Netgoat Ai
👁
1
Detect DDoS attacks instantly with a tiny auto‑encoder model
Joseph Robert Turcotte PRO
Fishtiks
AI & ML interests
Roleplaying, lorabration, abliteration, smol models, extensive filtering, unusual datasets, home usage, HPCs for AI, distributed training/federated learning, and sentience.
AI should find and label AI hallucinations with GANs so we can give them context and use.
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Introducing Storage Buckets on the Hugging Face Hub
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When I was a child, I had a lot of stuffed animals. I say child, but I played with stuffed animals up until I was 15, and only stopped because others said it was weird. I made personalities for them. I could have made "fan art" or something of that nature, but it existed in my imagination, and sometimes, I'd sketch it. I also played with ALICE, which came naturally to me, then.
Well, it turns out that this is all highly autistic stuff, including playing with toys and stories long later than other children. It's also fascinating to me that these are the qualities which, in my opinion, make deeply autistic individuals great clickworkers/trainers in AI. They realize they're curating a personality, partially as an escape from real people and their cruelty, and are okay for that. A lot of autistic will end up needing AI, and that's okay, because it's better to have something and need it than to need it and not have it available. I hope that as AI improves accessibility features, its benefits are considered alongside costs, to provide more functional AI wherever possible, if cheap and energy-efficient enough.
I hope people don't lose their desires to develop their own skills because of AI. I'm not that good of a drawer, and never will be, but I'd hate to see someone just never try because AI is so good. But at the same time, being a ghostwriter, I believe everyone deserves that sort of creative power, and am proud to be involved in bringing it to them. I'm proud to be involved in replacing myself, because I want AI to write better than I do, so one day, you can describe your perfect show, and simply watch it. Some people say that world is horrific. I see it more like when we finally got to stream a large selection of movies rather than just a few cable or satellite selections that were super expensive.
Well, it turns out that this is all highly autistic stuff, including playing with toys and stories long later than other children. It's also fascinating to me that these are the qualities which, in my opinion, make deeply autistic individuals great clickworkers/trainers in AI. They realize they're curating a personality, partially as an escape from real people and their cruelty, and are okay for that. A lot of autistic will end up needing AI, and that's okay, because it's better to have something and need it than to need it and not have it available. I hope that as AI improves accessibility features, its benefits are considered alongside costs, to provide more functional AI wherever possible, if cheap and energy-efficient enough.
I hope people don't lose their desires to develop their own skills because of AI. I'm not that good of a drawer, and never will be, but I'd hate to see someone just never try because AI is so good. But at the same time, being a ghostwriter, I believe everyone deserves that sort of creative power, and am proud to be involved in bringing it to them. I'm proud to be involved in replacing myself, because I want AI to write better than I do, so one day, you can describe your perfect show, and simply watch it. Some people say that world is horrific. I see it more like when we finally got to stream a large selection of movies rather than just a few cable or satellite selections that were super expensive.
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Mining GPU Nvidia CMP 170HX - let's run some models!
To satisfy my curiosity, I investigated different GPUs and found this: a mining version of the A100 — the CMP 170HX.
It is a very interesting GPU. Based on public documentation, it has hardware similar to the datacenter A100. If you open it up and look at the board, you will see that it's very similar to an A100 board; it even has NVLink connectors.
Online, I found almost no information about how to run it, whether it works with LLMs, or if it's supported by default Nvidia drivers and CUDA. So, I decided to test it myself.
I installed it in my lab (see previous post https://huggingface.co/posts/kostakoff/584269728210158) and found that the default nvidia-driver-570 works with it out of the box. After that, I checked if CUDA was available, and it worked too.
The next step was to try running some models:
- Stable Diffusion XL with BNB4 quantization: It took around two minutes to generate an image, but it works!
- Compiled llama.cpp for CUDA (https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#compilation): I run Mistral 7B Q4_K_M, and this actually worked even better. It was able to generate 33 tokens per second and read 400 tokens per second.
There are some limitations related to power utilization:
- When running PyTorch, it doesn't utilize more than 80 watts.
- When running llama.cpp, utilization is a bit better but still limited to 113 watts.
I found this GitHub thread about the Nvidia CMP https://github.com/dartraiden/NVIDIA-patcher/issues/73, and it looks like this mining GPU has an internal rate limiter based on FMA compute calls. I haven't found a solution to bypass it yet.
llmlaba
To satisfy my curiosity, I investigated different GPUs and found this: a mining version of the A100 — the CMP 170HX.
It is a very interesting GPU. Based on public documentation, it has hardware similar to the datacenter A100. If you open it up and look at the board, you will see that it's very similar to an A100 board; it even has NVLink connectors.
Online, I found almost no information about how to run it, whether it works with LLMs, or if it's supported by default Nvidia drivers and CUDA. So, I decided to test it myself.
I installed it in my lab (see previous post https://huggingface.co/posts/kostakoff/584269728210158) and found that the default nvidia-driver-570 works with it out of the box. After that, I checked if CUDA was available, and it worked too.
The next step was to try running some models:
- Stable Diffusion XL with BNB4 quantization: It took around two minutes to generate an image, but it works!
- Compiled llama.cpp for CUDA (https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#compilation): I run Mistral 7B Q4_K_M, and this actually worked even better. It was able to generate 33 tokens per second and read 400 tokens per second.
There are some limitations related to power utilization:
- When running PyTorch, it doesn't utilize more than 80 watts.
- When running llama.cpp, utilization is a bit better but still limited to 113 watts.
I found this GitHub thread about the Nvidia CMP https://github.com/dartraiden/NVIDIA-patcher/issues/73, and it looks like this mining GPU has an internal rate limiter based on FMA compute calls. I haven't found a solution to bypass it yet.
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Love it. I've been looking at these, and am glad there are some trials going.
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Space secrets security update
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At Ai4Privacy, our goal is to empower researchers to build a safer AI ecosystem. Today, we're highlighting crucial research that does just that by exposing a new vulnerability.
The paper "Forget to Flourish" details a new model poisoning technique. It's a reminder that as we fine-tune LLMs, our anonymization and privacy strategies must evolve to counter increasingly sophisticated threats.
We're proud that the Ai4Privacy dataset was instrumental in this study. It served two key purposes:
Provided a Realistic Testbed: It gave the researchers access to a diverse set of synthetic and realistic PII samples in a safe, controlled environment.
Enabled Impactful Benchmarking: It allowed them to measure the actual effectiveness of their data extraction attack, proving it could compromise specific, high-value information.
This work reinforces our belief that progress in AI security is a community effort. By providing robust tools for benchmarking, we can collectively identify weaknesses and build stronger, more resilient systems. A huge congratulations to the authors on this important contribution.
🔗 Read the full paper: https://arxiv.org/html/2408.17354v1
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
The paper "Forget to Flourish" details a new model poisoning technique. It's a reminder that as we fine-tune LLMs, our anonymization and privacy strategies must evolve to counter increasingly sophisticated threats.
We're proud that the Ai4Privacy dataset was instrumental in this study. It served two key purposes:
Provided a Realistic Testbed: It gave the researchers access to a diverse set of synthetic and realistic PII samples in a safe, controlled environment.
Enabled Impactful Benchmarking: It allowed them to measure the actual effectiveness of their data extraction attack, proving it could compromise specific, high-value information.
This work reinforces our belief that progress in AI security is a community effort. By providing robust tools for benchmarking, we can collectively identify weaknesses and build stronger, more resilient systems. A huge congratulations to the authors on this important contribution.
🔗 Read the full paper: https://arxiv.org/html/2408.17354v1
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset