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README.md

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+---
+license: apache-2.0
+language:
+- en
+- ar
+- fr
+- zh
+- de
+- es
+- ja
+- ko
+- ru
+- pt
+- multilingual
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen2
+- chat
+- code
+- security
+- alphaexaai
+- examind
+- conversational
+- open-source
+base_model:
+- Qwen/Qwen2.5-Coder-7B
+model-index:
+- name: ExaMind-V2-Final
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU
+      type: cais/mmlu
+    metrics:
+    - type: accuracy
+      name: MMLU World Religions (0-shot)
+      value: 94.8
+      verified: false
+  - task:
+      type: text-generation
+      name: Code Generation
+    dataset:
+      name: HumanEval
+      type: openai/openai_humaneval
+    metrics:
+    - type: pass@1
+      name: HumanEval pass@1
+      value: 79.3
+      verified: false
+---
+
+<div align="center">
+
+# 🧠 ExaMind
+
+### Advanced Open-Source AI by AlphaExaAI
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Model](https://img.shields.io/badge/Model-7B%20Parameters-purple)](https://huggingface.co/AlphaExaAI/ExaMind)
+[![GitHub](https://img.shields.io/badge/GitHub-AlphaExaAI-black?logo=github)](https://github.com/hleliofficiel/AlphaExaAI)
+[![Architecture](https://img.shields.io/badge/Architecture-Qwen2-green)](https://huggingface.co/Qwen)
+
+**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](#-quick-start) · [📊 Benchmarks](#-benchmarks) · [🤝 Contributing](#-contributing) · [📄 License](#-license)
+
+</div>
+
+---
+
+## 📌 Model Overview
+
+| Property | Details |
+|----------|---------|
+| **Model Name** | ExaMind |
+| **Version** | V2-Final |
+| **Developer** | [AlphaExaAI](https://github.com/hleliofficiel/AlphaExaAI) |
+| **Base Architecture** | Qwen2.5-Coder-7B |
+| **Parameters** | 7 Billion (7B) |
+| **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 |
+
+---
+
+## ✨ 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
+
+---
+
+## 📊 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.
+
+---
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+pip install transformers torch accelerate
+```
+
+### Basic Usage
+
+```python
+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
+
+```python
+model = AutoModelForCausalLM.from_pretrained(
+    "AlphaExaAI/ExaMind",
+    torch_dtype=torch.float32,
+    device_map="cpu"
+)
+```
+
+### Using with llama.cpp (GGUF — Coming Soon)
+
+```bash
+# GGUF quantized versions will be released for efficient CPU inference
+# Stay tuned for Q4_K_M, Q5_K_M, and Q8_0 variants
+```
+
+---
+
+## 🏗️ 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
+```
+
+---
+
+## 🛠️ 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 |
+
+---
+
+## 📚 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.
+
+---
+
+## 🔒 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
+
+---
+
+## 📋 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 |
+
+---
+
+## 🗺️ Roadmap
+
+- [x] ExaMind V1 — Initial release
+- [x] 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
+
+---
+
+## 🤝 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](https://github.com/hleliofficiel/AlphaExaAI)
+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
+
+---
+
+## 📄 License
+
+This project is licensed under the **Apache License 2.0** — see the [LICENSE](LICENSE) file for details.
+
+You are free to:
+- ✅ Use commercially
+- ✅ Modify and distribute
+- ✅ Use privately
+- ✅ Patent use
+
+---
+
+## 📬 Contact
+
+- **Organization:** [AlphaExaAI](https://huggingface.co/AlphaExaAI)
+- **GitHub:** [github.com/hleliofficiel/AlphaExaAI](https://github.com/hleliofficiel/AlphaExaAI)
+- **Email:** mahmedhleli@gmail.com
+
+---
+
+<div align="center">
+
+**Built with ❤️ by AlphaExaAI Team — 2026**
+
+*Advancing open-source AI, one model at a time.*
+
+</div>