---
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-8bit**  

### **Welcome to Nidum!**  
At Nidum, our mission is to bring cutting-edge AI capabilities to everyone with unrestricted access to innovation. With **Nidum-Llama-3.2-3B-Uncensored-MLX-8bit**, you get an optimized, efficient, and versatile AI model for diverse applications.  

---

[![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)  
**Discover Nidum's Open-Source Projects on GitHub**: [https://github.com/NidumAI-Inc](https://github.com/NidumAI-Inc)  

---

### **Key Features**

1. **Efficient and Compact**: Developed in **MLX-8bit format** for improved performance and reduced memory demands.  
2. **Wide Applicability**: Suitable for technical problem-solving, educational content, and conversational tasks.  
3. **Advanced Context Awareness**: Handles long-context conversations with exceptional coherence.  
4. **Streamlined Integration**: Optimized for use with the **mlx-lm library** for effortless development.  
5. **Unrestricted Responses**: Offers uncensored answers across all supported domains.  

---

### **How to Use**

To use **Nidum-Llama-3.2-3B-Uncensored-MLX-8bit**, install the **mlx-lm** library and follow these steps:

#### **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-8bit")

# 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-8bit** model, converted using **mlx-lm version 0.19.2**, brings:  

- **Memory Efficiency**: Tailored for systems with limited hardware.  
- **Performance Optimization**: Matches the capabilities of the original model while delivering faster inference.  
- **Plug-and-Play**: Easily integrates with the **mlx-lm** library for deployment ease.  

---

### **Use Cases**

- **Problem Solving in Tech and Science**  
- **Educational and Research Assistance**  
- **Creative Writing and Brainstorming**  
- **Extended Dialogues**  
- **Uninhibited Knowledge Exploration**  

---

### **Datasets and Fine-Tuning**  

Derived from **Nidum-Llama-3.2-3B-Uncensored**, the MLX-8bit version inherits:  

- **Uncensored Fine-Tuning**: Delivers detailed and open-ended responses.  
- **RAG-Based Optimization**: Enhances retrieval-augmented generation for data-driven tasks.  
- **Math Reasoning Support**: Precise mathematical computations and explanations.  
- **Long-Context Training**: Ensures relevance and coherence in extended conversations.  

---

### **Quantized Model Download**  

The **MLX-8bit** format strikes the perfect balance between memory optimization and performance.  

---

#### **Benchmark**

| **Benchmark**    | **Metric**                       | **LLaMA 3B** | **Nidum 3B** | **Observation**                                                                                     |
|-------------------|-----------------------------------|--------------|--------------|-----------------------------------------------------------------------------------------------------|
| **GPQA**         | Exact Match (Flexible)           | 0.3          | 0.5          | Nidum 3B achieves notable improvement in **generative tasks**.                                      |
|                  | Accuracy                         | 0.4          | 0.5          | Demonstrates strong performance, especially in **zero-shot** tasks.                                 |
| **HellaSwag**    | Accuracy                         | 0.3          | 0.4          | Excels in **common-sense reasoning** tasks.                                                         |
|                  | Normalized Accuracy              | 0.3          | 0.4          | Strong contextual understanding in sentence completion tasks.                                       |
|                  | Normalized Accuracy (Stderr)     | 0.15275      | 0.1633       | Enhanced consistency in normalized accuracy.                                                        |
|                  | Accuracy (Stderr)                | 0.15275      | 0.1633       | Demonstrates robustness in reasoning accuracy compared to LLaMA 3B.                                 |

---

### **Insights**

1. **High Performance, Low Resource**: The MLX-8bit format is ideal for environments with limited memory and processing power.  
2. **Seamless Integration**: Designed for smooth integration into lightweight systems and workflows.  

---

### **Contributing**  

Join us in enhancing the **MLX-8bit** model's capabilities. Contact us for collaboration opportunities.  

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### **Contact**  

For questions, support, or feedback, email **info@nidum.ai**.  

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### **Experience the Future**  

Harness the power of **Nidum-Llama-3.2-3B-Uncensored-MLX-8bit** for a perfect blend of performance and efficiency.  

---