Qwen3-14B-AI-Expert-250819

A specialized AI model fine-tuned for expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI Agents.

Quick Start

Pull the model

ollama pull jiakai/qwen3-14b-ai-expert-250819

Run the model

ollama run jiakai/qwen3-14b-ai-expert-250819

Chat with the model

ollama run jiakai/qwen3-14b-ai-expert-250819 "What is the difference between RAG and fine-tuning?"

Usage Examples

Basic Chat

ollama run jiakai/qwen3-14b-ai-expert-250819
>>> What is the MCP Protocol?
>>> How do AI agents work with tool calling?
>>> Explain the concept of retrieval-augmented generation

API Usage

curl http://localhost:11434/api/generate -d '{
  "model": "jiakai/qwen3-14b-ai-expert-250819",
  "prompt": "Explain the architecture of transformer models",
  "stream": false
}'

Python Integration

import requests
import json

def chat_with_model(prompt):
    response = requests.post('http://localhost:11434/api/generate',
                           json={
                               'model': 'jiakai/qwen3-14b-ai-expert-250819',
                               'prompt': prompt,
                               'stream': False
                           })
    return json.loads(response.text)['response']

# Example usage
result = chat_with_model("What are the key components of a RAG system?")
print(result)

Model Specifications

• Base Model: Qwen/Qwen3-14B
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Specialization: AI Core Technologies (LLMs, RAG, AI Agents)
• License: Apache 2.0
• Developer: real-jiakai

Specialized Domains

This model excels in providing detailed, accurate responses about:

🤖 Large Language Models (LLMs)

• Model architectures and training techniques
• Prompt engineering and optimization
• Performance evaluation and benchmarking
• Fine-tuning strategies and best practices

🔍 Retrieval-Augmented Generation (RAG)

• RAG system design and implementation
• Vector databases and embedding strategies
• Retrieval techniques and optimization
• Hybrid search approaches

🛠️ AI Agents

• Agent architectures and frameworks
• Tool integration and function calling
• Multi-agent systems and coordination
• Planning and reasoning mechanisms

Links

• 🤗 Hugging Face Model
• 🤖 ModelScope
• 🦙 Ollama Model

Training Details

• Dataset: 3,120 high-quality Alpaca-format items
  • LLM: 1,478 pairs
  • RAG: 1,178 pairs
    
  • AI Agents: 464 pairs
• Fine-tuning Framework: LlaMA-Factory
• Training Logs: Weights & Biases

Citation

@misc{Qwen3-14B-AI-Expert-250819,
  author = {real-jiakai},
  title = {Qwen3-14B-AI-Expert-250819},
  year = 2025,
  url = {https://huggingface.co/GXMZU/Qwen3-14B-ai-expert-250819},
  publisher = {Hugging Face}
}