johnlockejrr/syr_serto_synth
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John Locke
johnlockejrr
AI & ML interests
NLP, OCR, AI
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johnlockejrr/syr_serto_synth
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johnlockejrr/syr_serto_synth
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1 day ago
johnlockejrr/LightOnOCR-2-1B-base-samaritan
Organizations
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`max_new_tokens=256`
#1 opened 2 days ago
by
johnlockejrr
reacted to
danielhanchen's
post with 🚀
3 days ago
Post
3182
You can now run Kimi K2.5 locally! 🔥
We shrank the 1T model to 240GB (-60%) via Dynamic 1-bit.
Get >40 tok/s on 242GB or 622GB VRAM/RAM for near full precision.
GGUF: unsloth/Kimi-K2.5-GGUF
Guide: https://unsloth.ai/docs/models/kimi-k2.5
We shrank the 1T model to 240GB (-60%) via Dynamic 1-bit.
Get >40 tok/s on 242GB or 622GB VRAM/RAM for near full precision.
GGUF: unsloth/Kimi-K2.5-GGUF
Guide: https://unsloth.ai/docs/models/kimi-k2.5
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reacted to
scthornton's
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4 days ago
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2108
SecureCode: security-aware code models (3B–20B), trained for review + remediation
I’ve been frustrated by how often code assistants recommend patterns that pass tests but fail security review (e.g., string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, etc.). So I built **SecureCode**: a collection of **8 code models (3B → 20B)** trained to behave more like a security reviewer.
What you should expect from SecureCode:
- identify likely vuln patterns and explain *why* they’re risky
- outline plausible abuse paths (defensive framing)
- propose a secure rewrite (drop-in where possible)
- include defense-in-depth guidance + regression tests/checks
Links:
- **Models:** https://huggingface.co/collections/scthornton/securecode
- **Dataset:** scthornton/securecode-v2
- **Paper:** https://arxiv.org/html/2512.18542v1 SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models (2512.18542)
**How to test it (copy/paste prompt):**
**I’m looking for real-world feedback**
- Your “this slipped through review once” snippets (sanitized is fine)
- False positives / false negatives you observe
- Contributions of new CVE-grounded examples
If you drop a snippet, please include language/framework + what the *correct* remediation looks like in your environment. If you have any contributions or suggestions for the dataset, I'd be happy to hear them. I have some new features and enhancements planned for v3 that are already underway, but for now, I'm focused on testing as many use cases as possible. Appreciate you all!
I’ve been frustrated by how often code assistants recommend patterns that pass tests but fail security review (e.g., string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, etc.). So I built **SecureCode**: a collection of **8 code models (3B → 20B)** trained to behave more like a security reviewer.
What you should expect from SecureCode:
- identify likely vuln patterns and explain *why* they’re risky
- outline plausible abuse paths (defensive framing)
- propose a secure rewrite (drop-in where possible)
- include defense-in-depth guidance + regression tests/checks
Links:
- **Models:** https://huggingface.co/collections/scthornton/securecode
- **Dataset:** scthornton/securecode-v2
- **Paper:** https://arxiv.org/html/2512.18542v1 SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models (2512.18542)
**How to test it (copy/paste prompt):**
> 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: `...`**I’m looking for real-world feedback**
- Your “this slipped through review once” snippets (sanitized is fine)
- False positives / false negatives you observe
- Contributions of new CVE-grounded examples
If you drop a snippet, please include language/framework + what the *correct* remediation looks like in your environment. If you have any contributions or suggestions for the dataset, I'd be happy to hear them. I have some new features and enhancements planned for v3 that are already underway, but for now, I'm focused on testing as many use cases as possible. Appreciate you all!
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4 days ago
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Experience on finetuning for specific language
2
#13 opened 9 days ago
by
Huy227
vLLM or Llama.cpp output
7
#1 opened 7 days ago
by
johnlockejrr
published
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model
8 days ago
updated
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dataset
8 days ago
liked
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9 days ago
LightOnOCR-2-1B with Samaritan Hebrew
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3
#10 opened 9 days ago
by
johnlockejrr
published
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dataset
9 days ago
upvoted
a
paper
9 days ago
Finetune `nvidia/nemotron-ocr-v1` recognition model
5
#7 opened about 1 month ago
by
johnlockejrr
upvoted
an
article
11 days ago
Article
LightOnOCR-1B: The Case for End-to-End and Efficient Domain-Specific Vision-Language Models for OCR
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