2025-01-16 01:19:02
Large language models (LLMs) have revolutionized how we interact with technology. While many LLMs are censored for safety and ethical reasons, uncensored LLMs offer more freedom. Ollama, an open-source platform, allows users to run these models locally. This post explores the world of uncensored LLMs on Ollama, examining their capabilities, limitations, and potential benefits and risks.
Ollama offers a variety of uncensored LLMs, each with unique characteristics. Here are some notable ones:
Ollama also offers other uncensored LLMs, providing users with diverse options .
Uncensored LLMs have unique capabilities but also limitations:
It's important to recognize the trade-off between freedom of expression and responsible AI use .
Here's a comparison of some key models on Ollama:
Model | Size (Parameters) | Capabilities | Intended Use Cases |
---|---|---|---|
Llama 2 Uncensored | 7B, 70B | General purpose | Content creation, code generation, research |
WizardLM Uncensored | 13B | General purpose | Content creation, research, dialogue generation |
Wizard Vicuna Uncensored | 7B, 13B, 30B | General purpose | Content creation, code generation, research |
Dolphin-Mistral | 7B | Deeper conversations | Exploring complex ideas, addressing sensitive issues |
Dolphincoder | 8x7B | Code generation | Software development, code completion |
Nous Hermes Llama 2 13B | 13B | Long responses | Instruction following, question answering |
Consider factors like model size, capabilities, and intended use cases when choosing a model .
Uncensored LLMs can be used for:
Benefits:
Risks:
The availability of uncensored LLMs raises questions about acceptable content and highlights the need for ethical considerations .
Uncensored LLMs on Ollama offer a unique opportunity to explore language models without censorship. Use them responsibly and be aware of their limitations and risks. Mitigate risks by critically evaluating content, verifying information, and using the models ethically.
The future of uncensored LLMs involves discussions about AI safety, ethics, and societal impact. Finding a balance between innovation and responsible development is crucial. By understanding the capabilities, limitations, and ethical considerations, users can leverage these tools effectively and contribute to responsible AI development.
https://ollama.ai/library/llama2-uncensored https://ollama.ai/library/wizardlm-uncensored https://ollama.ai/library/wizard-vicuna-uncensored https://ollama.ai/library/dolphin-mistral https://ollama.ai/library/dolphincoder https://ollama.ai/library/nous-hermes-llama2-13b https://ollama.ai/library https://bdtechtalks.com/2023/07/17/uncensored-large-language-models/ https://venturebeat.com/ai/why-researchers-are-building-uncensored-large-language-models/ https://www.theguardian.com/technology/2023/jul/21/ai-chatbots-uncensored-language-models-danger https://huggingface.co/TheBloke/Llama-2-7B-Uncensored-GGML https://github.com/facebookresearch/llama https://www.timeshighereducation.com/campus/how-prepare-students-age-generative-ai https://ollama.ai/ https://www.fastcompany.com/90884902/meta-is-releasing-its-powerful-llama-2-ai-language-model-for-free https://www.bleepingcomputer.com/news/security/researchers-find-ways-to-jailbreak-metas-llama-2-large-language-model/ https://arstechnica.com/information-technology/2023/07/meta-releases-open-source-ai-model-llama-2-that-can-rival-googles-and-openais/
2025-01-16 01:02:00
Tired of manually formatting every heading, paragraph, and quote in your Word documents? Microsoft Word's Styles feature can be a huge time-saver! Styles allow you to apply a set of formatting options with a single click, ensuring consistency and making your documents look polished and professional. This blog post will guide you through the essentials of using Styles.
Think of Styles as pre-defined formatting templates. They contain a combination of formatting characteristics like font type, size, color, spacing, and more. Instead of manually adjusting each of these elements every time, you simply apply a Style, and Word takes care of the rest.
Word comes with a variety of built-in Styles. You can find them on the Home tab in the Styles group.
To apply a Style, simply select the text you want to format and click on the desired Style in the Styles group.
While built-in Styles are a great starting point, you can create your own custom Styles to match your specific needs.
Your new Style will now appear in the Styles group, ready to be used!
You can also modify existing Styles, including the built-in ones:
Mastering Styles in Word is a key step towards creating professional-looking documents with ease. By utilizing built-in Styles, creating custom Styles, and modifying existing ones, you can take control of your document formatting and significantly improve your workflow.
2025-01-16 00:25:12
Want to know how your website content is performing in terms of ad revenue? Linking Google Analytics with Google AdSense is the answer! This connection provides valuable insights into user behavior and helps optimize your monetization strategy.
Before you start, ensure you have:
Linking Google Analytics and AdSense offers several benefits:
By linking these powerful tools, you gain valuable data that can significantly improve your website's monetization strategy.
2025-01-16 00:13:20
ComfyUI is a powerful and modular GUI and backend for working with diffusion models, particularly Stable Diffusion. It offers a node-based interface for creating complex image generation workflows without coding. This guide will show you how to set up and run ComfyUI on Google Colab.
First, we need to set up the environment on Google Colab. You can choose to use Google Drive for storage or just rely on Colab's temporary storage.
If you want to save your models and outputs persistently, you can mount your Google Drive. This is useful for larger models or if you want to access your data later.
# Set USE_GOOGLE_DRIVE to True
USE_GOOGLE_DRIVE = True
if USE_GOOGLE_DRIVE:
print("Mounting Google Drive...")
from google.colab import drive
drive.mount('/content/drive')
WORKSPACE = "/content/drive/MyDrive/ComfyUI"
%cd /content/drive/MyDrive
else:
WORKSPACE = 'ComfyUI'
Now, clone the ComfyUI repository from GitHub.
![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI
%cd $WORKSPACE
If you want to ensure you have the latest version, pull the latest changes.
# Set UPDATE_COMFY_UI to True
UPDATE_COMFY_UI = True
if UPDATE_COMFY_UI:
print("-= Updating ComfyUI =-")
!git pull
Install the required Python packages.
print("-= Install dependencies =-")
!pip install -r requirements.txt
Note: Colab may have certain torch versions pre-installed. You can see these versions by running !pip list | grep torch
. When installing requirements, you may need to use the --extra-index-url
flag to specify a torch version compatible with your system.
For example:
!pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117
You'll need to download some models, checkpoints, and other resources to get started. Here's how to download a basic set of models, including Stable Diffusion 1.5:
!wget -c https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors -P ./models/checkpoints/
!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/
You can uncomment other commands in the provided Jupyter notebook to download additional models like SDXL, ControlNet, etc.
There are several ways to run ComfyUI on Colab. We recommend using cloudflared
for the best experience.
Cloudflared creates a secure tunnel to your Colab instance, allowing you to access the ComfyUI web interface easily.
Install Cloudflared:
!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb
!dpkg -i cloudflared-linux-amd64.deb
Run ComfyUI with Cloudflared:
import subprocess
import threading
import time
import socket
import urllib.request
def iframe_thread(port):
while True:
time.sleep(0.5)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
result = sock.connect_ex(('127.0.0.1', port))
if result == 0:
break
sock.close()
print("\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\n")
p = subprocess.Popen(["cloudflared", "tunnel", "--url", "http://127.0.0.1:{}".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
for line in p.stderr:
l = line.decode()
if "trycloudflare.com " in l:
print("This is the URL to access ComfyUI:", l[l.find("http"):], end='')
#print(l, end='')
threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()
!python main.py --dont-print-server
This will output a trycloudflare.com
URL that you can use to access the ComfyUI interface in your browser.
The Jupyter notebook also provides instructions for running ComfyUI with localtunnel
or using Colab's iframe. However, cloudflared
is generally the most reliable method.
That's it! You've successfully set up and deployed ComfyUI on Google Colab. You can now start experimenting with different models and creating your own unique image generation workflows. Remember to consult the ComfyUI Examples page for inspiration and guidance on using the various features of ComfyUI.
2025-01-15 23:28:35
ComfyUI is a powerful and modular GUI for working with diffusion models, offering a node-based interface for creating complex workflows. If you've been using AUTOMATIC1111's Stable Diffusion web UI (A1111) and have a collection of models, you might be wondering if you can use them in ComfyUI without downloading them again. The good news is, yes you can!
ComfyUI provides a straightforward way to share models with other diffusion UIs like A1111 through its configuration file.
extra_model_paths.yaml
FileComfyUI uses a configuration file named extra_model_paths.yaml
to define search paths for various model types. This file allows you to specify directories where ComfyUI should look for models, including those already used by A1111.
Locate the File:
extra_model_paths.yaml.example
in the main ComfyUI
directory.Rename the File: Rename extra_model_paths.yaml.example
to extra_model_paths.yaml
.
Edit the File: Open extra_model_paths.yaml
with a text editor.
Configure Paths: The file contains example configurations for different model types (checkpoints, VAEs, LoRAs, etc.). You need to modify these paths to point to the corresponding directories within your A1111 installation.
For example, if your A1111 checkpoints are located in stable-diffusion-webui/models/Stable-diffusion
, your configuration might look like this:
a1111:
base_path: /path/to/your/stable-diffusion-webui # Replace with the actual path
checkpoints: models/Stable-diffusion
configs: models/Stable-diffusion
vae: models/VAE
loras: |
models/Lora
models/LyCORIS
upscale_models: |
models/ESRGAN
models/SwinIR
embeddings: embeddings
hypernetworks: models/hypernetworks
controlnet: models/ControlNet
Important:
/path/to/your/stable-diffusion-webui
with the actual path to your A1111 installation directory.models/Stable-diffusion
, models/VAE
) according to your A1111 directory structure.|
symbol is used for multi-line strings in YAML, allowing you to specify multiple directories for a single model type.Save and Restart: Save the changes to extra_model_paths.yaml
and restart ComfyUI.
After restarting, ComfyUI will now be able to load models from the directories you specified, including those from your A1111 installation. This means you can seamlessly use your existing model collection in ComfyUI without any redundant downloads or storage usage. Enjoy experimenting with your A1111 models in the flexible environment of ComfyUI!
2025-01-15 23:22:28
ComfyUI is a powerful and modular GUI that allows you to design and execute advanced stable diffusion pipelines using a graph-based interface. This blog post will guide you through the process of installing ComfyUI on a Windows system with an AMD GPU.
Before we begin, make sure you have the following:
There are two primary methods for installing ComfyUI on Windows with an AMD GPU:
This is the simplest method, especially if you're new to ComfyUI or prefer a quick setup.
ComfyUI_windows_portable_nvidia.7z
). Even though the file name mentions Nvidia, it will still work for AMD GPUs if you follow the DirectML instructions below.ComfyUI
folder and run the following command:
bash
pip install torch-directml
bash
python main.py --directml
.ckpt
or .safetensors
files) in the ComfyUI\models\checkpoints
directory.That's it! ComfyUI should now be running. You can access the interface through your web browser, typically at http://127.0.0.1:8188
.
This method gives you more control over the installation process but requires a few more steps.
bash
git clone https://github.com/comfyanonymous/ComfyUI.git
bash
cd ComfyUI
bash
pip install -r requirements.txt
bash
pip install torch-directml
models/checkpoints
and your VAE models in models/vae
.bash
python main.py --directml
Note for Specific AMD GPUs:
HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py --directml
HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py --directml
If you have another Stable Diffusion UI installed (like Automatic1111) and want to share models with ComfyUI to save disk space, you can modify the model search paths:
extra_model_paths.yaml.example
file in the ComfyUI directory and rename it to extra_model_paths.yaml
.extra_model_paths.yaml
file with a text editor.You have now successfully installed ComfyUI on your Windows system with an AMD GPU. You can start exploring the powerful features of ComfyUI and create complex Stable Diffusion workflows. For examples and inspiration, visit the ComfyUI Examples page. Remember that the ComfyUI community is active and helpful, so don't hesitate to seek support on the Matrix space or Comfy.org if you encounter any issues. Happy creating!