tegridydev (GO:OD:AM)

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Open Source AI Agents | Github/Repo List | [2025]

https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory

Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)

commented on Open Source AI Agents | Github/Repo List | [2025] 1 day ago

Added :D

reacted to their post with ❤️ 2 days ago

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1926

Open Source AI Agents | Github/Repo List | [2025]

https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory

Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)

posted an update 2 days ago

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1926

Open Source AI Agents | Github/Repo List | [2025]

https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory

Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)

upvoted an article 2 days ago

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Open Source AI Agents | Github/Repo List | [2025]

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published an article 2 days ago

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Open Source AI Agents | Github/Repo List | [2025]

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reacted to their post with ❤️ 6 days ago

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1892

WTF is Fine-Tuning? (intro4devs)

Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃

Here's a cheat sheet for devs (but open to anyone!)

---

TL;DR

- Full Fine-Tuning: Max performance, high resource needs, best reliability.
- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
- RAFT: Best for fact-grounded models with dynamic retrieval.
- RLHF: Produces ethical, high-quality conversational AI, but expensive.

Choose wisely and match your approach to your task, budget, and deployment constraints.

I just posted the full extended article here
if you want to continue reading >>>

https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025

posted an update 6 days ago

Post

1892

WTF is Fine-Tuning? (intro4devs)

Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃

Here's a cheat sheet for devs (but open to anyone!)

---

TL;DR

- Full Fine-Tuning: Max performance, high resource needs, best reliability.
- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
- RAFT: Best for fact-grounded models with dynamic retrieval.
- RLHF: Produces ethical, high-quality conversational AI, but expensive.

Choose wisely and match your approach to your task, budget, and deployment constraints.

I just posted the full extended article here
if you want to continue reading >>>

https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025

upvoted an article 6 days ago

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WTF is Fine-Tuning? (intro4devs) | [2025]

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WTF is Fine-Tuning? (intro4devs) | [2025]

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upvoted an article 22 days ago

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LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs

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published an article 22 days ago

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LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs

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reacted to their post with ❤️ 22 days ago

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1437

Open-MalSec v0.1 – Open-Source Cybersecurity Dataset

Evening! 🫡

📂 Just uploaded an early-stage open-source cybersecurity dataset focused on phishing, scams, and malware-related text samples.

This is the base version (v0.1)—a few structured sample files. Full dataset builds will come over the next few weeks.

🔗 Dataset link:

tegridydev/open-malsec

🔍 What’s in v0.1?
A few structured scam examples (text-based)
Covers DeFi, crypto, phishing, and social engineering
Initial labelling format for scam classification

⚠️ This is not a full dataset yet (samples are currently available). Just establishing the structure + getting feedback.

📂 Current Schema & Labelling Approach
"instruction" → Task prompt (e.g., "Evaluate this message for scams")
"input" → Source & message details (e.g., Telegram post, Tweet)
"output" → Scam classification & risk indicators

🗂️ Current v0.1 Sample Categories
Crypto Scams → Meme token pump & dumps, fake DeFi projects
Phishing → Suspicious finance/social media messages
Social Engineering → Manipulative messages exploiting trust

🔜 Next Steps
- Expanding datasets with more phishing & malware examples
- Refining schema & annotation quality
- Open to feedback, contributions, and suggestions

If this is something you might find useful, bookmark/follow/like the dataset repo <3

💬 Thoughts, feedback, and ideas are always welcome! Drop a comment or DMs are open 🤙

posted an update 23 days ago

Post

1437

Open-MalSec v0.1 – Open-Source Cybersecurity Dataset

Evening! 🫡

📂 Just uploaded an early-stage open-source cybersecurity dataset focused on phishing, scams, and malware-related text samples.

This is the base version (v0.1)—a few structured sample files. Full dataset builds will come over the next few weeks.

🔗 Dataset link:

tegridydev/open-malsec

🔍 What’s in v0.1?
A few structured scam examples (text-based)
Covers DeFi, crypto, phishing, and social engineering
Initial labelling format for scam classification

⚠️ This is not a full dataset yet (samples are currently available). Just establishing the structure + getting feedback.

📂 Current Schema & Labelling Approach
"instruction" → Task prompt (e.g., "Evaluate this message for scams")
"input" → Source & message details (e.g., Telegram post, Tweet)
"output" → Scam classification & risk indicators

🗂️ Current v0.1 Sample Categories
Crypto Scams → Meme token pump & dumps, fake DeFi projects
Phishing → Suspicious finance/social media messages
Social Engineering → Manipulative messages exploiting trust

🔜 Next Steps
- Expanding datasets with more phishing & malware examples
- Refining schema & annotation quality
- Open to feedback, contributions, and suggestions

If this is something you might find useful, bookmark/follow/like the dataset repo <3

💬 Thoughts, feedback, and ideas are always welcome! Drop a comment or DMs are open 🤙

reacted to their post with 👀 24 days ago

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1398

So, what is #MechanisticInterpretability 🤔

Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours

Instead of treating a model as a monolithic function, we can:

1. Trace how input tokens propagate through attention heads & MLP layers
2. Identify localized “circuit motifs”
3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure.

Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to

1. Trust & Reliability
2. Safety & Alignment
3. Better Debugging / Development Insights

https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x

1 reply

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liked a model 24 days ago

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Text Generation • Updated 14 days ago • 1.09M • • 916

updated a dataset 24 days ago

tegridydev/open-malsec

Updated 24 days ago • 164 • 6

liked a model 24 days ago

PowerInfer/SmallThinker-3B-Preview

Text Generation • Updated Jan 16 • 109k • • 386

posted an update 24 days ago

Post

1398

So, what is #MechanisticInterpretability 🤔

Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours

Instead of treating a model as a monolithic function, we can:

1. Trace how input tokens propagate through attention heads & MLP layers
2. Identify localized “circuit motifs”
3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure.

Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to

1. Trust & Reliability
2. Safety & Alignment
3. Better Debugging / Development Insights

https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x

1 reply

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tegridydev's activity

Open Source AI Agents | Github/Repo List | [2025]

Open Source AI Agents | Github/Repo List | [2025]

WTF is Fine-Tuning? (intro4devs) | [2025]

WTF is Fine-Tuning? (intro4devs) | [2025]

LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs

LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

tegridydev/open-malsec

PowerInfer/SmallThinker-3B-Preview