janhq/Jan-v3.5-4B-gguf
Text Generation • 4B • Updated • 52.9k • 7
Gary Mitchell
echos-keeper
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reactedto scthornton's post with 🔥 2 months ago
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2185
# SecureCode: Security-Aware Code Models
**A collection of 8 code models (3B–20B) trained to behave like a security reviewer.**
## The Problem
Code assistants frequently recommend patterns that pass tests but fail security review—string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, and more. I built SecureCode to address this gap.
## What SecureCode Does
- **Identify vulnerable patterns** and explain why they're risky
- **Outline plausible abuse paths** (defensive framing)
- **Propose secure rewrites** (drop-in replacements where possible)
- **Include defense-in-depth guidance** + regression tests/checks
## Resources
| Resource | Link |
|----------|------|
| Models | https://huggingface.co/collections/scthornton/securecode |
| Dataset | scthornton/securecode (2,185 examples) |
| Paper | https://arxiv.org/abs/2512.18542 |
## How to Test It
Copy and paste this prompt with your code:
## Dataset Coverage
SecureCode covers both traditional and emerging security domains:
- **Traditional web security** (OWASP Top 10 2021)
- **AI/ML security** (OWASP LLM Top 10 2025): prompt injection, RAG poisoning, model extraction, agentic AI patterns
## We Want Your Feedback
We're looking for real-world contributions:
- **Real snippets**: Share code that "slipped through review once" (sanitized is fine)
- **False positives/negatives**: What didn't work as expected?
- **CVE-grounded examples**: New vulnerability patterns you've encountered
**Please include**: language/framework + what the correct remediation looks like in your environment.
---
**Have contributions or suggestions?** I'd be happy to hear them. Thanks for your support!
**A collection of 8 code models (3B–20B) trained to behave like a security reviewer.**
## The Problem
Code assistants frequently recommend patterns that pass tests but fail security review—string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, and more. I built SecureCode to address this gap.
## What SecureCode Does
- **Identify vulnerable patterns** and explain why they're risky
- **Outline plausible abuse paths** (defensive framing)
- **Propose secure rewrites** (drop-in replacements where possible)
- **Include defense-in-depth guidance** + regression tests/checks
## Resources
| Resource | Link |
|----------|------|
| Models | https://huggingface.co/collections/scthornton/securecode |
| Dataset | scthornton/securecode (2,185 examples) |
| Paper | https://arxiv.org/abs/2512.18542 |
## How to Test It
Copy and paste this prompt with your code:
You are a senior application security engineer. Review the code below.
Output:
(1) findings with severity,
(2) likely exploit scenarios (high level),
(3) secure rewrite,
(4) defense-in-depth recommendations,
(5) regression tests/checks.
Code: `...`## Dataset Coverage
SecureCode covers both traditional and emerging security domains:
- **Traditional web security** (OWASP Top 10 2021)
- **AI/ML security** (OWASP LLM Top 10 2025): prompt injection, RAG poisoning, model extraction, agentic AI patterns
## We Want Your Feedback
We're looking for real-world contributions:
- **Real snippets**: Share code that "slipped through review once" (sanitized is fine)
- **False positives/negatives**: What didn't work as expected?
- **CVE-grounded examples**: New vulnerability patterns you've encountered
**Please include**: language/framework + what the correct remediation looks like in your environment.
---
**Have contributions or suggestions?** I'd be happy to hear them. Thanks for your support!
reactedto consome2's post with ❤️ 2 months ago
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5259
We’ve released two conversational speech datasets from oto on Hugging Face 🤗
Both are based on real, casual, full-duplex conversations, but with slightly different focuses.
Dataset 1: Processed / curated subset
otoearth/otoSpeech-full-duplex-processed-141h
* Full-duplex, spontaneous multi-speaker conversations
* Participants filtered for high audio quality
* PII removal and audio enhancement applied
* Designed for training and benchmarking S2S or dialogue models
Dataset 2: Larger raw(er) release
otoearth/otoSpeech-full-duplex-280h
* Same collection pipeline, with broader coverage
* More diversity in speakers, accents, and conversation styles
* Useful for analysis, filtering, or custom preprocessing experiments
We intentionally split the release to support different research workflows:
clean and ready-to-use vs. more exploratory and research-oriented use.
The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested.
If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together.
Feedback and ideas are very welcome!
Both are based on real, casual, full-duplex conversations, but with slightly different focuses.
Dataset 1: Processed / curated subset
otoearth/otoSpeech-full-duplex-processed-141h
* Full-duplex, spontaneous multi-speaker conversations
* Participants filtered for high audio quality
* PII removal and audio enhancement applied
* Designed for training and benchmarking S2S or dialogue models
Dataset 2: Larger raw(er) release
otoearth/otoSpeech-full-duplex-280h
* Same collection pipeline, with broader coverage
* More diversity in speakers, accents, and conversation styles
* Useful for analysis, filtering, or custom preprocessing experiments
We intentionally split the release to support different research workflows:
clean and ready-to-use vs. more exploratory and research-oriented use.
The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested.
If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together.
Feedback and ideas are very welcome!
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instruct version?
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#1 opened 3 months ago
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thanks for uploading as safetensors!
#1 opened 4 months ago
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