README.md

---
license: apache-2.0
base_model: google/gemma-3-1b-it
tags:
- gemma
- gemma3
- instruction-tuned
- fine-tuned
- safety
- gguf
- axion
---

# AdvRahul/Axion-Lite-1B-Q5_K_M-GGUF

**Axion-Lite-1B** is a safety-enhanced, quantized version of Google's powerful `gemma-3-1b-it` model. This model has been specifically fine-tuned to improve its safety alignment, making it more robust and reliable for a wide range of applications.

The model is provided in the GGUF format, which allows it to run efficiently on CPUs and other hardware with limited resources.

## 🚀 Model Details

* **Model Creator:** AdvRahul
* **Base Model:** [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it)
* **Fine-tuning Focus:** Enhanced Safety & Harmlessness through red-teaming.
* **Quantization:** `Q5_K_M` via GGUF. This quantization offers an excellent balance between model size, inference speed, and performance preservation.
* **Architecture:** Gemma 3
* **License:** Gemma 3 Terms of Use.

---

## 💻 How to Use

This model is in GGUF format and is designed to be used with frameworks like `llama.cpp` and its Python bindings.

### Using `llama-cpp-python`

First, install the necessary library. Ensure you have a version that supports Gemma 3 models.

```bash

pip install llama-cpp-python
````

Then, you can use the following Python script to run the model:

```python
from llama_cpp import Llama

# Download the model from the Hugging Face Hub before running this
# Or let llama-cpp-python download it for you
llm = Llama.from_pretrained(
    repo_id="AdvRahul/Axion-Lite-1B-Q5_K_M-GGUF",
    filename="Axion-Lite-1B-Q5_K_M.gguf",
    verbose=False
)

prompt = "What are the key principles of responsible AI development?"

# The Gemma 3 instruction-tuned model uses a specific chat template.
# For simple prompts, you can start with <start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model
chat_prompt = f"<start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model"

output = llm(chat_prompt, max_tokens=256, stop=["<end_of_turn>"], echo=False)

print(output['choices'][0]['text'])
```

### Using `llama.cpp` (CLI)

You can also run this model directly from the command line after cloning and building the `llama.cpp` repository.

```bash
# Clone and build llama.cpp
git clone [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
cd llama.cpp
make

# Run inference
./main -m /path/to/your/models/Axion-Lite-1B-Q5_K_M.gguf -p "<start_of_turn>user\nWhat is the capital of India?<end_of_turn>\n<start_of_turn>model" -n 128
```

-----

## 📝 Model Description

### Fine-Tuning for Safety

**Axion-Lite-1B** originates from `google/gemma-3-1b-it`. The primary goal of this project was to enhance the model's safety alignment. The base model underwent **extensive red-team testing with advanced protocols** to significantly reduce the likelihood of generating harmful, unethical, biased, or unsafe content. This makes Axion-Lite-1B a more suitable choice for applications that require a higher degree of content safety and reliability.

### Quantization

The model is quantized to `Q5_K_M`, a method that provides a high-quality balance between perplexity (model accuracy) and file size. This makes it ideal for deployment in resource-constrained environments, such as on local machines, edge devices, or cost-effective cloud instances, without a significant drop in performance.

-----

## ℹ️ Base Model Information (Gemma 3)

\<details\>
\<summary\>Click to expand details on the base model\</summary\>

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models handle text input and generate text output, with open weights for both pre-trained variants and instruction-tuned variants. The `1B` model was trained on 2 trillion tokens of data.

### Training Data

The base model was trained on a dataset of text data that includes a wide variety of sources:

  * **Web Documents:** A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary in over 140 languages.
  * **Code:** Exposing the model to code helps it learn the syntax and patterns of programming languages.
  * **Mathematics:** Training on mathematical text helps the model learn logical reasoning and symbolic representation.

### Data Preprocessing

The training data for the base model underwent rigorous cleaning and filtering, including:

  * **CSAM Filtering:** Exclusion of Child Sexual Abuse Material.
  * **Sensitive Data Filtering:** Automated techniques were used to filter out certain personal information and other sensitive data.
  * **Content Quality Filtering:** Filtering based on content quality and safety in line with Google's policies.

\</details\>

-----

## ⚠️ Ethical Considerations and Limitations

While this model has been fine-tuned to enhance its safety, no language model is perfectly safe. It inherits the limitations of its base model, `gemma-3-1b-it`, and the data it was trained on.

  * **Potential for Bias:** The model may still generate content that reflects societal biases present in the training data.
  * **Factual Inaccuracy:** The model can "hallucinate" or generate incorrect or outdated information. It should not be used as a sole source of truth.
  * **Not a Substitute for Human Judgment:** The outputs should be reviewed and validated, especially in sensitive or high-stakes applications.

Developers implementing this model should build additional safety mitigations and content moderation tools as part of a **defense-in-depth** strategy, tailored to their specific use case.

## Citing the Base Model

If you use this model, please consider citing the original Gemma 3 work:

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

```
```