Anzhc/Anzhcs_YOLOs
Object Detection
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odyss3y
odyss3y
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AI & ML interests
Long term goal of using technology in the home to improve elderly care. Short term, I like to make AI art with Stable Diffusion using LLM's for prompt automation. Cybersecurity/DFIR background and generally enjoy breaking stuff to see how it works.
Recent Activity
liked
a model
4 months ago
Anzhc/Anzhcs_YOLOs
reacted
to
as-cle-bert's
post
with 🔥
4 months ago
Hi HuggingFacers!🤗
As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows.
I try to explain the state of AI energy consumptions, its environmental impact and the key points of "turning AI nuclear" in my last article on HF community blog: https://huggingface.co/blog/as-cle-bert/ai-is-turning-nuclear-a-review
Enjoy the reading!🌱
reacted
to
as-cle-bert's
post
with 🚀
4 months ago
Hi HuggingFacers!🤗
As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows.
I try to explain the state of AI energy consumptions, its environmental impact and the key points of "turning AI nuclear" in my last article on HF community blog: https://huggingface.co/blog/as-cle-bert/ai-is-turning-nuclear-a-review
Enjoy the reading!🌱
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odyss3y's activity
liked
a
model
4 months ago
reacted to
as-cle-bert's
post with 🔥🚀
4 months ago
Post
3217
Hi HuggingFacers!🤗
As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows.
I try to explain the state of AI energy consumptions, its environmental impact and the key points of "turning AI nuclear" in my last article on HF community blog: https://huggingface.co/blog/as-cle-bert/ai-is-turning-nuclear-a-review
Enjoy the reading!🌱
As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows.
I try to explain the state of AI energy consumptions, its environmental impact and the key points of "turning AI nuclear" in my last article on HF community blog: https://huggingface.co/blog/as-cle-bert/ai-is-turning-nuclear-a-review
Enjoy the reading!🌱
How did you quatize it?
1
#1 opened 4 months ago
by
P00j4n
liked
a
model
5 months ago
what is the meaning of these suffix
12
#3 opened 10 months ago
by
zenwangzy24
liked
2
models
6 months ago
reacted to
Taylor658's
post with 👀
6 months ago
Post
2354
💡Andrew Ng recently gave a strong defense of Open Source AI models and the need to slow down legislative efforts in the US and the EU to restrict innovation in Open Source AI at Stanford GSB.
🎥See video below
https://youtu.be/yzUdmwlh1sQ?si=bZc690p8iubolXm_
🎥See video below
https://youtu.be/yzUdmwlh1sQ?si=bZc690p8iubolXm_
- 4 replies
updated
2
models
6 months ago
reacted to
Csplk's
post with ➕👀
6 months ago
Post
2532
# Offensive Security Reconnaissance Continued with Public Facing Industrial Control System HMIs using Moondream
Building on my previous experiments with Moondream for physical security reconnaissance planning automation (https://huggingface.co/posts/Csplk/926337297827024), I've now turned my attention to exploring the potential of this powerful image-text-text model for offensive security reconnaissance in the realm of Industrial Control Systems (ICS).
ICS HMIs (Human-Machine Interfaces) are increasingly exposed to the public internet, often without adequate security measures in place. This presents a tantalizing opportunity for malicious actors to exploit vulnerabilities and gain unauthorized access to critical infrastructure.
Using Moondream with batch processing ( Csplk/moondream2-batch-processing), I've been experimenting with analyzing public facing ICS ( Csplk/ICS_UIs) HMI ( Csplk/HMI) screenshots from shodan to identify types of exposed ICS system HMIs, how they are operated and how malicious actors with access to these systems could cause damage to physical infrastructure. Feeding images of HMIs and pre-defined text prompts to Moondream batch processing successfully (unconfirmed accuracy levels) extracted information about the underlying systems, including
1. **System type**
2. **Possible Operation Details**
3. **Malicious Actor Outcomes**
Next steps:
* I have a longer and more in depth blog write up in the works that will cover the previous and this post's approaches for experiments for sharing via HF community blog posts soon.
* I plan to continue refining my Moondream-based tool to improve its accuracy and effectiveness in processing public facing ICS HMIs.
* As mentioned before, offensive security with moondream focused HF Space once its fleshed out.
Thanks again to @vikhyatk for the incredible Moondream model. vikhyatk/moondream2
Building on my previous experiments with Moondream for physical security reconnaissance planning automation (https://huggingface.co/posts/Csplk/926337297827024), I've now turned my attention to exploring the potential of this powerful image-text-text model for offensive security reconnaissance in the realm of Industrial Control Systems (ICS).
ICS HMIs (Human-Machine Interfaces) are increasingly exposed to the public internet, often without adequate security measures in place. This presents a tantalizing opportunity for malicious actors to exploit vulnerabilities and gain unauthorized access to critical infrastructure.
Using Moondream with batch processing ( Csplk/moondream2-batch-processing), I've been experimenting with analyzing public facing ICS ( Csplk/ICS_UIs) HMI ( Csplk/HMI) screenshots from shodan to identify types of exposed ICS system HMIs, how they are operated and how malicious actors with access to these systems could cause damage to physical infrastructure. Feeding images of HMIs and pre-defined text prompts to Moondream batch processing successfully (unconfirmed accuracy levels) extracted information about the underlying systems, including
1. **System type**
2. **Possible Operation Details**
3. **Malicious Actor Outcomes**
Next steps:
* I have a longer and more in depth blog write up in the works that will cover the previous and this post's approaches for experiments for sharing via HF community blog posts soon.
* I plan to continue refining my Moondream-based tool to improve its accuracy and effectiveness in processing public facing ICS HMIs.
* As mentioned before, offensive security with moondream focused HF Space once its fleshed out.
Thanks again to @vikhyatk for the incredible Moondream model. vikhyatk/moondream2
Feedback
7
#3 opened 6 months ago
by
Darkknight535
reacted to
Lewdiculous's
post with 🚀
9 months ago
Post
47746
More context for your Pascal GPU or older!
Update: Now available in the official releases of KoboldCpp!
[releases] https://github.com/LostRuins/koboldcpp/releases/latest
These are great news for all the users with GTX 10XX, P40...
Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama.cpp in the last few days, and should be merged in the next version of KoboldCpp, you can already try it with another fork or by building it.
[Mentioned KCPP fork] https://github.com/Nexesenex/kobold.cpp/releases/latest
[PR] https://github.com/ggerganov/llama.cpp/pull/7188
You should expect less VRAM usage for the same context, allowing you to experience higher contexts with your current GPU.
There have also been reported final tokens/second speed improvements for inference, so that's also grand!
If you have tried it, I'd like to hear your experiences with --flashattention so far, especially for this implementation and for the large number of Pascal (GTX 10XX, P40...) cards.
Discussion linked bellow, with more links to relevant information:
https://huggingface.co/LWDCLS/LLM-Discussions/discussions/11
Cheers!
Update: Now available in the official releases of KoboldCpp!
[releases] https://github.com/LostRuins/koboldcpp/releases/latest
These are great news for all the users with GTX 10XX, P40...
Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama.cpp in the last few days, and should be merged in the next version of KoboldCpp, you can already try it with another fork or by building it.
[Mentioned KCPP fork] https://github.com/Nexesenex/kobold.cpp/releases/latest
[PR] https://github.com/ggerganov/llama.cpp/pull/7188
You should expect less VRAM usage for the same context, allowing you to experience higher contexts with your current GPU.
There have also been reported final tokens/second speed improvements for inference, so that's also grand!
If you have tried it, I'd like to hear your experiences with --flashattention so far, especially for this implementation and for the large number of Pascal (GTX 10XX, P40...) cards.
Discussion linked bellow, with more links to relevant information:
https://huggingface.co/LWDCLS/LLM-Discussions/discussions/11
Cheers!
- 24 replies
reacted to
codelion's
post with 👍
9 months ago
Post
1790
The new gpt-4o model seems to a very good coder. OpenAI reported a 90+ score on https://huggingface.co/datasets/openai_humaneval
We tried the new model on our patched-codes/static-analysis-eval which evaluates the model on vulnerability remediation. gpt-4o has reclaimed the top spot on our leaderboard (from meta-llama/Meta-Llama-3-70B-Instruct).
You can now use the new model with our open-source framework PatchWork - https://github.com/patched-codes/patchwork by passing
We tried the new model on our patched-codes/static-analysis-eval which evaluates the model on vulnerability remediation. gpt-4o has reclaimed the top spot on our leaderboard (from meta-llama/Meta-Llama-3-70B-Instruct).
You can now use the new model with our open-source framework PatchWork - https://github.com/patched-codes/patchwork by passing
model=gpt-4o on the CLI.
- 5 replies