Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model:
+- nidum/Nidum-Llama-3.2-3B-Uncensored
+- meta-llama/Llama-3.2-3B
+library_name: adapter-transformers
+tags:
+- chemistry
+- biology
+- legal
+- code
+- medical
+- finance
+---
+### Nidum-Llama-3.2-3B-Uncensored  
+
+### Welcome to Nidum!  
+At Nidum, we believe in pushing the boundaries of innovation by providing advanced and unrestricted AI models for every application. Dive into our world of possibilities and experience the freedom of **Nidum-Llama-3.2-3B-Uncensored**, tailored to meet diverse needs with exceptional performance.
+
+---
+
+[![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. **Uncensored Responses**: Capable of addressing any query without content restrictions, offering detailed and uninhibited answers.
+2. **Versatility**: Excels in diverse use cases, from complex technical queries to engaging casual conversations.
+3. **Advanced Contextual Understanding**: Draws from an expansive knowledge base for accurate and context-aware outputs.
+4. **Extended Context Handling**: Optimized for handling long-context interactions for improved continuity and depth.
+5. **Customizability**: Adaptable to specific tasks and user preferences through fine-tuning.
+
+---
+
+### Use Cases
+
+- **Open-Ended Q&A**  
+- **Creative Writing and Ideation**  
+- **Research Assistance**  
+- **Educational Queries**  
+- **Casual Conversations**  
+- **Mathematical Problem Solving**  
+- **Long-Context Dialogues**  
+
+---
+
+### How to Use
+
+To start using **Nidum-Llama-3.2-3B-Uncensored**, follow the sample code below:
+
+```python
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation",
+    model="nidum/Nidum-Llama-3.2-3B-Uncensored",
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda",  # replace with "mps" to run on a Mac device
+)
+
+messages = [
+    {"role": "user", "content": "Tell me something fascinating."},
+]
+
+outputs = pipe(messages, max_new_tokens=256)
+assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
+print(assistant_response)
+```
+
+---
+#### Quantized Models Available for Download
+
+| **Quantized Model Version**                                                                                       | **Description**                                                                 |
+|-------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
+| [**Nidum-Llama-3.2-3B-Uncensored-F16.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/Nidum-Llama-3.2-3B-Uncensored-F16.gguf) | Full 16-bit floating point precision for maximum accuracy on high-end GPUs.     |
+| [**model-Q2_K.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q2_K.gguf)               | Optimized for minimal memory usage with lower precision, suitable for edge cases.|
+| [**model-Q3_K_L.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q3_K_L.gguf)           | Balanced precision with enhanced memory efficiency for medium-range devices.    |
+| [**model-Q3_K_M.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q3_K_M.gguf)           | Mid-range quantization for moderate precision and memory usage balance.         |
+| [**model-Q3_K_S.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q3_K_S.gguf)           | Smaller quantization steps, offering moderate precision with reduced memory use.|
+| [**model-Q4_0_4_4.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q4_0_4_4.gguf)       | Performance-optimized for low memory, ideal for lightweight deployment.         |
+| [**model-Q4_0_4_8.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q4_0_4_8.gguf)       | Extended quantization balancing memory use and inference speed.                 |
+| [**model-Q4_0_8_8.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q4_0_8_8.gguf)       | Advanced memory precision targeting larger contexts.                            |
+| [**model-Q4_K_M.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q4_K_M.gguf)           | High-efficiency quantization for moderate GPU resources.                        |
+| [**model-Q4_K_S.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q4_K_S.gguf)           | Optimized for smaller-scale operations with compact memory footprint.           |
+| [**model-Q5_K_M.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q5_K_M.gguf)           | Balances performance and precision, ideal for robust inferencing environments.  |
+| [**model-Q5_K_S.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q5_K_S.gguf)           | Moderate quantization targeting performance with minimal resource usage.        |
+| [**model-Q6_K.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-Q6_K.gguf)               | High-precision quantization for accurate and stable inferencing tasks.          |
+| [**model-TQ1_0.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-TQ1_0.gguf)             | Experimental quantization for targeted applications in test environments.       |
+| [**model-TQ2_0.gguf**](https://huggingface.co/nidum/Nidum-Llama-3.2-3B-Uncensored-GGUF/blob/main/model-TQ2_0.gguf)             | High-performance tuning for experimental use cases and flexible precision.      |
+
+---
+### Datasets and Fine-Tuning
+
+The following fine-tuning datasets are leveraged to enhance specific model capabilities:
+
+- **Uncensored Data**: Enables unrestricted and uninhibited responses.
+- **RAG-Based Fine-Tuning**: Optimizes retrieval-augmented generation for knowledge-intensive tasks.
+- **Long Context Fine-Tuning**: Enhances the model's ability to process and maintain coherence in extended conversations.
+- **Math-Instruct Data**: Specially curated for precise and contextually accurate mathematical reasoning.
+
+---
+
+### Benchmarks  
+
+After fine-tuning with **uncensored data**, **Nidum-Llama-3.2-3B** demonstrates **superior performance compared to the original LLaMA model**, particularly in accuracy and handling diverse, unrestricted scenarios.
+
+#### Benchmark Summary Table
+
+| **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. **GPQA Results**: Fine-tuning on uncensored data has boosted **Nidum 3B's Exact Match and Accuracy**, particularly excelling in **generative** and **zero-shot** tasks involving domain-specific knowledge.
+2. **HellaSwag Results**: **Nidum 3B** consistently outperforms **LLaMA 3B** in **common sense reasoning benchmarks**, indicating enhanced contextual and semantic understanding.
+
+---
+
+### Contributing
+
+We welcome contributions to improve and extend the model’s capabilities. Stay tuned for updates on how to contribute.
+
+---
+
+### Contact
+
+For inquiries, collaborations, or further information, please reach out to us at **info@nidum.ai**.
+
+---
+
+### Explore the Possibilities
+
+Dive into unrestricted creativity and innovation with **Nidum-Llama-3.2-3B-Uncensored**!