gemma-3-12b-it-ai-expert - A Fine-tuned Model for AI Core Technologies

🤗 Hugging Face   |   🤖 ModelScope

This model is a specialized expert on core Artificial Intelligence concepts, developed by performing Instruction Supervised Fine-Tuning (SFT) on the google/gemma-3-12b-it model.

The fine-tuning was conducted using Low-Rank Adaptation (LoRA), a parameter-efficient technique, on a custom-built dataset. This process adapted the model to provide high-quality, detailed responses specifically within the domains of:

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI Agents

The model was fine-tuned with LlaMA-Factory.

• Developed by: real-jiakai
• License: gemma
• Finetuned from model : google/gemma-3-12b-it

Usage

from transformers import pipeline
import torch

pipe = pipeline(
    model="GXMZU/gemma-3-12b-it-ai-expert",
    device="cuda",
    torch_dtype=torch.bfloat16
)

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are an AI expert assistant(Focus on LLM, RAG, and Agent Domain) to help with technical questions. You should provide clear, accurate, and helpful responses."}]
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What is MCP Protocol?"}
        ]
    }
]

output = pipe(messages, max_new_tokens=200, temperature=0.1)
print(output[0]["generated_text"][-1]["content"])

Performance

The primary objective of this fine-tuning is to adapt the model to a specialized domain, enhancing its performance on specific tasks by injecting relevant knowledge and terminology while preserving its foundational generalist capabilities.

Before Fine-tuning vs After Fine-tuning

The model demonstrates significant improvements in domain-specific tasks related to LLM, RAG, and AI Agents, as shown in the example below:

Fine-tuning Procedure

Dataset

The model was fine-tuned on a custom, high-quality dataset. The dataset was carefully curated to cover three core areas:

• Large Language Models (LLM)
• Retrieval-Augmented Generation (RAG)
• AI Agents

Citation

If you use this model in your work, please cite it as:

@misc{gemma-3-12b-it-ai-expert,
  author = {real-jiakai},
  title = {gemma-3-12b-it-ai-expert},
  year = 2025,
  url = {https://huggingface.co/GXMZU/gemma-3-12b-it-ai-expert},
  publisher = {Hugging Face}
}

@article{gemma_2025,
    title={Gemma 3},
    url={https://goo.gle/Gemma3Report},
    publisher={Kaggle},
    author={Gemma Team},
    year={2025}
}