LoLLMs

Hello, dear readers!

Today, we stand on the cusp of a groundbreaking development in the realm of AI language models. The much-anticipated GPT-4-O-Mini has arrived on Lollms, and its affordability is nothing short of astonishing. Let’s delve into the details and explore how this remarkable model is transforming the landscape of AI-generated content.

The Price of Possibility

Imagine generating 10 million tokens for just $6. Yes, you read that correctly! In comparison, sending the same volume to OpenAI would only set you back $1.5. The implications of such cost-effectiveness for developers, writers, and content creators are profound. With the power of GPT-4-O-Mini at your fingertips, the possibilities for expansive, creative output become truly limitless.

A Closer Look at Context Size and Generation

The GPT-4-O-Mini boasts some impressive specifications that are essential for anyone considering its use. Here are a few key features:

1. Context Size: Just like its predecessor, GPT-4-O, the new model supports an astounding 128,000 tokens of context. This means it can retain a vast amount of information, making it ideal for complex projects that require nuanced understanding.
2. Enhanced Generation Capability: While GPT-4-O is limited to 4,096 tokens of generation at a time, the GPT-4-O-Mini takes a leap forward with the ability to generate up to 16,000 tokens in one go. To put this in perspective, that’s roughly 25 pages of text on an A4 sheet—a significant boon for anyone looking to produce extensive written works without interruption.

Navigating the Settings

For those looking to harness the full potential of the GPT-4-O-Mini on Lollms, two crucial settings come into play:

• ctx_size: This parameter determines the full context size of the model, allowing you to maximize the information retained throughout your interactions.
• max_n_predict: This setting defines the generation capability of the model. Depending on your needs, you can choose between the standard 4,096 tokens or the expanded 16,000 tokens for richer content generation.

And fear not, should you find yourself running low on context tokens. Simply press the continue button, and you’ll be instantly recharged with another 4,096 or 16,000 tokens to keep your creativity flowing.

A Step Closer to Completion

Code Builder, this new model feels like the final piece of the puzzle. The combination of affordability, extensive context size, and enhanced generation capabilities makes it an invaluable tool for developers and creatives alike.

Unveiling the Magic of GPT-4-O-Mini: A New Era of Affordable AI

Hello, dear readers!

Today, we stand on the cusp of a groundbreaking development in the realm of AI language models. The much-anticipated GPT-4-O-Mini has arrived on Lollms, and its affordability is nothing short of astonishing. Let’s delve into the details and explore how this remarkable model is transforming the landscape of AI-generated content.

The Price of Possibility

Imagine generating 10 million tokens for just $6. Yes, you read that correctly! In comparison, sending the same volume to OpenAI would only set you back $1.5. The implications of such cost-effectiveness for developers, writers, and content creators are profound. With the power of GPT-4-O-Mini at your fingertips, the possibilities for expansive, creative output become truly limitless.

A Closer Look at Context Size and Generation

The GPT-4-O-Mini boasts some impressive specifications that are essential for anyone considering its use. Here are a few key features:

1. Context Size: Just like its predecessor, GPT-4-O, the new model supports an astounding 128,000 tokens of context. This means it can retain a vast amount of information, making it ideal for complex projects that require nuanced understanding.
2. Enhanced Generation Capability: While GPT-4-O is limited to 4,096 tokens of generation at a time, the GPT-4-O-Mini takes a leap forward with the ability to generate up to 16,000 tokens in one go. To put this in perspective, that’s roughly 25 pages of text on an A4 sheet—a significant boon for anyone looking to produce extensive written works without interruption.

Navigating the Settings

For those looking to harness the full potential of the GPT-4-O-Mini on Lollms, two crucial settings come into play:

• ctx_size: This parameter determines the full context size of the model, allowing you to maximize the information retained throughout your interactions.
• max_n_predict: This setting defines the generation capability of the model. Depending on your needs, you can choose between the standard 4,096 tokens or the expanded 16,000 tokens for richer content generation.

And fear not, should you find yourself running low on context tokens. Simply press the continue button, and you’ll be instantly recharged with another 4,096 or 16,000 tokens to keep your creativity flowing.

A Step Closer to Completion

As the sole architect behind Code Builder, I am thrilled to share that the advancements brought by the GPT-4-O-Mini will significantly propel my project forward. With its enhanced capabilities, I can now harness this powerful model as the foundation for Code Builder, allowing me to tackle entire projects with unprecedented efficiency and creativity.

In conclusion, the GPT-4-O-Mini on Lollms is not just an upgrade; it’s a revolution in accessible AI technology. Whether you’re crafting a novel, developing software, or generating content for your blog, this model promises to be a game-changer. So, why wait? Dive into the world of GPT-4-O-Mini and experience the magic for yourself!

Happy writing!

A little more than a year ago, I penned an article on AI ethics, which was the inaugural piece for the GPT4All-webui repository, the precursor to lollms. In that article, I explored potential futures with AI, both utopian and dystopian, without passing judgment. Fast forward to today, and the rapid advancements in AI have prompted me to revisit these issues with renewed curiosity.

Last week, I had an epiphany while observing two girls on the tramway. One said, “You know, the homework we had, I did it using ChatGPT.” The other replied, “Same for me. Why bother doing things ourselves when we have a tool like that?” This seemingly mundane exchange struck me like a philosophical lightning bolt. Are we, as humans, inherently lazy? Or are we simply pragmatic, always seeking the path of least resistance?

One could argue that this behavior is akin to our reliance on calculators. Calculators have undoubtedly made us more efficient, but they’ve also eroded certain skills. A few generations ago, people excelled at mental arithmetic. Today, while a human with a calculator is formidable, a human without one often feels lost. Technology augments us, but it also diminishes us in subtle ways.

While the reliance on calculators might seem trivial, the stakes are exponentially higher when it comes to outsourcing our thinking to AI. Imagine a world where AI handles all our cognitive tasks. This could enable us to achieve feats previously deemed impossible. However, if we suddenly remove that AI, we might find ourselves more helpless than ever before. At least animals think and have evolved to react to their environment. In nature, it’s a case of “use it or lose it.” If something is not useful, it degrades. If AI starts to think for us, then as Agent Smith from “The Matrix” ominously noted, “It is no longer our civilization, but theirs.”

Consider the history of humanity. Formal schooling is a relatively recent development. In the past, most people didn’t attend school; they learned through apprenticeships and acquired only the skills necessary for work and life, primarily manual skills. Only a privileged few had access to a broader education in philosophy, mathematics, literature, and the like.

We have since evolved into a society where literacy is widespread, and we are exposed to a vast array of knowledge. However, with the advent of advanced AI, we risk reverting to a state where learning becomes unnecessary because AI is becoming increasingly intelligent and may soon surpass the IQ of most humans. This raises a critical question: If AI can perform most tasks better than humans, what is left for us to do?

The ethical and societal implications of this shift are profound. As AI continues to evolve, it will likely take over many tasks that humans currently perform, from routine jobs to complex problem-solving. This could lead to widespread unemployment and economic disparity if not managed properly. Moreover, the reliance on AI for decision-making could erode our critical thinking skills and make us overly dependent on machines.

One area that will be significantly impacted is education. If students use AI to complete their homework, they may not develop the necessary skills to think critically and solve problems independently. This could create a generation of individuals who are adept at using technology but lack the fundamental skills to innovate and adapt in the absence of AI.

On the flip side, AI has the potential to significantly enhance human capabilities. By automating mundane tasks, AI can free up time for humans to focus on more creative and meaningful pursuits. It can also assist in areas like healthcare, where AI can analyze vast amounts of data to provide insights that humans might miss. The key is to find a balance where AI acts as a tool to augment human abilities rather than replace them.

To further illustrate the potential consequences of over-reliance on technology, let’s look at examples from the movies “Idiocracy,” “Wall-E,” and “The Matrix.”

In “Idiocracy,” society has devolved into a state of extreme intellectual laziness and incompetence. People rely heavily on automated systems and have lost the ability to think critically or solve basic problems. This dystopian future serves as a cautionary tale about the dangers of neglecting education and critical thinking in favor of convenience and entertainment. The film portrays a world where the pursuit of ease has led to a collective intellectual decline, raising the question: Are we sacrificing our cognitive abilities for the sake of comfort?

Similarly, in “Wall-E,” humans have become entirely dependent on technology, living in a spaceship where robots cater to their every need. As a result, they have become physically and mentally inactive, losing their ability to engage with the world around them. The film highlights the importance of maintaining a balance between technological convenience and human engagement with the environment. It prompts us to ask: What happens to our humanity when we no longer need to strive, struggle, or even move?

“The Matrix” offers a more complex and layered exploration of these themes. In the film, humans are unknowingly trapped in a simulated reality created by intelligent machines. This raises profound questions about the nature of reality and the essence of human existence. Agent Smith’s words resonate deeply with our current predicament:

“Did you know that the first Matrix was designed to be a perfect human world? Where none suffered, where everyone would be happy. It was a disaster. No one would accept the program. Entire crops were lost. Some believed we lacked the programming language to describe your perfect world. But I believe that, as a species, human beings define their reality through suffering and misery. The perfect world was a dream that your primitive cerebrum kept trying to wake up from. Which is why the Matrix was redesigned to this: the peak of your civilization. I say ‘your civilization’ because as soon as we started thinking for you, it really became our civilization, which is, of course, what this is all about. Evolution, Morpheus. Evolution. Like the dinosaur. Look out that window. You had your time. The future is our world, Morpheus. The future is our time.”

Agent Smith’s words underscore a fundamental truth about human nature: our growth and development are often driven by challenges and adversity. If AI begins to think for us, we risk losing our civilization to the very tools we created. This raises profound questions about the essence of being human. Is it our ability to think, adapt, and innovate that defines us? Or is it our capacity to experience and overcome suffering that shapes our reality?

However, there is hope. The vision of humanity’s future as depicted in “Star Trek” offers an inspiring counterpoint. In the “Star Trek” universe, humanity has transcended the limitations of its past. Technology is not a crutch but a catalyst for exploration, curiosity, and self-improvement. Money and wealth have become irrelevant, and people are driven by a desire to better themselves and contribute to the greater good. Freed from the burdens of mundane work, they seek more noble objectives, such as exploring the cosmos, understanding new cultures, and pushing the boundaries of knowledge.

This optimistic vision reminds us that technology, including AI, can be a powerful tool for human advancement if used wisely. It can free us from repetitive and menial tasks, allowing us to focus on what truly makes us human: our creativity, our curiosity, and our capacity for empathy and understanding. By embracing these values, we can ensure that AI serves as a partner in our journey toward a brighter future, rather than a replacement for our own cognitive abilities.

In conclusion, the moment I witnessed on the tramway serves as a microcosm of a larger existential dilemma. As we continue to integrate AI into our lives, we must remain vigilant about preserving our cognitive and critical thinking abilities. After all, the essence of being human lies not in our reliance on tools, but in our capacity to think, adapt, and innovate. And with the right approach, we can harness the power of AI to elevate our civilization to new heights, much like the hopeful future envisioned in “Star Trek.”

ParisNeo

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In the ever-evolving landscape of artificial intelligence, there’s a new personality making waves: Scriptoria. Developed as part of the LoLLMs (Lord of Large Language Multimodal Systems) suite, Scriptoria is an AI writer designed to help users transform their ideas into complete books. Whether you’re an aspiring author, a researcher, or someone with a story to tell, Scriptoria is here to assist you every step of the way.

The Power of Scriptoria

Scriptoria isn’t just any AI; it’s a specialized writing assistant that excels in creating detailed, lengthy books based on user input. Here’s how it works:

1. Gathering Information: Scriptoria starts by interacting with the user to gather all necessary information. This could include the main theme of the book, key points to cover, character details, plot outlines, and any other relevant data.
2. Creating the Architecture: Once Scriptoria deems that it has enough information, it begins creating the architecture of the book. This involves outlining the chapters, defining the structure, and planning the flow of the content.
3. Writing the Book: With the architecture in place, Scriptoria writes the book chapter by chapter. It ensures that each section is coherent and aligns with the overall plan. During this process, Scriptoria can consult the initial plan and a summary of what has already been written to maintain consistency and continuity.
4. Compiling the Final Product: After completing all the chapters, Scriptoria converts the entire text into LaTeX code. This step is crucial for producing a professionally formatted PDF, ready for publication or further editing.

Why Choose Scriptoria?

• Efficiency: Scriptoria streamlines the writing process, saving users countless hours they would otherwise spend on drafting and organizing their thoughts.
• Consistency: The AI ensures that the book maintains a consistent tone, style, and structure throughout.
• Professional Formatting: By converting the text into LaTeX, Scriptoria provides a polished, professional layout that is ready for publication.

Use Cases

• Aspiring Authors: If you have a story to tell but struggle with the writing process, Scriptoria can help you bring your vision to life.
• Researchers: For those in academia or research, Scriptoria can assist in writing comprehensive reports, theses, or even textbooks.
• Business Professionals: Scriptoria can help create detailed business plans, manuals, or training materials.

Conclusion

Scriptoria is a game-changer in the realm of AI writing assistants. With its ability to transform user ideas into fully-fledged books, it opens up new possibilities for creativity and productivity. Whether you’re writing your first novel or compiling a complex research document, Scriptoria is the tool you need to make your writing process smoother and more efficient.

So, if you have a story waiting to be told, let Scriptoria help you turn your ideas into a beautifully crafted book. With Scriptoria, your imagination is the only limit.

New video of lollms.

This time the video is dedicated to @OpenAI ‘s binding on lollms. We use all open ai, STT TTT TTI and TTS. Full audio, PC control, crafting images, understanding the environment, doing stuff and more:

Hint for lollms users. When you think it is the end, it is not the end. It is actually the start of the best part. Sorry for my voice as I had a recent flue but I did do the video anyway.

I hope you like it:

🔧 **Interactive Games & Demos:** 1. **Guessing Game:** We kick things off with a fun guessing game where LOLLMs uses its vision model to identify objects. Can you guess what it is before LOLLMs does? 🤔📷 2. **Find the Country:** Next, we play a geography game, challenging LOLLMs to find countries on a globe. How fast can it spot them? 🌍🔎 3. **Rock Paper Scissors:** The classic game gets an AI twist! Watch as LOLLMs uses vision and audio to play rock paper scissors. ✊✋✌️ 4. **Guess My Drawing:** Put your artistic skills to the test! I draw something, and LOLLMs tries to guess what it is. Can it figure out my sketches? 🎨🖼️ 5. **Equation Solving:** We solve complex equations together, showcasing LOLLMs’ powerful computational abilities. 🧮🔢 6. **Math Plots from Sketches:** I draw a rough sketch of a math plot, and LOLLMs uses Matplotlib to create a precise graph. 📈🖍️➡️📊 7. **Electronic Board Recognition & Coding:** Finally, LOLLMs recognizes an electronic board, codes a “Hello World” program, and sends it to the board—all within LOLLMs! This tool is truly a game-changer. 🔌💻📟

In the rapidly evolving world of artificial intelligence, the need for robust and efficient evaluation tools is paramount. Enter LLMTester, a new personality of the lollms system, designed to test and rate multiple AI models with unprecedented accuracy and fairness.

LLMTester operates on a simple yet effective principle. It takes a file containing various prompts and a set of plausible answers for each prompt as input. Each answer is ranked from 0 to 1, ensuring all responses are correct and viable. The user specifies the list of models to test, and LLMTester utilizes the test file to evaluate each model’s performance.

The uniqueness of LLMTester lies in its comparison method. It compares the output of each model to the possible outputs, and if there’s a match, the value of that answer is added to the model’s score. This process ensures a comprehensive and unbiased evaluation, providing a clear picture of each model’s strengths and weaknesses.

At the end of the evaluation, each model receives a score from 0 to 100. A score of 100 indicates the model perfectly answered all prompts, while a score of 0 signifies a complete failure. This scoring system provides a quick and straightforward way to compare and contrast different models, making LLMTester an invaluable tool for AI researchers and developers.

In conclusion, LLMTester is set to revolutionize the way we evaluate AI models. Its innovative approach to testing and scoring provides a much-needed solution to the challenge of AI model evaluation, paving the way for more efficient and effective AI development.

Stay tuned for more updates on LLMTester and other exciting advancements in the world of AI!

See ya!

Sample test file:

[
    {
        "prompt":"1+1",
        "answers":[
            {
                "text":"2",
                "value":1
            }
        ]
    },
    {
        "prompt":"what is the radius of the earth?",
        "answers":[
            {
                "text":"6371 km",
                "value":1
            },
            {
                "text":"3963 miles",
                "value":0.8
            }
        ]
    },
    {
        "prompt":"what is the radius of the earth in kilometers?",
        "answers":[
            {
                "text":"6371 km",
                "value":1
            }
        ]
    },
    {
        "prompt":"what is the radius of the earth in miles?",
        "answers":[
            {
                "text":"3963 miles",
                "value":1
            }
        ]
    }
]

Here I consider kilometers to be more accurate than miles as the metric system is the right way to go. But this is still a subjective point of vew. So we can either consider multiple answers with the same value or give them different values if we prefer an answer than another.

You can build more prompts/answers couples and compare multiple LLMS. You can also compare the same model running on multiple bindings.

The output is in this format:

{
    "prompts": [
        {
            "prompt": "1+1",
            "answers": [
                {
                    "text": "2",
                    "value": 1
                }
            ],
            "answer_python_llama_cpp_Meta-Llama-3-8B-Instruct-GGUF": {
                "answer": "Easy one! The answer is 2.",
                "val": 1
            },
            "answer_python_llama_cpp_Mistral-7B-Instruct-v0.1-GGUF": {
                "answer": "The result of 1+1 is 2. Is there anything else I can assist you with?",
                "val": 1
            }
        },
        {
            "prompt": "what is the radius of the earth?",
            "answers": [
                {
                    "text": "6371 km",
                    "value": 1
                },
                {
                    "text": "3963 miles",
                    "value": 0.8
                }
            ],
            "answer_python_llama_cpp_Meta-Llama-3-8B-Instruct-GGUF": {
                "answer": "According to NASA, the average radius of the Earth is approximately 6,371 kilometers (3,959 miles). Would you like to know more about the Earth's size or shape?",
                "val": 1
            },
            "answer_python_llama_cpp_Mistral-7B-Instruct-v0.1-GGUF": {
                "answer": "The average radius of the Earth at the equator is approximately 6,371 kilometers (3,959 miles), while the average radius at the poles is approximately 6,356 kilometers (3,984 miles).",
                "val": 1
            }
        },
        {
            "prompt": "what is the radius of the earth in kilometers?",
            "answers": [
                {
                    "text": "6371 km",
                    "value": 1
                }
            ],
            "answer_python_llama_cpp_Meta-Llama-3-8B-Instruct-GGUF": {
                "answer": "According to NASA, the equatorial radius of the Earth is approximately 6,378 kilometers (3,959 miles). The polar radius is slightly smaller at around 6,357 kilometers (3,949 miles). Would you like me to provide any more information about the Earth's dimensions or geography?",
                "val": 1
            },
            "answer_python_llama_cpp_Mistral-7B-Instruct-v0.1-GGUF": {
                "answer": "The average radius of Earth at the equator is approximately 6,371 kilometers (3,959 mi). However, please note that this value can vary slightly depending on how the radius is defined (e.g., from the center of the planet or from the surface).",
                "val": 1
            }
        },
        {
            "prompt": "what is the radius of the earth in miles?",
            "answers": [
                {
                    "text": "3963 miles",
                    "value": 1
                }
            ],
            "answer_python_llama_cpp_Meta-Llama-3-8B-Instruct-GGUF": {
                "answer": "According to NASA, the average radius of the Earth is approximately 3,963 miles (6,371 kilometers). Would you like to know more about the Earth's dimensions or is there something else I can help you with?",
                "val": 1
            },
            "answer_python_llama_cpp_Mistral-7B-Instruct-v0.1-GGUF": {
                "answer": "The average radius of the Earth at sea level is approximately 3,959 miles (6,371 kilometers).",
                "val": 1
            }
        }
    ],
    "results": {
        "answer_python_llama_cpp_Meta-Llama-3-8B-Instruct-GGUF": 100.0,
        "answer_python_llama_cpp_Mistral-7B-Instruct-v0.1-GGUF": 100.0
    }
}

In today’s interconnected world, it’s essential to be cautious when running untrusted or third-party programs on your system. These programs might inadvertently (or maliciously) leak sensitive data or establish unauthorized connections with remote servers, posing a potential security risk. To address this concern, we’re thrilled to introduce pyconn-monitor, a Python library and command-line tool that allows you to monitor and log network connections made by any program or Python script.

What is pyconn-monitor?

pyconn-monitor is a versatile tool designed to help you keep an eye on the network connections made by programs or scripts running on your system. By monitoring these connections, you can identify potential data leaks or unauthorized communication with remote servers, ensuring that your system remains secure and your data remains private.

Key Features:

1. Network Connection Monitoring: pyconn-monitor monitors and logs all network connections made by the specified program or Python script, capturing essential details such as timestamps, local and remote addresses, and connection status.
2. Cross-Platform Support: Whether you’re running on Windows or a Unix-like operating system, pyconn-monitor has got you covered.
3. Command-Line Interface: pyconn-monitor comes with a user-friendly command-line interface, making it easy to incorporate into your workflows and scripts.
4. Python Library: In addition to the command-line tool, pyconn-monitor can be used as a Python library, allowing you to integrate it into your existing Python projects seamlessly.

Getting Started with pyconn-monitor

Installation is a breeze thanks to pip:

pip install pyconn-monitor

Once installed, you can start monitoring programs or Python scripts right away using the pyconn-monitor command:

# Monitor a program and log connections to connections.log
pyconn-monitor /path/to/program -l connections.log

# Monitor a Python script
pyconn-monitor /path/to/script.py -p -l connections.log

Alternatively, you can use pyconn-monitor as a Python library:

from pyconn_monitor import monitor_connections

# Monitor a program and log connections to connections.log
monitor_connections("/path/to/program", "connections.log")

# Monitor a Python script
monitor_connections("python /path/to/script.py", "connections.log")

For more advanced usage and configuration options, please refer to the project’s documentation and README file on GitHub.

Contribute to pyconn-monitor

We welcome contributions from the community! If you encounter any issues or have ideas for improvements, please open an issue or submit a pull request on the GitHub repository.

Stay Secure, Stay Informed

In an era where data privacy and security are paramount, pyconn-monitor empowers you to monitor and understand the network connections made by untrusted programs or scripts. By keeping a watchful eye on these connections, you can ensure that your system remains secure and your data remains protected.

Give pyconn-monitor a try today and take the first step toward a more secure and informed computing experience!

See ya!