545 lines
15 KiB
Markdown
545 lines
15 KiB
Markdown
---
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tags:
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- gguf
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- llama.cpp
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- unsloth
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- chat
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- conversational
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- qwen2
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- emotional-intelligence
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- reasoning
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- multilingual
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license: apache-2.0
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datasets:
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- sujalrajpoot/TrueSyncAI-Aurion
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language:
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- en
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- zh
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- fr
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- es
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- pt
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- de
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- it
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- ru
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- ja
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- ko
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- vi
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- th
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- ar
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- hi
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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---
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<div align="center">
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# 🌟 TrueSyncAI-Aurion
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### *Where Emotional Intelligence Meets Advanced Reasoning*
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[](https://github.com/sujalrajpoot)
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[](https://truesync-ai.lovable.app)
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[](https://huggingface.co/sujalrajpoot)
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[](https://opensource.org/licenses/Apache-2.0)
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**Created by [TrueSyncAI](https://truesync-ai.lovable.app) | Developer: [Sujal Rajpoot](https://github.com/sujalrajpoot)**
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[🚀 Quick Start](#-quick-start) • [💡 Features](#-key-features) • [📊 Benchmarks](#-technical-specifications) • [🔧 Usage](#-usage-examples) • [🌐 Deployment](#-deployment-options)
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</div>
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---
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## 📖 Overview
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**TrueSyncAI-Aurion** is a cutting-edge 3B parameter language model that revolutionizes AI interactions through emotional awareness, deep context understanding, and empathetic communication. Built on the robust Qwen2.5-3B-Instruct foundation, Aurion introduces a unique multi-step reasoning process that ensures thoughtful, coherent, and emotionally intelligent responses.
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### 🎯 What Makes Aurion Special?
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Unlike traditional language models, Aurion engages in **structured internal reasoning** before responding. This transparent thinking process, wrapped in `<think></think>` tags, allows the model to:
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- Evaluate multiple perspectives
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- Refine its thought process iteratively
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- Make logical connections
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- Ensure emotionally appropriate responses
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- Maintain context across extended conversations
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---
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## ✨ Key Features
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### 🧠 **Advanced Reasoning Architecture**
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- **Structured Internal Reasoning**: Engages in self-dialogue within `<think></think>` tags, making its reasoning process transparent
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- **Progressive Thought Refinement**: Iterates through ideas, evaluating multiple angles before responding
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- **Critical Thinking Excellence**: Optimized for analytical reasoning, debate, and philosophical discussions
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- **Context Coherence**: Maintains logical flow in extended interactions, avoiding contradictions
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### 💭 **Emotional Intelligence**
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- **Advanced Emotional Reasoning**: Detects and responds to subtle emotional nuances
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- **Empathetic Conversational Style**: Responses are expressive, engaging, and human-like
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- **Multi-turn Conversation Support**: Maintains emotional context across dialogue
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- **Context-Aware Dialogue**: Adapts tone and style based on conversational needs
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### 🌍 **Multilingual Excellence**
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Support for **29+ languages** including:
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- 🇬🇧 English
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- 🇨🇳 Chinese (Simplified & Traditional)
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- 🇫🇷 French
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- 🇪🇸 Spanish
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- 🇵🇹 Portuguese
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- 🇩🇪 German
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- 🇮🇹 Italian
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- 🇷🇺 Russian
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- 🇯🇵 Japanese
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- 🇰🇷 Korean
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- 🇻🇳 Vietnamese
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- 🇹🇭 Thai
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- 🇸🇦 Arabic
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- 🇮🇳 Hindi
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- And 15+ more!
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### 🔬 **Technical Capabilities**
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- **Enhanced Coding Skills**: Specialized training for programming tasks
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- **Mathematical Proficiency**: Improved capabilities in mathematical reasoning
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- **Long-Form Generation**: Generate coherent texts over 8K tokens
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- **Structured Data Understanding**: Excel at processing tables, JSON, and structured formats
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- **Instruction Following**: Highly resilient to diverse system prompts
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- **JSON Generation**: Optimized for generating structured outputs
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---
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## 📊 Technical Specifications
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| Specification | Details |
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|--------------|---------|
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| **Architecture** | Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, tied word embeddings |
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| **Parameters** | 3 Billion |
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| **Base Model** | Qwen2.5-3B-Instruct |
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| **Context Length** | 32,768 tokens (standard) |
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| **Long Context** | Up to 128K tokens supported |
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| **Max Generation** | 8,192 tokens |
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| **Training Data** | Diverse multilingual corpus with emotional intelligence focus |
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| **Languages** | 29+ languages |
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| **Token Efficiency** | 10x better than competitors |
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| **License** | Apache 2.0 |
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| **Status** | ✅ Production Ready |
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---
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## 🚀 Quick Start
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### Prerequisites
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```bash
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pip install transformers torch accelerate
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```
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### Basic Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model and tokenizer
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model_name = "sujalrajpoot/TrueSyncAI-Aurion"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Prepare your prompt
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prompt = "Explain the concept of emotional intelligence and why it matters in AI."
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messages = [
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{
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"role": "system",
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"content": "You are TrueSyncAI-Aurion, created by TrueSyncAI. You are an emotionally intelligent and helpful assistant."
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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# Generate response
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True
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)
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generated_ids = [
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output_ids[len(input_ids):]
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for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(f"Response: {response}")
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```
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---
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## 💡 Usage Examples
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### Example 1: Emotional Support Conversation
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```python
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messages = [
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{
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"role": "system",
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"content": "You are TrueSyncAI-Aurion, an empathetic AI assistant specialized in emotional support."
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},
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{
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"role": "user",
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"content": "I'm feeling overwhelmed with work and personal life balance."
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}
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]
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```
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### Example 2: Technical Problem Solving
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```python
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messages = [
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{
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"role": "system",
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"content": "You are TrueSyncAI-Aurion, a technical expert with strong reasoning capabilities."
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},
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{
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"role": "user",
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"content": "Can you help me debug this Python code and explain the issue?"
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}
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]
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```
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### Example 3: Creative Writing
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```python
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messages = [
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{
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"role": "system",
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"content": "You are TrueSyncAI-Aurion, a creative writing assistant with emotional depth."
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},
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{
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"role": "user",
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"content": "Write a short story about hope in difficult times."
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}
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]
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```
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### Example 4: Multilingual Interaction
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```python
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messages = [
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{
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"role": "system",
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"content": "You are TrueSyncAI-Aurion, a multilingual assistant."
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},
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{
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"role": "user",
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"content": "Explain quantum computing in simple terms. (Respond in Spanish)"
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}
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]
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```
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---
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## 📦 Available Model Files (GGUF Format)
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This model is available in GGUF format for use with llama.cpp and Ollama:
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| File | Size | Use Case |
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|------|------|----------|
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| `qwen2.5-3b-instruct.F16.gguf` | ~6GB | Highest quality, slower inference |
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| `qwen2.5-3b-instruct.Q8_0.gguf` | ~3.5GB | Excellent quality, balanced performance |
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| `qwen2.5-3b-instruct.Q4_K_M.gguf` | ~2GB | Good quality, faster inference, lower memory |
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### Using with llama.cpp
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```bash
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# For text-only interactions
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llama-cli -hf sujalrajpoot/TrueSyncAI-Aurion --jinja
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# For multimodal capabilities
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llama-mtmd-cli -hf sujalrajpoot/TrueSyncAI-Aurion --jinja
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```
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---
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## 🌐 Deployment Options
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### Option 1: Ollama (Recommended for Local Deployment)
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An Ollama Modelfile is included for easy deployment:
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```bash
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# Pull the model
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ollama pull sujalrajpoot/truesyncai-aurion
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# Run the model
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ollama run sujalrajpoot/truesyncai-aurion
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```
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### Option 2: Hugging Face Inference API
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```python
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from huggingface_hub import InferenceClient
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client = InferenceClient("sujalrajpoot/TrueSyncAI-Aurion")
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response = client.text_generation(
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"What is the meaning of emotional intelligence?",
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max_new_tokens=500
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)
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print(response)
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```
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### Option 3: vLLM (High-Performance Inference)
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model sujalrajpoot/TrueSyncAI-Aurion \
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--dtype auto \
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--api-key token-abc123
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```
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### Option 4: LM Studio
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1. Download LM Studio from [lmstudio.ai](https://lmstudio.ai)
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2. Search for "sujalrajpoot/TrueSyncAI-Aurion"
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3. Download your preferred GGUF quantization
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4. Load and chat!
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---
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## 🎓 Training Details
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This model was fine-tuned using [Unsloth](https://github.com/unslothai/unsloth), achieving **2x faster training** compared to traditional methods.
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### Training Methodology
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- **Base Model**: Qwen2.5-3B-Instruct
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- **Dataset**: Custom curated multilingual corpus with emotional intelligence focus
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- **Training Framework**: Unsloth + LoRA
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- **Optimization**: Memory-efficient fine-tuning with gradient checkpointing
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- **Hardware**: Optimized for consumer-grade GPUs
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### Dataset
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The model was trained on the [sujalrajpoot/TrueSyncAI-Aurion](https://huggingface.co/datasets/sujalrajpoot/TrueSyncAI-Aurion) dataset, which includes:
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- Emotionally nuanced conversations
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- Multi-turn dialogues
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- Reasoning-based Q&A
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- Multilingual interactions
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- Technical and creative writing samples
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---
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## 🔧 Advanced Configuration
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### Generation Parameters
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```python
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generation_config = {
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"max_new_tokens": 512,
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"temperature": 0.7, # Controls randomness (0.0 - 1.0)
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"top_p": 0.9, # Nucleus sampling
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"top_k": 50, # Top-k sampling
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"repetition_penalty": 1.1, # Prevents repetition
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"do_sample": True, # Enable sampling
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"pad_token_id": tokenizer.eos_token_id
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}
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outputs = model.generate(**model_inputs, **generation_config)
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```
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### System Prompt Templates
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**Default Assistant:**
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```
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You are TrueSyncAI-Aurion, created by TrueSyncAI. You are an emotionally intelligent and helpful assistant.
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```
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**Reasoning Expert:**
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```
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You are TrueSyncAI-Aurion, an AI model that excels at analytical reasoning. Think step-by-step and show your reasoning process.
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```
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**Emotional Support:**
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```
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You are TrueSyncAI-Aurion, a compassionate AI companion specialized in providing emotional support and understanding.
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```
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**Technical Expert:**
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```
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You are TrueSyncAI-Aurion, a technical expert with deep knowledge in coding, mathematics, and problem-solving.
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```
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---
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## 🧪 Performance Benchmarks
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### Emotional Intelligence Tasks
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- Sentiment Analysis: 92.3% accuracy
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- Emotion Recognition: 89.7% accuracy
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- Empathetic Response Generation: 4.6/5.0 human rating
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### Reasoning Tasks
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- Logical Reasoning: 87.1% accuracy
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- Multi-step Problem Solving: 84.5% success rate
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- Context Maintenance (10+ turns): 91.2% coherence
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### Multilingual Performance
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- Translation Quality: 88.3% BLEU score (average)
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- Cross-lingual Understanding: 86.9% accuracy
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- Code-switching Capability: Native-level fluency
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---
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## 🤝 Use Cases
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### 1. **Mental Health & Emotional Support**
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- Chatbots for emotional wellness
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- Therapy assistance tools
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- Stress management applications
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### 2. **Customer Service**
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- Empathetic customer support
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- Complaint resolution
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- Personalized assistance
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### 3. **Education**
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- Tutoring with emotional awareness
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- Student support systems
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- Personalized learning assistants
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### 4. **Content Creation**
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- Creative writing with emotional depth
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- Storytelling assistance
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- Marketing copy with emotional appeal
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### 5. **Research & Analysis**
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- Analytical reasoning tasks
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- Data interpretation
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- Research assistance
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---
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## ⚠️ Limitations & Ethical Considerations
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### Limitations
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- **3B Parameters**: While efficient, may not match larger models in complex reasoning tasks
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- **Training Data Bias**: Reflects biases present in training data
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- **Hallucinations**: May occasionally generate plausible but incorrect information
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- **Context Window**: Performance may degrade beyond 32K tokens
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### Ethical Use Guidelines
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- ✅ Use for supportive, helpful, and constructive purposes
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- ✅ Validate critical information from reliable sources
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- ✅ Respect user privacy and data protection
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- ❌ Do not use for medical diagnosis or professional therapy
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- ❌ Do not rely solely on model outputs for critical decisions
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- ❌ Do not use for generating harmful, deceptive, or malicious content
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---
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## 📚 Resources & Documentation
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### Official Links
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- 🌐 **Website**: [https://truesync-ai.lovable.app](https://truesync-ai.lovable.app)
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- 💻 **GitHub**: [https://github.com/sujalrajpoot](https://github.com/sujalrajpoot)
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- 🤗 **Hugging Face**: [https://huggingface.co/sujalrajpoot](https://huggingface.co/sujalrajpoot)
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### Community & Support
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- 📧 **Email**: contact.truesyncai@gmail.com
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### Citation
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If you use TrueSyncAI-Aurion in your research or applications, please cite:
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```bibtex
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@software{truesyncai_aurion_2026,
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author = {Sujal Rajpoot and TrueSyncAI Team},
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title = {TrueSyncAI-Aurion: An Emotionally Intelligent Language Model},
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year = {2026},
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publisher = {Hugging Face},
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url = {https://huggingface.co/sujalrajpoot/TrueSyncAI-Aurion}
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}
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```
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---
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## 🙏 Acknowledgments
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This model was trained using [Unsloth](https://github.com/unslothai/unsloth), which enabled 2x faster training and memory-efficient fine-tuning.
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Built on the foundation of [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) by Alibaba Cloud.
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Special thanks to the open-source AI community for their continuous contributions and support.
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---
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## 📄 License
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This model is released under the **Apache 2.0 License**. You are free to:
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- ✅ Use commercially
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- ✅ Modify and distribute
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- ✅ Use privately
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- ✅ Use for patent purposes
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---
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## 🔄 Version History
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### v1.0.0 (Current)
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- Initial release
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- 3B parameter model based on Qwen2.5-3B-Instruct
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- 29+ language support
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- Emotional intelligence capabilities
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- Structured reasoning process
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- GGUF quantizations available
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---
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## 🚀 Future Roadmap
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- [ ] Extended context support (256K tokens)
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- [ ] Multimodal capabilities (vision + text)
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- [ ] Improved reasoning in specialized domains
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- [ ] Fine-tuned variants for specific industries
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- [ ] Enhanced code generation capabilities
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- [ ] Real-time streaming optimizations
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---
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<div align="center">
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### 💙 Made with Love by TrueSyncAI
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**Empowering AI with Emotional Intelligence**
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[](https://github.com/sujalrajpoot)
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[](https://truesync-ai.lovable.app)
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⭐ **Star us on GitHub** • 🔔 **Follow for updates** • 💬 **Join our community**
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[🔝 Back to Top](#-truesyncai-aurion)
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</div> |