arcee-train (Arcee Training Org)

posted an update 4 days ago

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🎉 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!

📊 The Numbers

29,700,000 downloads Thank you! 🙏

- 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)

🏆 Top Models by Downloads

1. [OpenMed-NER-PharmaDetect-SuperClinical-434M]( OpenMed/OpenMed-NER-PharmaDetect-SuperClinical-434M) — 147,305 downloads
2. [OpenMed-NER-ChemicalDetect-ElectraMed-33M]( OpenMed/OpenMed-NER-ChemicalDetect-ElectraMed-33M) — 126,785 downloads
3. [OpenMed-NER-BloodCancerDetect-TinyMed-65M]( OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M) — 126,465 downloads

🔬 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

1 reply

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danielhanchen

posted an update 10 days ago

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Run Qwen-Image-2512, the new SOTA text-to-image model! 💜

It's the top performing open diffusion model and has more realistic + accurate images/text.

Run locally with 14GB RAM via our Dynamic GGUF: unsloth/Qwen-Image-2512-GGUF

Guide: https://unsloth.ai/docs/models/qwen-image-2512

2 replies

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Johannes

authored a paper 15 days ago

INTELLECT-3: Technical Report

Paper • 2512.16144 • Published 23 days ago • 16

danielhanchen

posted an update 18 days ago

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You can now run GLM-4.7, the new 355B parameter SOTA model on your local device (128GB RAM).✨

The model achieves SOTA performance on coding, agentic and chat benchmarks.

GGUF: unsloth/GLM-4.7-GGUF
Guide: https://docs.unsloth.ai/models/glm-4.7

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danielhanchen

posted an update 22 days ago

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Google releases FunctionGemma, a new 270M parameter model that runs on just 0.5 GB RAM.✨

Built for tool-calling, run locally on your phone at 50+ tokens/s, or fine-tune with Unsloth & deploy to your phone.

GGUF: unsloth/functiongemma-270m-it-GGUF
Docs + Notebook: https://docs.unsloth.ai/models/functiongemma

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danielhanchen

posted an update 26 days ago

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NVIDIA releases Nemotron 3 Nano, a new 30B hybrid reasoning model! 🔥

Has 1M context window & best in class performance for SWE-Bench, reasoning & chat. Run the MoE model locally with 24GB RAM.

GGUF: unsloth/Nemotron-3-Nano-30B-A3B-GGUF
💚 Step-by-step Guide: https://docs.unsloth.ai/models/nemotron-3

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danielhanchen

posted an update 30 days ago

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Mistral's new SOTA coding models Devstral 2 can now be Run locally! (25GB RAM) 🐱
We fixed the chat template, so performance should be much better now!
24B: unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF
123B: unsloth/Devstral-2-123B-Instruct-2512-GGUF

🧡Step-by-step Guide: https://docs.unsloth.ai/models/devstral-2

danielhanchen

posted an update about 1 month ago

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Mistral's new Ministral 3 models can now be Run & Fine-tuned locally! (16GB RAM)
Ministral 3 have vision support and the best-in-class performance for their sizes.
14B Instruct GGUF: unsloth/Ministral-3-14B-Instruct-2512-GGUF
14B Reasoning GGUF: unsloth/Ministral-3-14B-Reasoning-2512-GGUF

🐱 Step-by-step Guide: https://docs.unsloth.ai/new/ministral-3
All GGUFs, BnB, FP8 etc. variants uploads: https://huggingface.co/collections/unsloth/ministral-3

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danielhanchen

posted an update about 1 month ago

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Qwen3-Next can now be Run locally! (30GB RAM)
Instruct GGUF: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF

The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B.
💜 Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next

Thinking GGUF: unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF

danielhanchen

posted an update 2 months ago

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You can now run Kimi K2 Thinking locally with our Dynamic 1-bit GGUFs: unsloth/Kimi-K2-Thinking-GGUF

We shrank the 1T model to 245GB (-62%) & retained ~85% of accuracy on Aider Polyglot. Run on >247GB RAM for fast inference.

We also collaborated with the Moonshot AI Kimi team on a system prompt fix! 🥰

Guide + fix details: https://docs.unsloth.ai/models/kimi-k2-thinking-how-to-run-locally

danielhanchen

posted an update 5 months ago

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Run DeepSeek-V3.1 locally on 170GB RAM with Dynamic 1-bit GGUFs!🐋
GGUFs: unsloth/DeepSeek-V3.1-GGUF

The 715GB model gets reduced to 170GB (-80% size) by smartly quantizing layers.

The 1-bit GGUF passes all our code tests & we fixed the chat template for llama.cpp supported backends.

Guide: https://docs.unsloth.ai/basics/deepseek-v3.1

MaziyarPanahi

authored 2 papers 5 months ago

INTELLECT-1 Technical Report

Paper • 2412.01152 • Published Dec 2, 2024 • 3

OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets

Paper • 2508.01630 • Published Aug 3, 2025 • 12

danielhanchen

posted an update 5 months ago

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Run OpenAI's new gpt-oss models locally with Unsloth GGUFs! 🔥🦥
20b GGUF: unsloth/gpt-oss-20b-GGUF
120b GGUF: unsloth/gpt-oss-120b-GGUF

Model will run on 14GB RAM for 20b and 66GB for 120b.

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danielhanchen

posted an update 6 months ago

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It's Qwen3 week! 💜 We uploaded Dynamic 2-bit GGUFs for:

Qwen3-Coder: unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF
Qwen3-2507: unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF

So you can run them both locally!
Guides are in model cards.

1 reply

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MaziyarPanahi

posted an update 6 months ago

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🧬 Breaking news in Clinical AI: Introducing the OpenMed NER Model Discovery App on Hugging Face 🔬

OpenMed is back! 🔥 Finding the right biomedical NER model just became as precise as a PCR assay!

I'm thrilled to unveil my comprehensive OpenMed Named Entity Recognition Model Discovery App that puts 384 specialized biomedical AI models at your fingertips.

🎯 Why This Matters in Healthcare AI:
Traditional clinical text mining required hours of manual model evaluation. My Discovery App instantly connects researchers, clinicians, and data scientists with the exact NER models they need for their biomedical entity extraction tasks.

🔬 What You Can Discover:
✅ Pharmacological Models - Extract "chemical compounds", "drug interactions", and "pharmaceutical" entities from clinical notes
✅ Genomics & Proteomics - Identify "DNA sequences", "RNA transcripts", "gene variants", "protein complexes", and "cell lines"
✅ Pathology & Disease Detection - Recognize "pathological formations", "cancer types", and "disease entities" in medical literature
✅ Anatomical Recognition - Map "anatomical systems", "tissue types", "organ structures", and "cellular components"
✅ Clinical Entity Extraction - Detect "organism species", "amino acids", 'protein families", and "multi-tissue structures"

💡 Advanced Features:
🔍 Intelligent Entity Search - Find models by specific biomedical entities (e.g., "Show me models detecting CHEM + DNA + Protein")
🏥 Domain-Specific Filtering - Browse by Oncology, Pharmacology, Genomics, Pathology, Hematology, and more
📊 Model Architecture Insights - Compare BERT, RoBERTa, and DeBERTa implementations
⚡ Real-Time Search - Auto-filtering as you type, no search buttons needed
🎨 Clinical-Grade UI - Beautiful, intuitive interface designed for medical professionals

Ready to revolutionize your biomedical NLP pipeline?

🔗 Try it now: OpenMed/openmed-ner-models
🧬 Built with: Gradio, Transformers, Advanced Entity Mapping

5 replies

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danielhanchen

posted an update 6 months ago

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Made some 245GB (80% size reduction) 1.8bit quants for Kimi K2!

unsloth/Kimi-K2-Instruct-GGUF

danielhanchen

posted an update 6 months ago

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We fixed more issues! Use --jinja for all!
* Fixed Nanonets OCR-s unsloth/Nanonets-OCR-s-GGUF
* Fixed THUDM GLM-4 unsloth/GLM-4-32B-0414-GGUF
* DeepSeek Chimera v2 is uploading! unsloth/DeepSeek-TNG-R1T2-Chimera-GGUF

danielhanchen

posted an update 6 months ago

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Gemma 3n finetuning is now 1.5x faster and uses 50% less VRAM in Unsloth!

Click "Use this model" and click "Google Colab"!

unsloth/gemma-3n-E4B-it

unsloth/gemma-3n-E2B-it

https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb

2 replies

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arthurbresnu

posted an update 6 months ago

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‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module & much more. Sparse + Dense = 🔥 hybrid search performance!

1️⃣ Sparse Encoder Models - New support for sparse embeddings (30k+ dims, <1% non-zero)

* Full SPLADE, Inference-free SPLADE, CSR support
* 4 new modules, 12 losses, 9 evaluators
* Integration with elastic, opensearch-project, Qdrant, ibm-granite
* Decode interpretable embeddings
* Hybrid search integration

2️⃣ Enhanced Encode Methods

* encode_query & encode_document with auto prompts
* Direct device list passing to encode()
* Cleaner multi-processing

3️⃣ Router Module & Training

* Different paths for queries vs documents
* Custom learning rates per parameter group
* Composite loss logging
* Perfect for two-tower architectures

4️⃣ Documentation & Training

* New Training/Loss Overview docs
* 6 training example pages
* Search engine integration examples

Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder

See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0

What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!

I'm incredibly excited to see the community explore sparse embeddings and hybrid search! The interpretability alone makes this a game-changer for understanding what your models are actually doing.

🙏 Thanks to @tomaarsen for this incredible opportunity!

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INTELLECT-1 Technical Report

OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets

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