizer.decode(outputs[0], skip_special_tokens=True).split('[')[-1].split(']')[0]
print(f"Detected Intent: {detected_intent}")

Chat Interface

from transformers import pipeline

chatbot = pipeline(
    "text-generation",
    model=model_id,
    tokenizer=tokenizer,
    device=0 if torch.cuda.is_available() else -1
)

messages = [
    {"role": "system", "content": "You are Antiplex, a helpful AI assistant specialized in SQL and classification tasks."},
    {"role": "user", "content": "Classify this intent: 'Can you help me with invoice generation?' Then write a SQL query to find recent invoices."}
]

response = chatbot(messages, max_new_tokens=512, temperature=0.7)
print(response[0]['generated_text'][-1]['content'])

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🚀 Deployment Options

Hardware Requirements

Environment	VRAM	Quantization	Speed
GPU (Optimal)	8-12 GB	FP16	⚡ Fast
GPU (Efficient)	4-6 GB	INT8	⚡ Fast
CPU	N/A	FP32	🐌 Slow
Edge Device	2-4 GB	INT4	⚡ Fast

Cloud Deployment (Docker)

FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["python", "app.py"]

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📁 Repository Structure

Antiplex-Instruct-3B/
├── README.md
├── model.safetensors
├── config.json
├── tokenizer.json
├── tokenizer_config.json
├── generation_config.json
├── special_tokens_map.json
├── quantasparklogo.png
├── examples/
│   ├── classification_demo.py
│   ├── sql_generation_demo.py
│   └── chat_interface.py
└── evaluation/
    └── test_results.json

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⚠️ Limitations & Safety

Known Limitations

• Domain Specificity: Not trained for medical/legal/safety-critical domains
• Bias Inheritance: May reflect biases in training data
• Context Window: Limited to 4K tokens
• Multilingual: Primarily English-focused

Safety Guidelines

# Recommended safety wrapper
def safety_check(text):
    blocked_terms = ["harmful", "dangerous", "illegal", "exploit"]
    if any(term in text.lower() for term in blocked_terms):
        return "Content filtered for safety reasons."
    return text

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🔄 Version History

Version	Date	Changes
v1.0.0	2026-01-1	Initial release
v1.1.0	2026-01-10	Enhanced classification head
v1.2.0	2026-01-25	SQL generation improvements

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📄 License & Citation

License: Apache 2.0

Citation:

@misc{antiplex2026,
  title={Antiplex-Instruct-3B: A Dual-Task Instruction-Tuned Language Model},
  author={QuantaSparkLabs},
  year={2026},
  url={https://huggingface.co/QuantaSparkLabs/Antiplex-instruct-3B}
}

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👥 Credits & Acknowledgments

• Base Model: Microsoft Phi-3 Mini team
• Fine-tuning Framework: Unsloth for efficient LoRA training
• Evaluation: Internal QuantaSparkLabs team
• Testing: Community contributors

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🤝 Contributing & Support

Reporting Issues

Please open an issue on our repository with:

1. Model version
2. Reproduction steps
3. Expected vs actual behavior

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Built with ❤️ by QuantaSparkLabs
_{Model ID: Antiplex-Instruct-3B • Parameters: ~3.8B • Release: 2026}

Special thanks to microsoft!