---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- llm
- instruction-tuned
- text-generation
- text-classification
- sql-generation
- reasoning
- lora
- lightweight
- safetensors
- causal-lm
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
fine_tuned_from: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
organization: QuantaSparkLabs
model_type: causal-lm
model_index:
- name: Antiplex-Instruct-3B
  results:
  - task:
      type: text-generation
      name: SQL Generation
    metrics:
    - type: accuracy
      value: 100
  - task:
      type: text-classification
      name: Intent Detection
    metrics:
    - type: accuracy
      value: 66.7
---

<p align="center">
<img src="quanta.png" width="900" alt="QuantaSparkLabs Logo"/>
</p>

<h1 align="center">Antiplex-Instruct-3B</h1>

<p align="center">
A compact, instruction-tuned large language model optimized for <strong>Text Generation</strong>, <strong>Intent Classification</strong>, and <strong>SQL Reasoning</strong>.
</p>

<p align="center">
<img src="https://img.shields.io/badge/Identity_Alignment-100%25-brightgreen" alt="Identity Alignment">
<img src="https://img.shields.io/badge/SQL_Generation-100%25-brightgreen" alt="SQL Generation">
<img src="https://img.shields.io/badge/General_Reasoning-90%25-yellow" alt="General Reasoning">
<img src="https://img.shields.io/badge/Release-2026-blue" alt="Release Year">
</p>

---

## 📋 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. |
<p align="center">
<img src="statics.png" width="900" alt="statics"/>
</p>
---

## 📊 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)
<p align="center">
<img src="overview.png" width="900" alt="overview"/>
</p>

<details>
<summary>📈 View Detailed Test Categories</summary>

| 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`
</details>

---

## 🏗️ Model Architecture

### Training Pipeline
```mermaid
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
```
<p align="center">
<img src="structure.png" width="900" alt="structure"/>
</p>

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

---

## 🔧 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 |

---

## 💻 Quick Start

### Installation
```bash
pip install transformers torch accelerate
```

### Basic Usage (Text Generation)
```python
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
```python
# 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
```python
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'])
```

---

## 🚀 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)
```dockerfile
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"]
```

---

## 📁 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
```

---

## ⚠️ 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
```python
# 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
```

---

## 🔄 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 |

---

## 📄 License & Citation

**License:** Apache 2.0

**Citation:**
```bibtex
@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}
}
```

---

## 👥 Credits & Acknowledgments

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

---

## 🤝 Contributing & Support

### Reporting Issues
Please open an issue on our repository with:
1. Model version
2. Reproduction steps
3. Expected vs actual behavior

---

<p align="center">
<i>Built with ❤️ by QuantaSparkLabs</i><br/>
<sub>Model ID: Antiplex-Instruct-3B • Parameters: ~3.8B • Release: 2026</sub>
</p>

<p align="center">
<a href="https://github.com/unslothai/unsloth">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
</a>
</p>

>Special thanks to microsoft!