As an American, this is still crap. Assange is a hero as is Snowden and others that had the moral backbone to stand up to the post 9/11 monster insistent on proving bin Laden won against any remaining visage of freedom and democracy in the USA. We live in his dystopian dream of crumbling failure now, and insist on persecuting those that show how and what we lost.
Not a blinker in the group, sudden lane changes, tailgating; what else?
It has a lot of potential if the T5 can be made conversational. After diving into a custom DPM adaptive sampler, there is a lot more specificity required. I believe the vast majority of people are not using the model with the correct workflow. Applying the old model workflows to SD3 makes garbage results. The 2 CLIPS models and the T5 need separate prompts, and the negative prompt needs an inverted channel with a slight delay before reintegration. I also think the smaller quantized version of the T5 is likely the primary problem overall. Any Transformer text model that small, that is them quantized to extremely small size is problematic.
The license is garbage. The company is toxic. But the tool is more complex than most of the community seems to understand. I can generate a woman lying on grass in many intentional and iterative ways.
You need to be sure your instance in federated with the instance that you are engaging with and visa versa. It is always listed on the …/instances page. So your host is:
Just keep going until you hit China. Five year old me will be blown away.
It would be far easier to hit up some near Earth objects, then maybe Vesta, but Ceres is the mother load. Small enough to be solid, but large enough to be spherical and therefore somewhat differentiated if you can get to the center of that Tootsie Pop.
Maybe in the process we’ll learn enough to effectively mine and utilize Phobos, the larger moon of Mars at 11 km in mean radius. That is the largest accessible orbital habitat that we have available (in flat pack IKEA furniture form) and it only has around 30-50 million years before it hits the Roche limit and turns into a ring, unless we manage to intervene.
Adding to what others have already mentioned… Most of the gold will be from various collisions of external objects. The vast majority of the gold and other heavy elements are in Earth’s core due to gravitational differentiation.
There is a volcano (in South America IIRC) that has unusually high gold content, but it is from the underground magma reservoir coming in contact with gold deposits. This is why space mining is a really big deal. The Earth is a resource poor gravity prison by comparison. The wealth in space is enormous compared to any differentiated body.
Gold is actually everywhere and relatively common, but only in very small quantities. Under the right conditions, the weight can help gold to concentrate and fall out of solution when that solution was once covering a very large area, dissolved the tiny bits of gold found all over a large area, and then pools into a low point over extremely long periods of time.
I found a Python project that does enough for my needs. Jq looks super powerful though. Thanks. I managed to get yq working for PNG’s, but I had trouble with both jq and yq with safetensor files. I couldn’t figure out how to parse a string embedded in an inconsistent starting binary, and with massive files. I could get in and grab the first line with head. I tried some stuff with expansions, but that didn’t work and sent me looking for others that have solved the issue better than myself.
So can I bang on the Intel 12th gen P-cores with the enterprise microcode to see if they are really fused? I promise to isolate the CPU set if I can pretty please have access to the full AVX instructions for use with llama.cpp. Pretty please.
ComfyUI is an extremely popular graphical user interface for Stable Diffusion that’s shared freely on Github, making it easier for users to generate images and modify their image generation models. ComfyUI_LLMVISION, the extension that was compromised to hack users, is a ComfyUI extension that allowed users to integrate large language models GPT-4 and Claude 3 into the same interface.
That is likely a small group of people. I have never used proprietary AI and never plan to do so. A feature to run these is already a stalkerware corporate hack IMO.
You’re in a metabolic phase where you are craving junk food. Let me shove your favorite things in your face in constant interruptions of your media consumption because you quit buying my product and you’re vulnerable.
I’m an imbecile managing healthcare insurance. Your resting heart rate is well below average because you’ve been an athlete in the past. I’m too stupid to handle this kind of data on a case by case level. You have absolutely no other health factors, but I’m going to double the rates of any outliers because I’m only concerned with maximizing profitability.
The human cognitive scope is tiny. Your data is a means of manipulation. Anyone owning such data can absolutely influence and control you in an increasingly digital world.
This is your fundamental autonomy and right to citizenship instead of serfdom. Allowing anyone to own any part of you is stepping back to the middle ages. It will have massive impacts long term for your children’s children if you do not care.
Once upon a time there were Greek citizens, but they lost those rights to authoritarianism. Once upon a time there were Roman citizens, but they lost those rights to authoritarians, which lead to the medieval era of serfs and feudalism. This right of autonomy is a cornerstone of citizenship. Failure to realize the import of this issue is making us the generation that destroyed an era. It is subtle change at first, but when those rights are eroded, they never come back without paying the blood of revolutions.
There is truth in statistics. The minor errors are irrelevant in the actual LLM. Problems like the bad reddit quotes by google have nothing to do with and actual LLM, that is a RAG (augmented retrieval) and just bad standard code. The model itself is learning statistical word associations across millions of instances of similar data. The minor errors are irrelevant in this context.
Generative tools posted online are trash in their controls and especially the depth of capabilities. If you play with an enthusiast level consumer machine, with ComfyUI, the full nodes manager (not just the comfy anonymous repo), and the hundreds of nodes, things change. I’ve spent the last week reading white papers, following code examples, and trying new techniques. The possibilities are getting exponentially complex in a short period of time. I think most people working on generative AI in the public space are turning inward at the moment because it is hard to grasp all the possibilities, or maybe I’m just not following the right people.
We are in a data grab phase where it is feasible to collect more data as opposed to refining what exists. I think the techniques are growing too fast to say what will be the most efficient way of refining data. Eventually a refinement phase is likely.
Hallucinations are not actually a thing. The reasons they happen are just too complex to explain to a consumer public or no one would use the tool. If you learn about alignment and you really start reading into the tokenizer code, you’ll learn that it is just a complex system where most errors are due to safety alignment. The rest are generalizations made for an average use case. The underlying capability is far more complex and nuanced than any publicly hosted stalkerware data mining operation might appear. These real capabilities of the LLM are the building blocks of change. There are many other systems than just the tensor tables and word relationship statistics.
You should always get a second opinion or more for any kind of serious diagnosis. Doctors are only human and they make mistakes too. However, I don’t think anyone here can ethically give you medical advice. I think a lot of people here are struggling in life to some extent. I’m no exception. I hope it works out for you though.
We all have various states of inner dialogue. Your functional thought will have a bearing on how you think and interact with others. Some people have a rich inner dialogue. I do have a rich inner dialogue, but it is not with other people per say. I enjoy thinking in terms of how someone else might view me or some event, but it is never persistent.
One thing I like to tell myself is that everyone has moments when they show some kinds of signs of mental health problems. That is perfectly normal. It is only a disorder when the problem is impacting your life in a way that you are unable to address.
I run my models on my own hardware. In general, the larger quantized models run better when raw. They are more intuitive and approachable. Almost everything people complain about with AI is because they do not understand how it works in practice. There are many layers of function and capability beneath the surface. If all you use are the small models, like anything under around a 30B, you’re likely to find it hard to use. At these sizes the model lacks the comprehension to self diagnose many problems. These models tend to have multiple potential error sources that can occur at the same time. So that can be really frustrating too. If you understand most of the ways models respond in error, it becomes much easier to address issues with the smaller models. The smaller models can be useful and quite capable with specialized training.
Think of it like this: the AI has a small available window of attention it can operate within. (There are multiple spaces where “Attention” has meanings that are different.) That window can view a small part of the surface of information available. You can move the window around to view any section on the surface relatively easy by using a basic prompt with good instructions. However, that is nowhere near what the model really knows. You need to build momentum in the space you’re interested in accessing within the model. This is only one of several factors. You also need to know how to talk to a model. This is very different than humans. For instance, my casual grammar and style in the last sentence is useless with AI. I must use proper nouns and think out what I am trying to say differently. Personally I have other methods where I establish who I am, my knowledge and expectations, and then I ask a series of leading questions where I know the answers and can let the AI build the prompt dialogue momentum for me. Then I can ask much deeper questions and get good/useful answers.
The momentum factor is one of the largest differences between the bigger and smaller models. With the larger (30B+) models it is not very hard to build momentum in a space and get deeper into useful territory. With the smaller models you’re kinda stuck in the stupid entry level zone at first. It is like a dense underbrush at the edge of a forest where you’re in need of a brake to find your footpath to where you want to go. If you have the experience to spot the issues, you can walk right through that dense tangle and find the other side with only minor annoyances and a few thorns. If you want to use the small model for something specific, you can train it yourself on some little niche and this will be like a bridge over the dense thicket and get you into a useful space relative to the training.
By contrast, the larger models have brakes and footpaths all across the edge of the forest. It is still easy to get lost, or on some kind of dead end, but the forest itself is far more self aware and, if asked well, it will be able to help you find your way more effectively and with far less momentum required to get you there.
If the tool you’re using does not give you absolute and full control of everything the AI has in the prompt, you’re already in trouble. Your past prompts may be fed back into the model with each query. This I’d great for the stalkerware company trying to data mine, as it creates a better and more detailed profile of who you are as a person. However any unrelated information passed to the model at the same time ruins your momentum within the underlying tensor tables of the model.
I use Oobabooga Textgen a lot (GitHub), and with the notebook tab interface. That is more like a text editor where I see everything in the entire prompt. I also have my own Python code that adds features to this interface.
Models come from huggingface.co. I often use a Mixtral 8×7B or a Llama 70B on the large models side. I also use the newer Llama3 8B on the smaller side. The 8×7B is much quicker than the 70B and it is nearly as accurate. However, it lacks some of the advanced self awareness aspects and displays some issues that are common to smaller models.
I’m extremely intuitive and function in abstract thought most of the time. My view of the world is largely that of relativism. I find accuracy to be subjective in all spaces and “facts” as foolish idealism in an absolute sense. I view everything models say as a casual water cooler conversation with an interesting non expert. Nothing said is a primary citation worthy source, but neither is anything said here, yet here we are.
With Oobabooga Textgen, there is a chatGPT compatible API. If you launch Oobabooga from the command line, it only takes adding the “–listen” flag to make Oobabooga available on your home network, and/or “–api” to make the chatGPT API available as well. This works with most third party tools that connect to chatGPT, or so I’ve read.
If you want to get into more technically capable setups, you need a RAG for document reference look up and retrieval. A couple of RAG options are Ollama and privateGPT, or if you want a basic code interface for Python, langchain and chroma db.
It points you in a new direction of research and information like any others here. Sorry I cared to ask and respond I certainly won’t next time.
Offline open source AI pointed me to this article on Wikipedia:
Some HPDs reduce the sound reaching the eardrum through a combination of electronic and structural components. Electronic HPDs are available in both earmuff and custom earplug styles. Electronic microphones, circuitry, and receivers perform active noise reduction, also known as noise-cancelling, in which a signal that is 180-degrees out-of-phase of the noise is presented, which in theory cancels the noise.[1]
I keep a copy of 4chanGPT around; trained on the board. It is not very bright or useful. It’s ribaldry and politically incorrect sarcasm is mildly amusing. However, its primarily useful because it does not have the safety alignment training and mechanisms that all other mainstream large language models have. Using 4chanGPT, I can see the structures that are truly persistent across models, their purpose, and their functions. It is only a minor thing I rarely use.
Only fools buy and compare hardware now. You shop for hardware by shopping for FOSS software first.
It takes away time from campaigning and money raising. Sadly, the goal is not related to him. No one is paying attention to real issues and demanding accountability from Congress while all this is happening. This is the only factor that matters to the criminal oligarchy. Their goal is to prevent any forms of reasonable legislation that might restrict the loopholes they use to loot and pillage the rest of us.
I only need the ram to initially load an AI model.
The French lived in dense areas with power centers nearby. We live in endless sprawl with a need to mobilize the guillotine, while our houses are made of artillery target toothpicks for a reason.