I’ve been diving deep into the world of Large Language Models (LLMs) like ChatGPT, Gemini, Claude, and LLAMA. But recently, I stumbled upon something that completely blew my mind: uncensored LLMs! 🤯
As someone who loves pushing the boundaries of AI and exploring new frontiers, I couldn’t resist the temptation to try out an uncensored LLM for myself. And let me tell you, the experience was nothing short of mind-blowing! 🎆 After setting up and running an uncensored LLM locally for the first time, I was amazed by the raw, unfiltered outputs it generated. It gave me a whole new perspective on the potential of such LLMs and why having an uncensored variant is so important for certain perspectives and society in general.
In this blog post, I’ll be sharing my journey with uncensored LLMs, diving into the nitty-gritty details of what they are, how they differ from regular LLMs, and why they exist. I’ll also be sharing my hands-on experience with setting up and running an uncensored LLM locally, so you can try it out for yourself! 💻
🤖 Introduction: Uncensored LLM vs Regular LLM
Large Language Models (LLMs) are AI systems trained on vast amounts of text data to understand and generate human-like text based on input prompts. There are two main types of LLMs: regular and uncensored.
Regular LLMs, such as those created by major organizations like OpenAI, Anthropic, Google, etc. are designed with specific safety and ethical guidelines, often reflecting societal norms and legal standards. These models avoid generating harmful or inappropriate content. (Click on each link to read their AI Principles)
Uncensored LLMs, on the other hand, are models that do not have these built-in restrictions. They are designed to generate outputs based on the input without ethical filtering, which can be useful for certain applications but also pose risks.
📊 Table of Comparison
| Feature | Regular LLM | Uncensored LLM |
|---|---|---|
| Content Filtering | Yes (aligned to avoid harmful content) | No (generates responses as is) |
| Use Cases | General purpose, safer for public use | Specialized tasks needing raw output |
| Cultural Alignment | Often aligned with Western norms | No specific alignment |
| Risk of Harmful Output | Lower | Higher |
| Flexibility | Restricted by ethical guidelines | Higher flexibility in responses |
🐬 What is the Dolphin 2.9 Latest Model?
🐬Dolphin 2.9 is a project by Eric Hartford @ Cognitive Computations aimed at creating an open-source, uncensored, and commercially licensed dataset and series of instruct-tuned language models. This initiative is based on Microsoft’s Orca paper and seeks to provide a foundation for building customized models without the typical content restrictions found in conventional LLMs. The model uses a dataset that removes biases, alignment, or any form of censorship, aiming to create a purely instructional tool that can be layered with user-specific alignments.
🐬 The Dolphin 2.9 Dataset
Following are the details of the dataset used to train the Dolphin Model: (Note the base model is Llama-3-8b)
| Dataset Details | Links |
|---|---|
| cognitivecomputations/dolphin – This dataset is an attempt to replicate the results of Microsoft’s Orca | cognitivecomputations/dolphin · Datasets at Hugging Face |
| HuggingFaceH4/ultrachat_200k – | HuggingFaceH4/ultrachat_200k · Datasets at Hugging Face |
| teknium/OpenHermes-2.5 – This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models. | teknium/OpenHermes-2.5 · Datasets at Hugging Face |
| microsoft/orca-math-word-problems-200k – This dataset contains ~200K grade school math word problems. | microsoft/orca-math-word-problems-200k · Datasets at Hugging Face |
💻 How to Run the Model Locally Using LMStudio
To run Dolphin or any similar uncensored model locally, you typically need to follow these steps, assuming you are using a system like LMStudio for managing your AI models:
- Setup Your Environment:
- Install LMStudio software its available on (Windows, Mac & Linux). This software allows you to manage and deploy local LLMs without you having to setup Python, Machine Learning, Transformers etc. libraries.
- Link to Download the Windows Bits – LM Studio – Discover, download, and run local LLMs
- My laptop config has 11th Gen Intel processor, 64 GB RAM & Nvdia RTX 3080 8 GB VRAM, 3 TB Storage.
- Download the Model and Dependencies:
- The best space to keep a track on models is Hugging Face – Models – Hugging Face. You can keep a track of the model releases and updates here.
- Copy the model name from Hugging Face – cognitivecomputations/dolphin-2.9-llama3-8b
- Paste this name in LM Studio and it will list out all the quantized models
- In my case due to the configurations I selected 8Bit model. Please note lower the quantized version less accurate the model is.
- Download of the model will take time depending upon your internet connection.
- Prepare the Model for Running:
- Within LMStudio click on the Chat interface to configure model settings.
- Select the model from the drop down list – dolphin 2.9 llama3
- You will be able to run it stock but I like to configure the Advanced Configurations
- Based on your system set the GPU to 50/50 or max. I have setup for max
- Click Relod model to apply configuration
- Run the Model:
- Use LMStudio to load and run the model.
- Within the User Prompt enter what you want to ask the Dolphin model
- Monitor the model’s performance and adjust settings as needed.
- Testing and Usage:
- Once the model is running, you can begin to input prompts and receive outputs.
- Test the model with various inputs to ensure it functions as expected and adjust configurations as needed.
- Note below was a test fun prompt across ChatGPT, Claude & Dolphin. You can clearly see the winner being Dolphin 🤗
- Eject and Closing the model:
- Once you done with the session you can select Eject Model
- This will release the VRAM/RAM and CPU utlization back to normal
💻 Quantized & GGUF Model
As home systems usually wont have the necessary GPU to run LLM models natively on consumer grade hardware. A quantized model is a compressed version of a neural network where the weights and activations are represented with lower-precision data types, such as int8 or uint8, instead of the typical float32. This reduces the model’s size and computational requirements while maintaining acceptable performance.
GGUF stands for “General-Purpose, GPU-Free”. It refers to a type of large language model that is designed to be versatile and capable of performing a wide range of natural language processing tasks without requiring expensive GPU hardware for inference.
The Dolphin 2.9 GGUF models are:
| Model Name | Quantization | Model Size | CPU | GPU | VRAM | RAM |
|---|---|---|---|---|---|---|
| dolphin-2.9-llama3-8b-q3_K_M.gguf | 3-bit (q3) | 4.02 GB | Compatible with most CPUs | Not required for inference | Not required for inference | ~4.02 GB |
| dolphin-2.9-llama3-8b-q4_K_M.gguf | 4-bit (q4) | 4.92 GB | Compatible with most CPUs | Not required for inference | Not required for inference | ~4.92 GB |
| dolphin-2.9-llama3-8b-q5_K_M.gguf | 5-bit (q5) | 5.73 GB | Compatible with most CPUs | Not required for inference | Not required for inference | ~5.73 GB |
| dolphin-2.9-llama3-8b-q6_K.gguf | 6-bit (q6) | 6.6 GB | Compatible with most CPUs | Not required for inference | Not required for inference | ~6.6 GB |
| dolphin-2.9-llama3-8b-q8_0.gguf | 8-bit (q8) | 8.54 GB | Compatible with most CPUs | Not required for inference | Not required for inference | ~8.54 GB |
Reference Links
Following are the list of helpful links:
| Description | Link |
|---|---|
| Details and background about the Dolphin Model | Dolphin 🐬 (erichartford.com) |
| What are uncensored models? | Uncensored Models (erichartford.com) |
| Various Dolphin Models on various base LLMs | cognitivecomputations (Cognitive Computations) (huggingface.co) |
| Dolphin Llama 3 8B GGUF model I used on LMStudio | cognitivecomputations/dolphin-2.9-llama3-8b-gguf · Hugging Face |
| LM Studio | LM Studio – Discover, download, and run local LLMs |
| Model Memory Estimator Utility | Model Memory Utility – a Hugging Face Space by hf-accelerate |
By following these steps, you can deploy and utilize an uncensored LLM like Dolphin 2.9 for research, development, or any specialized application where conventional content restrictions are not desirable. I hope you’ll find this insightful on your joruney of LLMs. Please let me know if I’ve missed any steps or details, and I’ll be happy to update the post.
Thanks,
Aresh Sarkari
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