147 lines
8.9 KiB
Markdown
147 lines
8.9 KiB
Markdown
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---
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license: apache-2.0
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base_model:
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- nidum/Nidum-Llama-3.2-3B-Uncensored
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- meta-llama/Llama-3.2-3B
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library_name: adapter-transformers
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tags:
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- chemistry
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- biology
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- legal
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- code
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- medical
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- finance
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- roleplay
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- uncensored
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- uncensored LLM
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pipeline_tag: text-generation
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---
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### Nidum-Llama-3.2-3B-Uncensored
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### Welcome to Nidum!
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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.
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---
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[](https://github.com/NidumAI-Inc)
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**Explore Nidum's Open-Source Projects on GitHub**: [https://github.com/NidumAI-Inc](https://github.com/NidumAI-Inc)
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---
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### Key Features
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1. **Uncensored Responses**: Capable of addressing any query without content restrictions, offering detailed and uninhibited answers.
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2. **Versatility**: Excels in diverse use cases, from complex technical queries to engaging casual conversations.
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3. **Advanced Contextual Understanding**: Draws from an expansive knowledge base for accurate and context-aware outputs.
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4. **Extended Context Handling**: Optimized for handling long-context interactions for improved continuity and depth.
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5. **Customizability**: Adaptable to specific tasks and user preferences through fine-tuning.
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---
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### Use Cases
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- **Open-Ended Q&A**
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- **Creative Writing and Ideation**
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- **Research Assistance**
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- **Educational Queries**
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- **Casual Conversations**
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- **Mathematical Problem Solving**
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- **Long-Context Dialogues**
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---
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### How to Use
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To start using **Nidum-Llama-3.2-3B-Uncensored**, follow the sample code below:
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```python
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import torch
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from transformers import pipeline
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pipe = pipeline(
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"text-generation",
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model="nidum/Nidum-Llama-3.2-3B-Uncensored",
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model_kwargs={"torch_dtype": torch.bfloat16},
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device="cuda", # replace with "mps" to run on a Mac device
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)
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messages = [
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{"role": "user", "content": "Tell me something fascinating."},
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]
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outputs = pipe(messages, max_new_tokens=256)
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assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
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print(assistant_response)
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```
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---
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#### Quantized Models Available for Download
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| **Quantized Model Version** | **Description** |
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|-------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
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| [**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. |
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| [**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.|
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| [**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. |
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| [**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. |
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| [**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.|
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| [**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. |
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| [**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. |
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| [**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. |
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| [**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. |
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| [**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. |
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| [**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. |
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| [**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. |
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| [**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. |
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| [**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. |
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| [**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. |
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---
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### Datasets and Fine-Tuning
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The following fine-tuning datasets are leveraged to enhance specific model capabilities:
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- **Uncensored Data**: Enables unrestricted and uninhibited responses.
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- **RAG-Based Fine-Tuning**: Optimizes retrieval-augmented generation for knowledge-intensive tasks.
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- **Long Context Fine-Tuning**: Enhances the model's ability to process and maintain coherence in extended conversations.
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- **Math-Instruct Data**: Specially curated for precise and contextually accurate mathematical reasoning.
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---
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### Benchmarks
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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.
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#### Benchmark Summary Table
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| **Benchmark** | **Metric** | **LLaMA 3.2 3B** | **Nidum 3.2 3B** | **Observation** |
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|-------------------|-----------------------------------|--------------|--------------|-----------------------------------------------------------------------------------------------------|
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| **GPQA** | Exact Match (Flexible) | 0.3 | 0.5 | Nidum 3B demonstrates significant improvement, particularly in **generative tasks**. |
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| | Accuracy | 0.4 | 0.5 | Consistent improvement, especially in **zero-shot** scenarios. |
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| **HellaSwag** | Accuracy | 0.3 | 0.4 | Better performance in **common sense reasoning** tasks. |
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| | Normalized Accuracy | 0.3 | 0.4 | Enhanced ability to understand and predict context in sentence completion. |
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| | Normalized Accuracy (Stderr) | 0.15275 | 0.1633 | Slightly improved consistency in normalized accuracy. |
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| | Accuracy (Stderr) | 0.15275 | 0.1633 | Shows robustness in reasoning accuracy compared to LLaMA 3B. |
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---
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### Insights:
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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.
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2. **HellaSwag Results**: **Nidum 3B** consistently outperforms **LLaMA 3B** in **common sense reasoning benchmarks**, indicating enhanced contextual and semantic understanding.
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---
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### Contributing
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We welcome contributions to improve and extend the model’s capabilities. Stay tuned for updates on how to contribute.
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---
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### Contact
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For inquiries, collaborations, or further information, please reach out to us at **info@nidum.ai**.
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---
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### Explore the Possibilities
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Dive into unrestricted creativity and innovation with **Nidum Llama 3.2 3B Uncensored**!
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