---
title: gemma-3-12b-it-ai-expert
canonical_url: "https://www.modelscope.cn/models/GXMZU/gemma-3-12b-it-ai-expert"
md_url: "https://www.modelscope.cn/models/GXMZU/gemma-3-12b-it-ai-expert.md"
repository: GXMZU/gemma-3-12b-it-ai-expert
last_updated: 2026-01-05
license: gemma
pipeline_tag: question-answering
tasks:
  - question-answering
model_type:
  - gemma3
architectures:
  - Gemma3ForConditionalGeneration
base_model:
  - google/gemma-3-12b-it
base_model_relation: finetune
parameters: 12.2B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
language:
  - en
  - zh
inference_backends:
  - "deploy_task vlm/text/emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 25
stars: 1
tags:
  - text-generation-inference
  - transformers
  - gemma3
---

# gemma-3-12b-it-ai-expert

> gemma-3-12b-it-ai-expert - GXMZU 在 ModelScope 开源的模型。gemma-3-12b-it-ai-expert - A Fine-tuned Model for AI Core Technologies

GXMZU/gemma-3-12b-it-ai-expert 是 ModelScope 魔搭社区上的 12.2B 参数question-answering模型，采用 gemma 许可，基于 google/gemma-3-12b-it 构建，可用 deploy_task vlm/text/emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: GXMZU/gemma-3-12b-it-ai-expert
- **License**: gemma
- **Tasks**: question-answering
- **Parameters**: 12.2B
- **Base model**: google/gemma-3-12b-it
- **Inference backends**: deploy_task vlm/text/emb, sglang 0.5.2, vllm 0.9.2
- **Tags**: text-generation-inference, transformers, gemma3
- **Downloads**: 25
- **Stars**: 1
- **Last updated**: 2026-01-05

Source: https://www.modelscope.cn/models/GXMZU/gemma-3-12b-it-ai-expert

---

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

<p align="center">
        🤗 <a href="https://huggingface.co/GXMZU/gemma-3-12b-it-ai-expert">Hugging Face</a>&nbsp;&nbsp; | &nbsp;&nbsp;🤖 <a href="https://www.modelscope.cn/models/GXMZU/gemma-3-12b-it-ai-expert">ModelScope</a>
</p>

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](https://huggingface.co/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](https://github.com/hiyouga/LLaMA-Factory).

- **Developed by:** [real-jiakai](https://huggingface.co/real-jiakai)
- **License:** gemma
- **Finetuned from model :** google/gemma-3-12b-it

## Usage

```python
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:

![demo](https://cdn.sa.net/2025/10/01/13yTmUZaDViEJWp.webp)

## 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}
}
```