unsloth/GLM-4.7-Flash-GGUF
Text Generation
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30B
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Updated
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54k
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pascalmusabyimana
pascal-maker
AI & ML interests
computer vision, nlp , machine learning and deeplearning
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π OpenMed 2025 Year in Review: 6 Months of Open Medical AI
I'm thrilled to share what the OpenMed community has accomplished since our July 2025 launch!
π The Numbers
29,700,000 downloads Thank you! π
- 481 total models (475 medical NER models + 6 fine-tuned LLMs)
- 475 medical NER models in [OpenMed](
OpenMed
) organization
- 6 fine-tuned LLMs in [openmed-community](
openmed-community
)
- 551,800 PyPI downloads of the [openmed package](https://pypi.org/project/openmed/)
- 707 followers on HuggingFace (you!)
- 97 GitHub stars on the [toolkit repo](https://github.com/maziyarpanahi/openmed)
π Top Models by Downloads
1. [OpenMed-NER-PharmaDetect-SuperClinical-434M]( OpenMed/OpenMed-NER-PharmaDetect-SuperClinical-434M) β 147,305 downloads
2. [OpenMed-NER-ChemicalDetect-ElectraMed-33M]( OpenMed/OpenMed-NER-ChemicalDetect-ElectraMed-33M) β 126,785 downloads
3. [OpenMed-NER-BloodCancerDetect-TinyMed-65M]( OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M) β 126,465 downloads
π¬ Model Categories
Our 481 models cover comprehensive medical domains:
- Disease Detection (~50 variants)
- Pharmaceutical Detection (~50 variants)
- Oncology Detection (~50 variants)
- Genomics/DNA Detection (~80 variants)
- Chemical Detection (~50 variants)
- Species/Organism Detection (~60 variants)
- Protein Detection (~50 variants)
- Pathology Detection (~50 variants)
- Blood Cancer Detection (~30 variants)
- Anatomy Detection (~40 variants)
- Zero-Shot NER (GLiNER-based)
OpenMed
OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets (2508.01630)
https://huggingface.co/collections/OpenMed/medical-and-clinical-ner
https://huggingface.co/collections/OpenMed/zeroshot-medical-and-clinical-ner
OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B
I'm thrilled to share what the OpenMed community has accomplished since our July 2025 launch!
π The Numbers
29,700,000 downloads Thank you! π
- 481 total models (475 medical NER models + 6 fine-tuned LLMs)
- 475 medical NER models in [OpenMed](
- 6 fine-tuned LLMs in [openmed-community](
- 551,800 PyPI downloads of the [openmed package](https://pypi.org/project/openmed/)
- 707 followers on HuggingFace (you!)
- 97 GitHub stars on the [toolkit repo](https://github.com/maziyarpanahi/openmed)
π Top Models by Downloads
1. [OpenMed-NER-PharmaDetect-SuperClinical-434M]( OpenMed/OpenMed-NER-PharmaDetect-SuperClinical-434M) β 147,305 downloads
2. [OpenMed-NER-ChemicalDetect-ElectraMed-33M]( OpenMed/OpenMed-NER-ChemicalDetect-ElectraMed-33M) β 126,785 downloads
3. [OpenMed-NER-BloodCancerDetect-TinyMed-65M]( OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M) β 126,465 downloads
π¬ Model Categories
Our 481 models cover comprehensive medical domains:
- Disease Detection (~50 variants)
- Pharmaceutical Detection (~50 variants)
- Oncology Detection (~50 variants)
- Genomics/DNA Detection (~80 variants)
- Chemical Detection (~50 variants)
- Species/Organism Detection (~60 variants)
- Protein Detection (~50 variants)
- Pathology Detection (~50 variants)
- Blood Cancer Detection (~30 variants)
- Anatomy Detection (~40 variants)
- Zero-Shot NER (GLiNER-based)
OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets (2508.01630)
https://huggingface.co/collections/OpenMed/medical-and-clinical-ner
https://huggingface.co/collections/OpenMed/zeroshot-medical-and-clinical-ner
OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B
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Tokenization in Transformers v5: Simpler, Clearer, and More Modular
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β
New Article: *Pattern-Learning-Bridge (PLB)*
Title:
𧩠Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures
π https://huggingface.co/blog/kanaria007/learns-from-its-own-failures
---
Summary:
Most stacks βlearnβ by fine-tuning weights and redeploying β powerful, but opaque.
SI-Core already produces *structured evidence* (jump logs, ethics traces, effect ledgers, goal vectors, rollback traces), so learning can be *structural* instead:
*Upgrade policies, compensators, SIL code, and goal structures β using runtime evidence.*
> Learning isnβt a model tweak.
> *Itβs upgrading the structures that shape behavior.*
---
Why It Matters:
β’ Makes improvement *localized and explainable* (what changed, where, and why)
β’ Keeps βself-improvementβ *governable* (versioned deltas + review + CI/CD)
β’ Turns incidents/metric drift into *actionable patches*, not postmortem PDFs
β’ Scales to real ops: ethics policies, rollback plans, semantic compression, goal estimators
---
Whatβs Inside:
β’ What βlearningβ means in SI-Core (and what changes vs. classic ML)
β’ The *Pattern-Learning-Bridge*: where it sits between runtime evidence and governed code
β’ Safety properties: PLB proposes *versioned deltas*, never edits production directly
β’ Validation pipeline: sandbox/simulation β conformance checks β golden diffs β rollout
---
π Structured Intelligence Engineering Series
A non-normative, implementable design for βlearning from failuresβ without sacrificing auditability.
Title:
𧩠Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures
π https://huggingface.co/blog/kanaria007/learns-from-its-own-failures
---
Summary:
Most stacks βlearnβ by fine-tuning weights and redeploying β powerful, but opaque.
SI-Core already produces *structured evidence* (jump logs, ethics traces, effect ledgers, goal vectors, rollback traces), so learning can be *structural* instead:
*Upgrade policies, compensators, SIL code, and goal structures β using runtime evidence.*
> Learning isnβt a model tweak.
> *Itβs upgrading the structures that shape behavior.*
---
Why It Matters:
β’ Makes improvement *localized and explainable* (what changed, where, and why)
β’ Keeps βself-improvementβ *governable* (versioned deltas + review + CI/CD)
β’ Turns incidents/metric drift into *actionable patches*, not postmortem PDFs
β’ Scales to real ops: ethics policies, rollback plans, semantic compression, goal estimators
---
Whatβs Inside:
β’ What βlearningβ means in SI-Core (and what changes vs. classic ML)
β’ The *Pattern-Learning-Bridge*: where it sits between runtime evidence and governed code
β’ Safety properties: PLB proposes *versioned deltas*, never edits production directly
β’ Validation pipeline: sandbox/simulation β conformance checks β golden diffs β rollout
---
π Structured Intelligence Engineering Series
A non-normative, implementable design for βlearning from failuresβ without sacrificing auditability.
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MikeDoes's
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25 days ago
Post
4203
What if an AI agent could be tricked into stealing your data, just by reading a tool's description? A new paper reports it's possible.
The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments.
This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, ιΎζ±δΈ, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents.
π Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110
#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments.
This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, ιΎζ±δΈ, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents.
π Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110
#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
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