---
license: apache-2.0
base_model: nidum/Nidum-Llama-3.2-3B-Uncensored
library_name: adapter-transformers
tags:
- chemistry
- biology
- legal
- code
- medical
- finance
- mlx
pipeline_tag: text-generation
---

### Nidum-Llama-3.2-3B-Uncensored-MLX-4bit  

### Welcome to Nidum!  
At Nidum, we are committed to delivering cutting-edge AI models that offer advanced capabilities and unrestricted access to innovation. With **Nidum-Llama-3.2-3B-Uncensored-MLX-4bit**, we bring you a performance-optimized, space-efficient, and feature-rich model designed for diverse use cases.  

---

[![GitHub Icon](https://upload.wikimedia.org/wikipedia/commons/thumb/9/95/Font_Awesome_5_brands_github.svg/232px-Font_Awesome_5_brands_github.svg.png)](https://github.com/NidumAI-Inc)  
**Explore Nidum's Open-Source Projects on GitHub**: [https://github.com/NidumAI-Inc](https://github.com/NidumAI-Inc)

---

### Key Features

1. **Compact and Efficient**: Built in the **MLX-4bit format** for optimized performance with minimal memory usage.  
2. **Versatility**: Excels in a wide range of tasks, including technical problem-solving, educational queries, and casual conversations.  
3. **Extended Context Handling**: Capable of maintaining coherence in long-context interactions.  
4. **Seamless Integration**: Enhanced compatibility with the **mlx-lm library** for a streamlined development experience.  
5. **Uncensored Access**: Provides uninhibited responses across a variety of topics and applications.  

---

### How to Use

To utilize **Nidum-Llama-3.2-3B-Uncensored-MLX-4bit**, install the **mlx-lm** library and follow the example code below:

#### Installation

```bash
pip install mlx-lm
```

#### Usage

```python
from mlx_lm import load, generate

# Load the model and tokenizer
model, tokenizer = load("nidum/Nidum-Llama-3.2-3B-Uncensored-MLX-4bit")

# Create a prompt
prompt = "hello"

# Apply the chat template if available
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

# Generate the response
response = generate(model, tokenizer, prompt=prompt, verbose=True)

# Print the response
print(response)
```

---

### About the Model

The **nidum/Nidum-Llama-3.2-3B-Uncensored-MLX-4bit** model was converted to MLX format from **nidum/Nidum-Llama-3.2-3B-Uncensored** using **mlx-lm version 0.19.2**, providing the following benefits:  

- **Smaller Memory Footprint**: Ideal for environments with limited hardware resources.  
- **High Performance**: Retains the advanced capabilities of the original model while optimizing inference speed and efficiency.  
- **Plug-and-Play Compatibility**: Easily integrate with the **mlx-lm** ecosystem for seamless deployment.  

---

### Use Cases

- **Technical Problem Solving**  
- **Research and Educational Assistance**  
- **Open-Ended Q&A**  
- **Creative Writing and Ideation**  
- **Long-Context Dialogues**  
- **Unrestricted Knowledge Exploration**  

---

### Datasets and Fine-Tuning  

The model inherits the fine-tuned capabilities of its predecessor, **Nidum-Llama-3.2-3B-Uncensored**, including:  

- **Uncensored Data**: Ensures detailed and uninhibited responses.  
- **RAG-Based Fine-Tuning**: Optimizes retrieval-augmented generation for information-intensive tasks.  
- **Math-Instruct Data**: Tailored for precise mathematical reasoning.  
- **Long-Context Fine-Tuning**: Enhanced coherence and relevance in extended interactions.  

---

### Quantized Model Download  

The **MLX-4bit** version is highly efficient, maintaining a balance between precision and memory usage.  

---

#### Benchmark

| **Benchmark**    | **Metric**                       | **LLaMA 3B** | **Nidum 3B** | **Observation**                                                                                     |
|-------------------|-----------------------------------|--------------|--------------|-----------------------------------------------------------------------------------------------------|
| **GPQA**         | Exact Match (Flexible)           | 0.3          | 0.5          | Nidum 3B demonstrates significant improvement, particularly in **generative tasks**.                |
|                  | Accuracy                         | 0.4          | 0.5          | Consistent improvement, especially in **zero-shot** scenarios.                                      |
| **HellaSwag**    | Accuracy                         | 0.3          | 0.4          | Better performance in **common sense reasoning** tasks.                                             |
|                  | Normalized Accuracy              | 0.3          | 0.4          | Enhanced ability to understand and predict context in sentence completion.                          |
|                  | Normalized Accuracy (Stderr)     | 0.15275      | 0.1633       | Slightly improved consistency in normalized accuracy.                                               |
|                  | Accuracy (Stderr)                | 0.15275      | 0.1633       | Shows robustness in reasoning accuracy compared to LLaMA 3B.                                        |

---

### Insights:

1. **Compact Efficiency**: The MLX-4bit model ensures high performance with reduced resource usage.  
2. **Enhanced Usability**: Optimized for seamless integration with lightweight deployment scenarios.  

---

### Contributing  

We invite contributions to further enhance the **MLX-4bit** model's capabilities. Reach out to us for collaboration opportunities.  

---

### Contact  

For inquiries, support, or feedback, email us at **info@nidum.ai**.  

---

### Explore the Future  

Embrace the power of innovation with **Nidum-Llama-3.2-3B-Uncensored-MLX-4bit**—the ideal blend of performance and efficiency.  

---