zai-org/GLM-OCR
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John Locke
johnlockejrr
AI & ML interests
NLP, OCR, AI
Recent Activity
liked
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zai-org/GLM-OCR
updated
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johnlockejrr/CATMuS-lines
published
a dataset
about 8 hours ago
johnlockejrr/CATMuS-lines
Organizations
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alibidaran's
post with 🔥
about 13 hours ago
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1706
I’m excited to share PlaiTO, a reasoning-focused language model built on LLaMA 3.1 (8B) and optimized for humanities and social sciences.
PlaiTO is designed to go beyond surface-level text generation, emphasizing structured reasoning, conceptual clarity, and analytical depth—especially in domains centered on human behavior and social systems.
🎯 Focus Areas
Psychology
Management & Organizational Studies
Sociology
📊 MMLU Benchmark Results (100 samples per domain)
Professional Psychology: 76%
Management: 74%
Sociology: 75%
These results highlight PlaiTO’s strong performance in abstract, theory-heavy, and reasoning-driven tasks.
💡 Why PlaiTO?
Strong analytical and reasoning capabilities
Better handling of complex human-centered problems
Suitable for academic, educational, and research use cases
Balanced performance across multiple humanities disciplines
PlaiTO is ideal for conceptual analysis, case reasoning, academic discussion, and decision-support scenarios—while still requiring human oversight for high-stakes applications.
📌 Built on LLaMA 3.1, compliant with its licensing terms.
alibidaran/Platio_merged_model
PlaiTO is designed to go beyond surface-level text generation, emphasizing structured reasoning, conceptual clarity, and analytical depth—especially in domains centered on human behavior and social systems.
🎯 Focus Areas
Psychology
Management & Organizational Studies
Sociology
📊 MMLU Benchmark Results (100 samples per domain)
Professional Psychology: 76%
Management: 74%
Sociology: 75%
These results highlight PlaiTO’s strong performance in abstract, theory-heavy, and reasoning-driven tasks.
💡 Why PlaiTO?
Strong analytical and reasoning capabilities
Better handling of complex human-centered problems
Suitable for academic, educational, and research use cases
Balanced performance across multiple humanities disciplines
PlaiTO is ideal for conceptual analysis, case reasoning, academic discussion, and decision-support scenarios—while still requiring human oversight for high-stakes applications.
📌 Built on LLaMA 3.1, compliant with its licensing terms.
alibidaran/Platio_merged_model
updated
a
dataset
1 day ago
published
a
dataset
1 day ago
Ivrit only?
1
#1 opened 3 days ago
by
johnlockejrr
Training Unicode Normalization
#1 opened 3 days ago
by
johnlockejrr
updated
a
model
5 days ago
`max_new_tokens=256`
#1 opened 6 days ago
by
johnlockejrr
reacted to
danielhanchen's
post with 🚀
7 days ago
Post
3350
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
- 7 replies
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scthornton's
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8 days ago
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2141
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!
upvoted
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collection
8 days ago
liked
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model
8 days ago
Experience on finetuning for specific language
2
#13 opened 13 days ago
by
Huy227
vLLM or Llama.cpp output
7
#1 opened 11 days ago
by
johnlockejrr
published
a
model
12 days ago
updated
a
dataset
12 days ago
liked
a
model
13 days ago
LightOnOCR-2-1B with Samaritan Hebrew
❤️
3
#10 opened 13 days ago
by
johnlockejrr