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We aim to unify the schema across many different biomedical NLP resources.

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MaziyarPanahi  authored a paper 9 days ago
Arcee Trinity Large Technical Report
mariagrandury  authored a paper 10 days ago
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data
mariagrandury  authored a paper 10 days ago
Measuring what Matters: Construct Validity in Large Language Model Benchmarks
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albertvillanova 
posted an update 3 days ago
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1605
🚀 TRL v0.29.0 introduces trl-training: an agent-native training skill.

This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)

We’re excited to see what the community builds on top of this.

If you’re working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! 🤗

The future of ML tooling is agent-native.
🔗 https://github.com/huggingface/trl/releases/tag/v0.29.0
Tonic 
posted an update 8 days ago
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3000
🤔 Who would win ?

- a fully subsidized ai lab
OR
- 3 random students named
kurakurai ?

demo : Tonic/fr-on-device

if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
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MaziyarPanahi 
authored a paper 9 days ago
mariagrandury 
authored 2 papers 10 days ago
Tonic 
posted an update 12 days ago
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3170
🙋🏻‍♂️hello my lovelies ,

it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.

repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw

you can also run it locally and see for yourself :

docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest


just a few quite minor details i'll take care of but i wanted to share here first
  • 2 replies
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albertvillanova 
posted an update 18 days ago
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5 years already working in democratizing AI 🤗
Grateful to be part of such an awesome team making it happen every day.
MaziyarPanahi 
posted an update 19 days ago
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2099
Announcing: OpenMed Multilingual PII Detection Models

Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.

All Apache 2.0 licensed. Free for commercial use. No restrictions.

Performance:

- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)

All top-10 models per language exceed 96% F1

Coverage:

55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns

Training Data:

French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples

Why Multilingual?

European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.

Effective de-identification requires:

- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.

HIPAA & GDPR Compliance
Built for US and European privacy regulations:

- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.

Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across

https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
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MaziyarPanahi 
posted an update 22 days ago
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From Golden Gate Bridge to Broken JSON: Why Anthropic's SAE Steering Fails for Structured Output

I ran 6 experiments trying to use Anthropic's SAE steering for JSON generation.

- Base model: 86.8% valid JSON
- Steering only: 24.4%
- Fine-tuned: 96.6%
- FSM constrained: 100%

Steering is for semantics, not syntax.

https://huggingface.co/blog/MaziyarPanahi/sae-steering-json
MaziyarPanahi 
posted an update 23 days ago
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🚨 Day 8/8: OpenMed Medical Reasoning Dataset Release - THE GRAND FINALE

Today I complete my 8-day release series with Medical-Reasoning-SFT-Mega.
The largest open medical reasoning dataset, combining 7 state-of-the-art AI models with fair distribution deduplication.

THE 7 SOURCE MODELS (Original Sample Counts):

1. Trinity-Mini: 810,284 samples
2. Qwen3-Next-80B: 604,249 samples
3. GPT-OSS-120B: 506,150 samples
4. Nemotron-Nano-30B: 444,544 samples
5. GLM-4.5-Air: 225,179 samples
6. MiniMax-M2.1: 204,773 samples
7. Baichuan-M3-235B: 124,520 samples

TOTAL BEFORE DEDUPLICATION: 2,919,699 samples

TOKEN COUNTS:
- Content tokens: 2.22 Billion
- Reasoning tokens: 1.56 Billion
- Total tokens: 3.78 Billion
- Samples with chain-of-thought: 100%

Quick Start:
from datasets import load_dataset
ds = load_dataset("OpenMed/Medical-Reasoning-SFT-Mega")


All datasets Apache 2.0 licensed. Free for research and commercial use.

Thank you for following OpenMed's release series. I can't wait to see what you build. 🔥

OpenMed/Medical-Reasoning-SFT-Mega
OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B-V2
OpenMed/Medical-Reasoning-SFT-Trinity-Mini
OpenMed/Medical-Reasoning-SFT-GLM_4.5_Air
OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1
OpenMed/Medical-Reasoning-SFT-Qwen3-Next-80B
OpenMed/Medical-Reasoning-SFT-Nemotron-Nano-30B
https://huggingface.co/datasets/OpenMed/Medical-Reasonin

https://huggingface.co/collections/OpenMed/medical-datasets
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jmhb 
submitted a paper to Daily Papers 25 days ago
aryopg 
authored 2 papers 25 days ago
jmhb 
authored a paper 25 days ago
mkurman 
posted an update about 2 months ago
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# SYNTHLabs

Symbolic reasoning dataset creator & curator.

- Generator: create your own dataset from scratch
- Converter: use existing datasets (Hugging Face support) with reasoning traces to match our SYNTH style
- DEEP Mode: multiple agents working together in various configurations
- Multi-turn Support: pass one DEEP run, let the model ask follow-up questions, and choose who should respond using SYNTH-like thinking
- Firebase/Firestore: download your data directly as a JSONL file or upload it to your Firestore (production mode)
- Data Preview: Have data but unsure what's inside? Explore it directly!
- Verifier View: evaluate generated data, remove duplicates, assign ratings

and many more!

GitHub: https://github.com/mkurman/synthlabs

Dataset:
mkurman/medical-SYNTH-reasoning-preview
Nymbo 
posted an update about 2 months ago
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Genuine recommendation: You should really use this AutoHotKey macro. Save the file as macros.ahk and run it. Before sending a prompt to your coding agent, press Ctrl + Alt + 1 and paste your prompt to any regular chatbot. Then send the output to the agent. This is the actual, boring, real way to "10x your prompting". Use the other number keys to avoid repeating yourself over and over again. I use this macro prolly 100-200 times per day. AutoHotKey isn't as new or hype as a lot of other workflows, but there's a reason it's still widely used after 17 years. Don't overcomplicate it.

; Requires AutoHotkey v1.1+

; All macros are `Ctrl + Alt + <variable>`

^!1::
    Send, Please help me more clearly articulate what I mean with this message (write the message in a code block):
return

^!2::
    Send, Please make the following changes:
return

^!3::
    Send, It seems you got cut off by the maximum response limit. Please continue by picking up where you left off.
return


In my experience the past few months, Ctrl + Alt + 1 works best with Instruct models (non-thinking). Reasoning causes some models to ramble and miss the point. I've just been using GPT-5.x for this.
MaziyarPanahi 
posted an update about 2 months 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
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Nymbo 
posted an update 2 months ago
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🚨 New tool for the Nymbo/Tools MCP server: The new Agent_Skills tool provides full support for Agent Skills (Claude Skills but open-source).

How it works: The tool exposes the standard discover/info/resources/validate actions. Skills live in /Skills under the same File_System root, and any bundled scripts run through Shell_Command, no new infrastructure required.

Agent_Skills(action="discover")  # List all available skills
Agent_Skills(action="info", skill_name="music-downloader")  # Full SKILL.md
Agent_Skills(action="resources", skill_name="music-downloader")  # Scripts, refs, assets


I've included a music-downloader skill as a working demo, it wraps yt-dlp for YouTube/SoundCloud audio extraction.

Caveat: On HF Spaces, Shell_Command works for most tasks, but some operations (like YouTube downloads) are restricted due to the container environment. For full functionality, run the server locally on your machine.

Try it out ~ https://www.nymbo.net/nymbot
Nymbo 
posted an update 3 months ago
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🚀 I've just shipped a major update to the Nymbo/Tools MCP server: the Agent_Terminal, a single "master tool" that cuts token usage by over 90%!

Anthropic found 98.7% context savings using code execution with MCP, Cloudflare published similar findings. This is my open-source implementation of the same idea.

# The Problem

Traditional MCP exposes every tool definition directly to the model. With 12 tools, that's thousands of tokens consumed *before the conversation even starts*. Each tool call also passes intermediate results through the context window — a 10,000-row spreadsheet? That's all going into context just to sum a column.

# The Solution: One Tool to Rule Them All

Agent_Terminal wraps all 12 tools (Web_Search, Web_Fetch, File_System, Generate_Image, Generate_Speech, Generate_Video, Deep_Research, Memory_Manager, Obsidian_Vault, Shell_Command, Code_Interpreter) into a single Python code execution gateway.

Instead of the model making individual tool calls, it writes Python code that orchestrates the tools directly:

# Search for Bitcoin price
result = Web_Search("current price of bitcoin", max_results=3)
print(result)


Don't know what tools are available? The agent can discover them at runtime:

print(search_tools('image'))  # Find tools by keyword
print(usage('Generate_Image'))  # Get full docs for a specific tool


The individual direct tool calls are all still there, but they can be disabled if using the Agent_Terminal. Try it now - https://www.nymbo.net/nymbot
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Locutusque 
posted an update 4 months ago
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🚀 AutoXLA - Accelerating Large Models on TPU
AutoXLA is an experimental library that automates the distribution, optimization, and quantization of large language models for TPUs using PyTorch/XLA. It extends the Hugging Face Transformers interface with TPU-aware features such as automatic sharding, custom attention kernels, and quantization-aware loading, making large-scale deployment and training both simpler and faster.
With quantization and Splash Attention kernels, AutoXLA achieves up to 4× speedups over standard Flash Attention implementations, significantly improving throughput for both inference and training workloads.
Whether you’re experimenting with distributed setups (FSDP, 2D, or 3D sharding) or optimizing memory via LanguageModelQuantizer, AutoXLA is built to make scaling LLMs on TPU seamless.
⚠️ Note: This is an experimental repository. Expect rough edges! Please report bugs or unexpected behavior through GitHub issues.
🔗 GitHub Repository: https://github.com/Locutusque/AutoXLA