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LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
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LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
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LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
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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:
https://huggingface.co/datasets/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 🤙
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Article
LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
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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 🤙
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
6 days ago
Post
1397
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 🤙
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 🤙
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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
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
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updated
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liked
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8 days ago
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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
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
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