sphiratrioth666/SillyTavern-Presets-Sphiratrioth
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odyss3y
odyss3y
AI & ML interests
Red-teaming and bias testing LLMs with a focus on refusals, alignment cracks, and narrative integrity (Banes Benchmark). Cybersecurity/DFIR background, SDXL tinkerer, long-term interest in multimodal tech for home and elder care.
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sphiratrioth666/SillyTavern-Presets-Sphiratrioth
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QuantFactory/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-GGUF:🚩 Report: Spam
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🚩 Report: Spam
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#5 opened 3 months ago
by
pcking
reacted to
mitkox's
post with 🔥
3 months ago
Post
3447
XBai o4 claims to beat Claude Opus 4 and o3-mini, and they provide verifiable proof. My skepticism circuits overloaded, but my local AI FOMO module screamed louder.
I've thrown this 33B monoblock LLM onto a single GPU and used Roo Code for some… let’s call it “vibe testing”. It’s terrifyingly competent. As an architect, it’s the best open-weight model I’ve touched this side of 2025.
I've thrown this 33B monoblock LLM onto a single GPU and used Roo Code for some… let’s call it “vibe testing”. It’s terrifyingly competent. As an architect, it’s the best open-weight model I’ve touched this side of 2025.
- 1 reply
Update README.md
1
#2 opened 3 months ago
by
odyss3y
liked
a
model
6 months ago
reacted to
fdaudens's
post with 🔥
8 months ago
Post
2341
Photoshopping will never be the same. Gemini 2.0 Flash in a Gradio app = 🤯
ameerazam08/Gemini-Image-Edit
ameerazam08/Gemini-Image-Edit
reacted to
Kseniase's
post with 👍
9 months ago
Post
9890
8 Free Sources about AI Agents:
Agents seem to be everywhere and this collection is for a deep dive into the theory and practice:
1. "Agents" Google's whitepaper by Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic -> https://www.kaggle.com/whitepaper-agents
Covers agents, their functions, tool use and how they differ from models
2. "Agents in the Long Game of AI. Computational Cognitive Modeling for Trustworthy, Hybrid AI" book by Marjorie McShane, Sergei Nirenburg, and Jesse English -> https://direct.mit.edu/books/oa-monograph/5833/Agents-in-the-Long-Game-of-AIComputational
Explores building AI agents, using Hybrid AI, that combines ML with knowledge-based reasoning
3. "AI Engineer Summit 2025: Agent Engineering" 8-hour video -> https://www.youtube.com/watch?v=D7BzTxVVMuw
Experts' talks that share insights on the freshest Agent Engineering advancements, such as Google Deep Research, scaling tips and more
4. AI Agents Course from Hugging Face -> https://huggingface.co/learn/agents-course/en/unit0/introduction
Agents' theory and practice to learn how to build them using top libraries and tools
5. "Artificial Intelligence: Foundations of Computational Agents", 3rd Edition, book by David L. Poole and Alan K. Mackworth -> https://artint.info/3e/html/ArtInt3e.html
Agents' architectures, how they learn, reason, plan and act with certainty and uncertainty
6. "Intelligent Agents: Theory and Practice" book by Michael Wooldridge -> https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/ker95/ker95-html.html
A fascinating option to dive into how agents were seen in 1995 and explore their theory, architectures and agent languages
7. The Turing Post articles "AI Agents and Agentic Workflows" on Hugging Face -> @Kseniase
We explore agentic workflows in detail and agents' building blocks, such as memory and knowledge
8. Our collection "8 Free Sources to Master Building AI Agents" -> https://www.turingpost.com/p/building-ai-agents-sources
Agents seem to be everywhere and this collection is for a deep dive into the theory and practice:
1. "Agents" Google's whitepaper by Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic -> https://www.kaggle.com/whitepaper-agents
Covers agents, their functions, tool use and how they differ from models
2. "Agents in the Long Game of AI. Computational Cognitive Modeling for Trustworthy, Hybrid AI" book by Marjorie McShane, Sergei Nirenburg, and Jesse English -> https://direct.mit.edu/books/oa-monograph/5833/Agents-in-the-Long-Game-of-AIComputational
Explores building AI agents, using Hybrid AI, that combines ML with knowledge-based reasoning
3. "AI Engineer Summit 2025: Agent Engineering" 8-hour video -> https://www.youtube.com/watch?v=D7BzTxVVMuw
Experts' talks that share insights on the freshest Agent Engineering advancements, such as Google Deep Research, scaling tips and more
4. AI Agents Course from Hugging Face -> https://huggingface.co/learn/agents-course/en/unit0/introduction
Agents' theory and practice to learn how to build them using top libraries and tools
5. "Artificial Intelligence: Foundations of Computational Agents", 3rd Edition, book by David L. Poole and Alan K. Mackworth -> https://artint.info/3e/html/ArtInt3e.html
Agents' architectures, how they learn, reason, plan and act with certainty and uncertainty
6. "Intelligent Agents: Theory and Practice" book by Michael Wooldridge -> https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/ker95/ker95-html.html
A fascinating option to dive into how agents were seen in 1995 and explore their theory, architectures and agent languages
7. The Turing Post articles "AI Agents and Agentic Workflows" on Hugging Face -> @Kseniase
We explore agentic workflows in detail and agents' building blocks, such as memory and knowledge
8. Our collection "8 Free Sources to Master Building AI Agents" -> https://www.turingpost.com/p/building-ai-agents-sources
- 5 replies
liked
a
model
about 1 year ago
reacted to
as-cle-bert's
post with 🔥🚀
about 1 year ago
Post
3221
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 about 1 year ago
by
P00j4n
liked
a
model
about 1 year ago
what is the meaning of these suffix
👍
2
13
#3 opened over 1 year ago
by
zenwangzy24
liked
2
models
about 1 year ago
reacted to
Taylor658's
post with 👀
about 1 year ago
Post
2383
💡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
about 1 year ago
reacted to
Csplk's
post with ➕👀
about 1 year ago
Post
2860
# 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