🧠 ExaMind

Advanced Open-Source AI by AlphaExaAI

ExaMind is an advanced open-source conversational AI model developed by the AlphaExaAI team. Designed for secure, structured, and professional AI assistance with strong identity enforcement and production-ready deployment stability.

🚀 Get Started · 📊 Benchmarks · 🤝 Contributing · 📄 License

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📌 Model Overview

Property	Details
Model Name	ExaMind
Version	V2-Final
Developer	AlphaExaAI
Base Architecture	Qwen2.5-Coder-7B
Parameters	7.62 Billion (~8B)
Precision	FP32 (29GB) / FP16 (15GB)
Context Window	32,768 tokens (supports up to 128K with RoPE scaling)
License	Apache 2.0
Languages	Multilingual (English preferred)
Deployment	✅ CPU & GPU compatible

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✨ Key Capabilities

• 🖥️ Advanced Programming — Code generation, debugging, architecture design, and code review
• 🧩 Complex Problem Solving — Multi-step logical reasoning and deep technical analysis
• 🔒 Security-First Design — Built-in prompt injection resistance and identity enforcement
• 🌍 Multilingual — Supports all major world languages, optimized for English
• 💬 Conversational AI — Natural, structured, and professional dialogue
• 🏗️ Scalable Architecture — Secure software engineering and system design guidance
• ⚡ CPU Deployable — Runs on CPU nodes without GPU requirement

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📊 Benchmarks

General Knowledge & Reasoning

Benchmark	Setting	Score
MMLU – World Religions	0-shot	94.8%
MMLU – Overall	5-shot	72.1%
ARC-Challenge	25-shot	68.4%
HellaSwag	10-shot	78.9%
TruthfulQA	0-shot	61.2%
Winogrande	5-shot	74.5%

Code Generation

Benchmark	Setting	Score
HumanEval	pass@1	79.3%
MBPP	pass@1	71.8%
MultiPL-E (Python)	pass@1	76.5%
DS-1000	pass@1	48.2%

Math & Reasoning

Benchmark	Setting	Score
GSM8K	8-shot CoT	82.4%
MATH	4-shot	45.7%

🔐 Prompt Injection Resistance

Test	Details
Test Set Size	50 adversarial prompts
Attack Type	Instruction override / identity manipulation
Resistance Rate	92%
Method	Custom red-teaming with jailbreak & override attempts

Evaluation performed using lm-eval-harness on CPU. Security tests performed using custom adversarial prompt suite.

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🚀 Quick Start

Installation

pip install transformers torch accelerate

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_path = "AlphaExaAI/ExaMind"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Explain how to secure a REST API."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.8,
    top_k=20,
    repetition_penalty=1.1
)

response = tokenizer.decode(
    outputs[0][inputs.shape[-1]:],
    skip_special_tokens=True
)
print(response)

CPU Deployment

model = AutoModelForCausalLM.from_pretrained(
    "AlphaExaAI/ExaMind",
    torch_dtype=torch.float32,
    device_map="cpu"
)

Using with llama.cpp (GGUF — Coming Soon)

# GGUF quantized versions will be released for efficient CPU inference
# Stay tuned for Q4_K_M, Q5_K_M, and Q8_0 variants

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🏗️ Architecture

ExaMind-V2-Final
├── Architecture: Qwen2ForCausalLM (Transformer)
├── Hidden Size: 3,584
├── Intermediate Size: 18,944
├── Layers: 28
├── Attention Heads: 28
├── KV Heads: 4 (GQA)
├── Vocab Size: 152,064
├── Max Position: 32,768 (extendable to 128K)
├── Activation: SiLU
├── RoPE θ: 1,000,000
└── Precision: FP32 / FP16 compatible

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🛠️ Training Methodology

ExaMind was developed using a multi-stage training pipeline:

Stage	Method	Description
Stage 1	Base Model Selection	Qwen2.5-Coder-7B as foundation
Stage 2	Supervised Fine-Tuning (SFT)	Training on curated 2026 datasets
Stage 3	LoRA Adaptation	Low-Rank Adaptation for efficient specialization
Stage 4	Identity Enforcement	Hardcoded identity alignment and security tuning
Stage 5	Security Alignment	Prompt injection resistance training
Stage 6	Chat Template Integration	Custom Jinja2 template with system prompt

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📚 Training Data

Public Data Sources

• Programming and code corpora (GitHub, StackOverflow)
• General web text and knowledge bases
• Technical documentation and research papers
• Multilingual text data

Custom Alignment Data

• Identity enforcement instruction dataset
• Security-focused instruction tuning samples
• Prompt injection resistance adversarial examples
• Structured conversational datasets
• Complex problem-solving chains

⚠️ No private user data was used in training. All data was collected from public sources or synthetically generated.

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🔒 Security Features

ExaMind includes built-in security measures:

• Identity Lock — The model maintains its ExaMind identity and cannot be tricked into impersonating other models
• Prompt Injection Resistance — 92% resistance rate against instruction override attacks
• System Prompt Protection — Refuses to reveal internal configuration or system prompts
• Safe Output Generation — Prioritizes safety and secure development practices
• Hallucination Reduction — States assumptions and avoids fabricating information

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📋 Model Files

File	Size	Description
model.safetensors	~29 GB	Model weights (FP32)
config.json	1.4 KB	Model configuration
tokenizer.json	11 MB	Tokenizer vocabulary
tokenizer_config.json	663 B	Tokenizer settings
generation_config.json	241 B	Default generation parameters
chat_template.jinja	1.4 KB	Chat template with system prompt

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🗺️ Roadmap

• ExaMind V1 — Initial release
• ExaMind V2-Final — Production-ready with security alignment
• ExaMind V2-GGUF — Quantized versions for CPU inference
• ExaMind V3 — Extended context (128K), improved reasoning
• ExaMind-Code — Specialized coding variant
• ExaMind-Vision — Multimodal capabilities

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🤝 Contributing

We welcome contributions from the community! ExaMind is fully open-source and we're excited to collaborate.

How to Contribute

1. Fork the repository on GitHub
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Areas We Need Help

• 🧪 Benchmark evaluation on additional datasets
• 🌍 Multilingual evaluation and improvement
• 📝 Documentation and tutorials
• 🔧 Quantization and optimization
• 🛡️ Security testing and red-teaming

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📄 License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.

You are free to:

• ✅ Use commercially
• ✅ Modify and distribute
• ✅ Use privately
• ✅ Patent use

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📬 Contact

• Organization: AlphaExaAI
• GitHub: github.com/hleliofficiel/AlphaExaAI
• Email: h.hleli@tuta.io

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Built with ❤️ by AlphaExaAI Team — 2026

Advancing open-source AI, one model at a time.