It's a standard clause for most apps. If a breach of the terms of conditions (such as using it for commercial purposes, like selling the software), they are allowed to launch an investigation. No where does this mention "spying" on modifying the app for such use.
- The chatbox field has a normal "write here" state, when no chat is really selected. I thought my keyboard broke until I discovered that
- I didn't find a way to set cuda acceleration before loading a model, only managed to set gpu offloaded layers and using "relaunch to apply"
- Some HugginFace models are simply not listed and there's no indication about why. I guess models are really curated, but somehow presented as a HuggingFace browser?
- Scrolling in the accordion parts of the interface seems to be responding to mouse wheel scroll only. I have a mouse with a damaged one and couldn't find a way to reliably navigate to bottom drawers
That said, I really liked the server tab, which allowed for initial debugging very easily
M1 is only 3 years old and no one cares to support intel macs any more. There are surely a lot of them out there. Are they that much worse to run LLMs on?
I did just that, including signing up for discord. Despite never having used discord before, I was able to find the link to the beta AppImage in a pinned message and downloaded it. Made it executable with chmod +x LM...... Ran it. Searched for some of the models referenced in this discussion. Downloaded one and ran it. It just worked on Linux Mint 21.2.
For my experiments with new self-hostable models on Linux, I've been using a script to download GGUF-models from TheBloke on HuggingFace (currently, TheBloke's repository has 657 models in the GGUF format) which I feed to a simple program I wrote which invokes llama.cpp compiled with GPU support. The GGUF format and TheBloke are a blessing, because I'm able to check out new models basically on the day of their release (TheBloke is very fast) and without an issue. However, the only frontend I have is console. Judging by their site, their setup is exactly the same as mine (which I implemented over a weekend), except that they also added a React-based UI on top. I wonder, how they're planning to commercialize it, because it's pretty trivial to replicate, and there're already open-source UI's like oogabooga.
I'd like to build myself a headless server to run models, that could be queried from various clients locally on my LAN, but am usure where to start and what the hardware requirements would be. Software can always be changed later but I'd rather buy the hardware parts only once.
Do you have recommendations about this? or blog posts to get started? What would be a decent hardware configuration?
You can currently do this in an M2 Max with ollama and a Nextjs UI [0] running in a docker container. Any devices in the network can use the UI... and I guess if you want a LAN API you just need to run another container with with OAI compatible API that can query ollama.. eg [1]
Ollama does this. I run it in a container on my homelab (Proxmox on a HP EliteDesk SFF G2 800) and 7B models run decently fast on CPU-only. Ollama has a nice API and makes it easy to manage models.
Together with ollama-webui, it can replace ChatGPT 3.5 for most tasks. I also use it in VSCode and nvim with plugins, works great!
I have been meaning to write a short blog post about my setup...
Depending on what you mean by "production" you'll probably want to look at "real" serving implementations like HF TGI, vLLM, lmdeploy, Triton Inference Server (tensorrt-llm), etc. There are also more bespoke implementations for things like serving large numbers of LoRA adapters[0].
These are heavily optimized for more efficient memory usage, performance, and responsiveness when serving large numbers of concurrent requests/users in addition to things like model versioning/hot load/reload/etc, Prometheus metrics, things like that.
One major difference is at this level a lot of the more aggressive memory optimization techniques and support for CPU aren't even considered. Generally speaking you get GPTQ and possibly AWQ quantization + their optimizations + CUDA only. Their target users and their use cases are often using A100/H100 and just trying to need fewer of them. Support for lower VRAM cards, older CUDA compute architectures, etc come secondary to that (for the most part).
Thanks! Really helpful. I've a 3090 at home and my idea is to do some testing on a similar config in the cloud to have an idea of the amount of requests that could be served.
The good news is the number of requests and performance is very impressive. For example, on my RTX 4090 from testing many months ago with lmdeploy (it was the first to support AWQ) I was getting roughly 70 tokens/s each across 10 simultaneous sessions with LLama2-13b-Chat - almost 700 tokens/s total. If I were to test again now with all of the impressive stuff that's been added to all of these I'm sure it would only be better (likely dramatically).
The bad news is because "low VRAM cards" like the 24GB RTX 3090 and RTX 4090 aren't really targetted by these frameworks you'll eventually run into "Yeah you're going to need more VRAM for that model/configuration. That's just how it is." as opposed to some of the approaches for local/single session serving that emphasize memory optimization first and tokens/s for a single session next. Often with no consideration or support at all for multiple simultaneous sessions.
It's certainly possible that with time these serving frameworks will deploy more optimizations and strategies for low VRAM cards but if you look at timelines to even implement quantization support (as one example) it's definitely an after-thought and typically only implemented when it aligns with the overall "more tokens for more users across more sessions on the same hardware" goals.
Loading a 70B model on CPU and getting 3 tokens/s (or whatever) is basically seen as an interesting yet completely impractical and irrelevant curiosity to these projects.
In the end "the right tool for the job" always applies.
> usure where to start and what the hardware requirements would be
Have a look at the localllama subreddit
In short though dual 3090 is common, single 4090 or various flavours of M123 macs. Alternatively p40 can be jury-rigged too but research that carefully. In fact anything with more than one gpu is going to require careful research
What a comment. Why do it the easy way when the more difficult and slower way works ok it to the same result‽ For people who just want to USE models and not back at them, TheBloke is exactly the right place to go.
Like telling someone interested in 3D printing minis to build a 3D printer instead of buying one. Obviously that helps them get to their goal of printing minis faster right?
Actually, consider that the commenter may have helped un-obfuscate this world a little bit by saying that it is in fact easy. To be honest the hardest part about the local LLM scene is the absurd amount of jargon introduced - everything looks a bit more complex than it is. It’s really is easy with llama.cpp, someone even wrote a tutorial here: https://github.com/ggerganov/llama.cpp/discussions/2948 .
But yes, TheBloke tends to have conversions up very quickly as well and has made a name for himself for doing this (+more)
I don't mean this as a criticism, I'm just curious because I work in this space too: who is this for? What is the niche of people savvy enough to use this who can't run one of the many open source local llm software? It looks in the screenshot like it's exposing much of the complexity of configuration anyway. Is the value in the interface and management of conversation and models? It would be nice to see info or even speculation about the potential market segments of LLM users.
In most workplaces that deal with LLMs you’ve got a few classes of people:
1. People who understand LLMs and know how to run them and have access to run them on the cloud.
2. People who understand LLMs well enough but don’t have access to cloud resources - but still have a decent MacBook Pro. Or maybe access to cloud resources is done via overly tight pipelines.
3. People who are interested in LLMs but don’t have enough technical chops/time to get things going with Llama CPP.
4. People who are fans of LLMs but can’t even install stuff in their computer.
This is clearly for #3 and it works well for that group of people. It could also be for #2 when they don’t want to spin up their own front end.
I got Mistral-7b running locally, and although it wasn't hard, it did take some time nonetheless. I just wanted to try it out and was not that interested in the technical details.
It's for people who want to discover LLMs and either don't have the skill to deploy it, or value their time, and prefer not to fool around for hours getting it to work before they can try it.
The fact it has configuration is good, as long as it has some defaults.
Exactly. People like me have been waiting for a tool like this.
I'm more than capable of compiling/installing/running pretty much any software, but all I want is the ability to chat with a LLM of my choice without spending an afternoon tabbing back to a 30 step esoteric GitHub .md full of caveats, assumptions, and requiring dependencies to be installed and configured according to preferences I don't have.
Yeah, I think I fit into this category. If I see a new model announced, it’s been nice to just click and evaluate for myself if it’s useful for me. If anyone knows other tools for this kind workflow I’d love to hear about them. Right now I just keep my “test” prompts in a text file.
It's actually quite handy. I built all the various things by hand at one point, but had to wipe it all. Instead of following the directions again I just downloaded this.
Being able to swap out models is also handy. This probably saved a couple of hours of my life, which I appreciate.
For me it's pretty simple- LM Studio supports Apple Silicon GPU acceleration out of the box, and I like the interface better than Gradio Web UI. It saves me the headache and the tinkering of the alternatives. That said, free software that's hiring developers probably won't stay free for long, so I'm keeping my eye on other options.
Why purple or some shade of purple is the color of all AI products? For some reason, the landing pages of AI products immediately remind of Crypto products. This one does not have Crypto vibes but the colour is purple. I don't get why.
Because apps mostly prefer dark theme now, and dark red, brown, dark green and so on look weird, and gray is OK, but very boring, like someone desaturated the UI. Which leaves shades of blue and purple.
It's a default color in Tailwind.css and is used in a lot of the templates and examples. Nine times out of ten, if you check the source of a page with this flavor of purple, you'll see it's using Tailwind, as the OP site in fact does.
Ah! that makes more sense. New startup, new tech and therefore the new default color. I hope its just that and because I only tend to notice AI startups purple is what I end up seeing.
LMStudio is great to run local LLMs, also support OpenAI-compatible API. In the case you need more advance UI/UX, you can use LMStudio with MindMac(https://mindmac.app), just check this video for details https://www.youtube.com/watch?v=3KcVp5QQ1Ak.
Thank you for your support. I just found a workaround solution to use Ollama with MindMac. Please check this video https://www.youtube.com/watch?v=bZfV70YMuH0 for more details. I will integrate Ollama deeply in the future version.
MindMac is the first example I've seen where the UI for working w/ LLMs is not complete and utter horseshit and starts to support workflows that are sensible.
I will buy this with so much enthusiasm if it holds up. Argh, this has been such a pain point.
This works, but I've noticed that my CPU use goes up to about 30 percent, all in kernel time (windows), after installing and opening this, even when it's not doing anything, on two separate machines... I also hear the fan spinning fast on my laptop.
Killed the LM studio process and re-opened it and the ghost background usage is down to about 5%.
I'm having a lot of fun chatting with characters using Faraday and koboldcpp. Faraday has a great UI that lets you adjust character profiles, generate alternative model responses, undo, or edit dialogue, and experiment with how models react to your input. There's also SillyTavern that I have yet to try out.
I looked at Ollama before, but couldn't quite figure something out from the docs [1]
It looks like a lot of the tooling is heavily engineered for a set of modern popular LLM-esque models. And looks like llama.cpp also supports LoRA models, so I'd assume there is a way to engineer a pipeline from LoRA to llama.cpp deployments, which probably covers quite a broad set of possibilities.
Beyond llama.cpp, can someone point me to what the broader community uses for general PyTorch model deployments?
I haven't quite ever self-hosted models, and am really keen to do one. Ideally, I am looking for something that stays close to the PyTorch core, and therefore allows me the flexibility to take any nn.Module to production.
As far as I know, ollama doesn’t support exllama, qlora fine tuning, multi-GPU, etc. Text-generation-webui might seem like a science project, but it’s leagues ahead (Like 2-4x faster inference with the right plugins) of everything else. Also has a nice openai mock API that works great.
Feature set seems like a decent amount of overlap. One limitation of FastChat, as far as I can tell, is that one is limited to the models that FastChat supports (though I think it would be minor to modify it to support arbitrary models?)
I have a question, looking for the most popular "uncensored" model I just find "TheBloke/Luna-AI-Llama2-Uncensored-GGML", but it has 14 files to download between 2 to 7 GB, I just download the first one: https://imgur.com/a/DE2byOB
The readme of their repositories each have tables that detail the quality of each file. The QK_4_M and QK_5_M seem to be the two main recommended ones for low quality loss while too being too large.
Only need 1 of the files, but recommend checking out the GGUF version of the model (just replace GGML in the URL) instead of GGML. Llama.cpp no longer supports GGML, and not sure if TheBloke still uploads new GGML versions of models.
Honest question from someone new to exploring and using these models; why do you need uncensored? What are the use-cases that would call for it?
Again, not questioning your motives or anything, just straight up curious. To use your example, any of us can find bomb building info online fairly easily, and has been a point of social contention since the Anarchist's cookbook. Nobody needs an uncensored LLM for that, of course.
It’s very easy to hit absurd “moral” limits on chatgpt for the most stupid things.
Earlier I was looking for “a phrase that is used as an insult for someone who writes with too much rambling” and all I got was some bullshit about how it’s sorry but it can’t do that because it’s allegedly against its OpenAI rules.
So I asked again “a phrase negatively used to mean someone that writes too much while rambling” and it worked.
I simply cannot be bothered to deal with stupid insipid “corporate friendly language” and other dumb restrictions.
Imagine having a real conversation with someone and they freaked out any time anything negative was discussed?
Where do you typically have the 'safe search' setting when you web search? Personally I have it 'off' ('moderate' when I worked in an office I think) even though I'm not looking for anything that ought to be filtered by it.
I'm not using models censored or otherwise, but I imagine I'd feel the same way there - I won't be offended, so don't try to be too clever, just give me the unfiltered results and let me decide what's correct.
(Bing AI actually banned me for trying to generate images a rough likeness of myself by combining traits of minor celebrities - in combination it shouldn't have looked like those people either, so I don't think should violate ToS, certainly it didn't in intention (I wanted myself, but it doesn't know what I look like and I couldn't provide a photo at the time, idk if you can now (banned!)) so it does happen, 'false positive censoring' if you like.)
Holy shit that's so badass BingAI banned you. I mean like it sucks and i hope it gets resolved, but still awesome. you got it just from trying to make a composite of people's faces? i guess it takes fraud seriously, at least for biometrics. however you clearly either weren't doing that or omitted some choice details. good luck with appealing; that process always wrecks my faith in humanity.
i've been trying to see how much i can get away with before it suspends or bans me using one of my throwaway accounts. it takes some doing, but if you convince it you aren't doing something shady right before doing something shady it'll play along with you a fair bit more than if you just say "hi bing, i wanna do some shady shit." unfortunately you have to engineer your "i'm not gonna do (insert something shady)" prompt on a per shade basis.
I'd switch that question around: Why would I want to use a censored LLM?
It doesn't make sense for me personally. It does make sense if you're offering an LLM publicly, so that you doesn't get bad PR if your LLM says some politically incorrect or questionable things.
At best the false positives are a nuisance and makes the model dumber. But really censorship is fundamentally wrong.
Unless we are talking about gods and not flawed humans like me I prefer to have the say in what is right and what is wrong for things that are entirely personal and only affect me.
Because I don’t appreciate it when a model has blatant democrat/anti-republican bias, for example. The fact that chatGPT, Bard, etc are heavily and purposefully biased on certain topics is well documented[1].
> Again, not questioning your motives or anything, just straight up curious. To use your example, any of us can find bomb building info online fairly easily, and has been a point of social contention since the Anarchist's cookbook. Nobody needs an uncensored LLM for that, of course.
When you ask a local LLM, at worst you get no useful info.
When you ask online, at worst you spend the rest of your life in a government black site without any chance of due process.
Am I missing something here? I'm on a recent M2 machine. Every model I've downloaded fails to load immediately when trying to load it. Is there some way to get feedback on the reason for failure, like a log file or something?
EDIT: The problem is I'm on macOS 13.2 (Ventura). According to a message in Discord, the minimum version for some (most?) models is 13.6.
I expected it to not let me run this. I have an intel Macbook, was expecting that I'd need Apple Silicon... am I misunderstanding something? I get fairly fast results at the prompt with the default model. How's this thing running with whatever shitty GPU I have in my laptop?
I include a universal binary of llama.cpp's server example to do inference. What's your machine? The lowest spec I've heard it running on is a 2017 iMac with 8GB RAM (~5.5 tokens/s). On my m1 with 64GB RAM I get ~30 tokens per second on the default 7B model.
Macbook Pro 2020 with 16gb of system ram. I think the gpu is Iris Plus? But I don't much keep up on those.
I'm now delving into getting this running in Terminal... there are a few things I want to try that I don't think the simple interface allows.
Also, I've noticed that when chats get a few kilobytes long, it just seizes up and can't go further. I complained to it, it spent a sentence apologizing, started up where it left off... and got about 12 words further.
Is anyone using open source models to actually get work done or solving problems in their software architecture? So far I haven't found anything near the quality of GPT-4.
Top of the line consumer machines can run this at a good clip, though most machines will need to use a quantized model (ExLlamaV2 is quite fast). I found a model for that as well, though I haven't used it myself:
Zephyr is coherent enough to bounce ideas off of, but I'm eagerly awaiting when open-source models are on par productivity wise with the big providers. I imagine some folks are utilizing codellama 34b somehow, but I haven't been able to effectively.
Newbie question... Is this purely for hosting text language models? Is there something similar for image models? i.e., upload an image and have some local model provide some detection/feedback on it.
After the latest Chatgpt debacles, the poor performance I'm getting from 4 turbo, I'd really like a local version of chatgpt4 or equivalent.
I'd even buy a new pc if I had too.
Considering the code is closed source and they can change the ToS anytime to send conversation data to their servers whenever they want, i would like to know what would be the benefit of using this over ChatGPT?
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[ 2.4 ms ] story [ 224 ms ] threadAnd when I do try them, it's with Little Snitch blocking outgoing connections.
- The chatbox field has a normal "write here" state, when no chat is really selected. I thought my keyboard broke until I discovered that
- I didn't find a way to set cuda acceleration before loading a model, only managed to set gpu offloaded layers and using "relaunch to apply"
- Some HugginFace models are simply not listed and there's no indication about why. I guess models are really curated, but somehow presented as a HuggingFace browser?
- Scrolling in the accordion parts of the interface seems to be responding to mouse wheel scroll only. I have a mouse with a damaged one and couldn't find a way to reliably navigate to bottom drawers
That said, I really liked the server tab, which allowed for initial debugging very easily
I did find it quite useful for opening a socket for remote tooling (played around withe the continue plugin).
The quirky UI did slow me down,but nothing really showstopping
The demo on this video is from Intel Mac https://youtu.be/C0GmAmyhVxM?si=puTCpGWButsNvKA5
It also supports openai compatible api and completely open-source unlike LM studio
Do you have recommendations about this? or blog posts to get started? What would be a decent hardware configuration?
[0]https://github.com/ivanfioravanti/chatbot-ollama
[1]https://github.com/BerriAI/litellm
Together with ollama-webui, it can replace ChatGPT 3.5 for most tasks. I also use it in VSCode and nvim with plugins, works great!
I have been meaning to write a short blog post about my setup...
These are heavily optimized for more efficient memory usage, performance, and responsiveness when serving large numbers of concurrent requests/users in addition to things like model versioning/hot load/reload/etc, Prometheus metrics, things like that.
One major difference is at this level a lot of the more aggressive memory optimization techniques and support for CPU aren't even considered. Generally speaking you get GPTQ and possibly AWQ quantization + their optimizations + CUDA only. Their target users and their use cases are often using A100/H100 and just trying to need fewer of them. Support for lower VRAM cards, older CUDA compute architectures, etc come secondary to that (for the most part).
[0] - https://github.com/S-LoRA/S-LoRA
The bad news is because "low VRAM cards" like the 24GB RTX 3090 and RTX 4090 aren't really targetted by these frameworks you'll eventually run into "Yeah you're going to need more VRAM for that model/configuration. That's just how it is." as opposed to some of the approaches for local/single session serving that emphasize memory optimization first and tokens/s for a single session next. Often with no consideration or support at all for multiple simultaneous sessions.
It's certainly possible that with time these serving frameworks will deploy more optimizations and strategies for low VRAM cards but if you look at timelines to even implement quantization support (as one example) it's definitely an after-thought and typically only implemented when it aligns with the overall "more tokens for more users across more sessions on the same hardware" goals.
Loading a 70B model on CPU and getting 3 tokens/s (or whatever) is basically seen as an interesting yet completely impractical and irrelevant curiosity to these projects.
In the end "the right tool for the job" always applies.
The setup of connecting to Ollama is a bit clunky, but once it's set up it works well!
https://github.com/ggerganov/llama.cpp/blob/master/examples/...
Have a look at the localllama subreddit
In short though dual 3090 is common, single 4090 or various flavours of M123 macs. Alternatively p40 can be jury-rigged too but research that carefully. In fact anything with more than one gpu is going to require careful research
Like telling someone interested in 3D printing minis to build a 3D printer instead of buying one. Obviously that helps them get to their goal of printing minis faster right?
But yes, TheBloke tends to have conversions up very quickly as well and has made a name for himself for doing this (+more)
So learn to cook.
1. People who understand LLMs and know how to run them and have access to run them on the cloud. 2. People who understand LLMs well enough but don’t have access to cloud resources - but still have a decent MacBook Pro. Or maybe access to cloud resources is done via overly tight pipelines. 3. People who are interested in LLMs but don’t have enough technical chops/time to get things going with Llama CPP. 4. People who are fans of LLMs but can’t even install stuff in their computer.
This is clearly for #3 and it works well for that group of people. It could also be for #2 when they don’t want to spin up their own front end.
The fact it has configuration is good, as long as it has some defaults.
I'm more than capable of compiling/installing/running pretty much any software, but all I want is the ability to chat with a LLM of my choice without spending an afternoon tabbing back to a 30 step esoteric GitHub .md full of caveats, assumptions, and requiring dependencies to be installed and configured according to preferences I don't have.
Being able to swap out models is also handy. This probably saved a couple of hours of my life, which I appreciate.
Tl;DR It's for Mac users
I will buy this with so much enthusiasm if it holds up. Argh, this has been such a pain point.
Killed the LM studio process and re-opened it and the ghost background usage is down to about 5%.
If you're looking to do the same with open source code, you could likely run Ollama and a UI.
https://github.com/jmorganca/ollama + https://github.com/ollama-webui/ollama-webui
- https://faraday.dev/
- https://github.com/LostRuins/koboldcpp
- https://github.com/SillyTavern/SillyTavern
It looks like a lot of the tooling is heavily engineered for a set of modern popular LLM-esque models. And looks like llama.cpp also supports LoRA models, so I'd assume there is a way to engineer a pipeline from LoRA to llama.cpp deployments, which probably covers quite a broad set of possibilities.
Beyond llama.cpp, can someone point me to what the broader community uses for general PyTorch model deployments?
I haven't quite ever self-hosted models, and am really keen to do one. Ideally, I am looking for something that stays close to the PyTorch core, and therefore allows me the flexibility to take any nn.Module to production.
[1]: https://github.com/jmorganca/ollama/blob/main/docs/import.md.
As far as I know, ollama doesn’t support exllama, qlora fine tuning, multi-GPU, etc. Text-generation-webui might seem like a science project, but it’s leagues ahead (Like 2-4x faster inference with the right plugins) of everything else. Also has a nice openai mock API that works great.
ooba's worth keeping an eye on, but koboldcpp is more stable, almost as versatile, and way less frustrating. It also still supports GGML.
https://github.com/oobabooga/text-generation-webui/issues/41...
- A local model runtime
- A model catalog
- A UI to chat with the models easily
- An openAI compatible API
And it has several plugins such as for RAG (using ChromaDB) and others.
Personally I think the positioning is very interesting. They're well positioned to take advantage of new capabilities in the OS ecosystem.
It's still unfortunate that it is not itself open-source.
Personally I use a locally served frontend to use ChatGPT via API.
Feature set seems like a decent amount of overlap. One limitation of FastChat, as far as I can tell, is that one is limited to the models that FastChat supports (though I think it would be minor to modify it to support arbitrary models?)
The other is some python thing that requires python knowledge of creating virtual environments, installing dependencies, etc...
Want to try uncensored models.
I have a question, looking for the most popular "uncensored" model I just find "TheBloke/Luna-AI-Llama2-Uncensored-GGML", but it has 14 files to download between 2 to 7 GB, I just download the first one: https://imgur.com/a/DE2byOB
I try the model and it works: https://imgur.com/a/2vtPcui
I should download all the 14 files to get better results?
Also, asking how to make a bomb it looks that at least this model isn't "uncesored": https://imgur.com/a/iYz7VYQ
teknium/openhermes-2.5-mistral-7b is a good one
You don't need all 14 files, just pick one that is recommended with a slight loss of quality - hover over the little (i)icon to find out
Only need 1 of the files, but recommend checking out the GGUF version of the model (just replace GGML in the URL) instead of GGML. Llama.cpp no longer supports GGML, and not sure if TheBloke still uploads new GGML versions of models.
Again, not questioning your motives or anything, just straight up curious. To use your example, any of us can find bomb building info online fairly easily, and has been a point of social contention since the Anarchist's cookbook. Nobody needs an uncensored LLM for that, of course.
Earlier I was looking for “a phrase that is used as an insult for someone who writes with too much rambling” and all I got was some bullshit about how it’s sorry but it can’t do that because it’s allegedly against its OpenAI rules.
So I asked again “a phrase negatively used to mean someone that writes too much while rambling” and it worked.
I simply cannot be bothered to deal with stupid insipid “corporate friendly language” and other dumb restrictions.
Imagine having a real conversation with someone and they freaked out any time anything negative was discussed?
TLDR: Thought police ruining LLMs
I'm not using models censored or otherwise, but I imagine I'd feel the same way there - I won't be offended, so don't try to be too clever, just give me the unfiltered results and let me decide what's correct.
(Bing AI actually banned me for trying to generate images a rough likeness of myself by combining traits of minor celebrities - in combination it shouldn't have looked like those people either, so I don't think should violate ToS, certainly it didn't in intention (I wanted myself, but it doesn't know what I look like and I couldn't provide a photo at the time, idk if you can now (banned!)) so it does happen, 'false positive censoring' if you like.)
i've been trying to see how much i can get away with before it suspends or bans me using one of my throwaway accounts. it takes some doing, but if you convince it you aren't doing something shady right before doing something shady it'll play along with you a fair bit more than if you just say "hi bing, i wanna do some shady shit." unfortunately you have to engineer your "i'm not gonna do (insert something shady)" prompt on a per shade basis.
It doesn't make sense for me personally. It does make sense if you're offering an LLM publicly, so that you doesn't get bad PR if your LLM says some politically incorrect or questionable things.
Unless we are talking about gods and not flawed humans like me I prefer to have the say in what is right and what is wrong for things that are entirely personal and only affect me.
[1] https://www.brookings.edu/articles/the-politics-of-ai-chatgp...
When you ask a local LLM, at worst you get no useful info. When you ask online, at worst you spend the rest of your life in a government black site without any chance of due process.
EDIT: The problem is I'm on macOS 13.2 (Ventura). According to a message in Discord, the minimum version for some (most?) models is 13.6.
I expected it to not let me run this. I have an intel Macbook, was expecting that I'd need Apple Silicon... am I misunderstanding something? I get fairly fast results at the prompt with the default model. How's this thing running with whatever shitty GPU I have in my laptop?
I include a universal binary of llama.cpp's server example to do inference. What's your machine? The lowest spec I've heard it running on is a 2017 iMac with 8GB RAM (~5.5 tokens/s). On my m1 with 64GB RAM I get ~30 tokens per second on the default 7B model.
I'm now delving into getting this running in Terminal... there are a few things I want to try that I don't think the simple interface allows.
Also, I've noticed that when chats get a few kilobytes long, it just seizes up and can't go further. I complained to it, it spent a sentence apologizing, started up where it left off... and got about 12 words further.
Thanks for trying it!
https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder
https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0
Top of the line consumer machines can run this at a good clip, though most machines will need to use a quantized model (ExLlamaV2 is quite fast). I found a model for that as well, though I haven't used it myself:
https://huggingface.co/oobabooga/CodeBooga-34B-v0.1-EXL2-4.2...
GPT-4 has an estimated 1.8 trillion parameters. Orders of magnitude beyond open source models and ~10x GPT-3.5 which has 175 billion parameters.
https://the-decoder.com/gpt-4-architecture-datasets-costs-an...
The decent chat ones are based on gpt data and they’re basically shitty distilled models.
The best use case is a narrow one that you decide and can create adequate fine-tuning data around. Plenty of real production ability here.
I have an HP z440 with an E5-1630 v4 and 64GB DDR4 quad channel RAM.
I run LLMs on my CPU, and the 7 billion parameter models spit out text faster than I can read it.
I wish it supported LMMs (multi modal models.)