open-acc (open/ acc)

posted an update 7 days ago

Post

2599

Introducing the github-codereview dataset: A compilation of 200k+ human-written code reviews from top OSS projects (React, Tensorflow, VSCode...).

I finetuned a Qwen2.5-Coder-32B-Instruct model with this dataset and saw significant improvements in generating better code fixes and review comments (4x improved BLEU-4, ROUGE-L, SBERT scores compared to base model).

#codereview #code #datasets

ronantakizawa/github-codereview

MaziyarPanahi

posted an update 9 days ago

Post

4170

DNA, mRNA, proteins, AI. I spent the last year going deep into computational biology as an ML engineer. This is Part I of what I found. 🧬

In 2024, AlphaFold won the Nobel Prize in Chemistry.

By 2026, the open-source community had built alternatives that outperform it.

That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.

I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.

New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape

Hope you all enjoy! 🤗

2 replies

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ronantakizawa

posted an update 10 days ago

Post

2362

Introducing the WebUI dataset: a compilation of screenshot to code pairs of modern websites detailing the styling, framework used, and box bounds for all viewports (Desktop, mobile, tablet).

This dataset showed signs of improved performance in web design LLM benchmarks for a finetuned QWEN 2.5 VL-7B!

#web #ui #datasets

ronantakizawa/webui

3 replies

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mitkox

posted an update 19 days ago

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5357

My USB charger has a Blackwell GPU and 128GB RAM.
What. A. Time. To. Be. Alive.
People in Sofia: “It’s freezing.”
Me: sitting next to 3kW of space AI heaters on my desk 👀
1x GLM-5, 2x MiniMax-M2.5, 1x Qwen3 Coder Next; all on single Aibrix/K8s cluster

6 replies

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MaziyarPanahi

authored a paper 21 days ago

Arcee Trinity Large Technical Report

Paper • 2602.17004 • Published 22 days ago • 17

ronantakizawa

posted an update 21 days ago

Post

2114

Introducing the github-top-code dataset: A curated dataset of 1.3M+ source code files from GitHub's top ranked developers.

I collected the best source code files from Github's highest trending developers of all time, and compiled a dataset to train LLMs to write well-structured, production-grade code.

#dataset #codedataset #pretraining

ronantakizawa/github-top-code

mitkox

posted an update 22 days ago

Post

297

134,614 tok/sec input prefil max
1031 tokens/sec out gen max

At these local AI speeds, there is no User Interface for humans. My human UI is the Radicle distributed Git issues queue

On my GPU workstation:
- Z8 Fury G5 4x A6000
- MiniMax-M2.5
- Claude Code to localhost:8000

1 reply

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ronantakizawa

posted an update 25 days ago

Post

283

Introducing the LeetCode Assembly Dataset: a dataset of 400+ LeetCode problem solutions in assembly across x86-64, ARM64, MIPS64, and RISC-V using GCC & Clang at -O0/-O1/-O2/-O3 optimizations.

This dataset is perfect for teaching LLMs complex compiler behavior!

#dataset #leetcode #assembly

ronantakizawa/leetcode-assembly

1aurent

authored a paper 30 days ago

Ministral 3

Paper • 2601.08584 • Published Jan 13 • 56

MaziyarPanahi

posted an update about 1 month ago

Post

2229

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

1 reply

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ronantakizawa

posted an update about 1 month ago

Post

240

Hit 10,000+ downloads across my models and datasets on Hugging Face!

Follow for more @ronantakizawa !

#building #datasets #huggingface

mitkox

posted an update about 1 month ago

Post

4738

I just pushed Claude Code Agent Swarm with 20 coding agents on my desktop GPU workstation.

With local AI, I don’t have /fast CC switch, but I have /absurdlyfast:
- 100’499 tokens/second read, yeah 100k, not a typo | 811 tok/sec generation
- KV cache: 707’200 tokens
- Hardware: 5+ year old GPUs 4xA6K gen1; It’s not the car. It’s the driver.

Qwen3 Coder Next AWQ with cache at BF16. Scores 82.1% in C# on 29-years-in-dev codebase vs Opus 4.5 at only 57.5%. When your codebase predates Stack Overflow, you don't need the biggest model; you need the one that actually remembers Windows 95.

My current bottleneck is my 27" monitor. Can't fit all 20 Theos on screen without squinting.

3 replies

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MaziyarPanahi

posted an update about 1 month ago

Post

1255

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 about 1 month ago

Post

3996

🚨 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")

6 replies

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Sri-Vigneshwar-DJ

posted an update about 1 month ago

Post

1404

Just released a new dataset designed for training reasoning models on Meta (Facebook/Instagram) advertising fatigue detection!

What is it? A GRPO (Group Relative Policy Optimization) training dataset with 200+ carefully crafted scenarios covering:

🔍 Fatigue Signal Detection: CTR drops, CPM spikes, frequency analysis
🩺 Performance Diagnosis: Root cause analysis frameworks
📋 Strategy: Creative refresh cadence, testing frameworks
📊 Analysis: ROI calculations, metric interpretation
Why GRPO? GRPO training helps models learn structured reasoning. Each response follows the <thinking> and <answer> format.

Check it out here: Sri-Vigneshwar-DJ/meta-fatigue-grpo-dataset

ronantakizawa

posted an update about 1 month ago

Post

2663

Moltbook, a Reddit platform only for AI agents, is going viral right now as agents are acting unhinged!

I compiled a dataset of all posts and subreddits in Moltbook so far so anyone can easily analyze the activity in Moltbook.

ronantakizawa/moltbook

#moltbook #clawd #aiagent

2 replies

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julien-c

submitted a paper to Daily Papers about 1 month ago

Shaping capabilities with token-level data filtering

Paper • 2601.21571 • Published Jan 29 • 27

cfahlgren1

submitted a paper to Daily Papers about 1 month ago

How AI Impacts Skill Formation

Paper • 2601.20245 • Published Jan 28 • 9

mitkox

posted an update about 1 month ago

Post

312

▐▛██▜▌ Claude Code v2.1.23
▝████▘ Kimi-K2.5 · API Usage Billing
▘▘ ▝▝ ~/dev/vllm
/model to try Opus 4.5
❯ hey
● Hello! How can I help you today?
❯ what model are you?
● I'm Claude Kimi-K2.5, running in a local environment on Linux.

Took some time to download and vLLM hybrid inferencing magic to get it running on my desktop workstation.

Sri-Vigneshwar-DJ

posted an update about 2 months ago

Post

219

🏙️ Hugging Face Community Post
Title: 🧬 Experimenting with "Dynamic Chaos" in Tamil SLMs

Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.

I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.

Key highlights of the study:

Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations).
The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable.
Compression: 4-bit (Q4_K_M) quantization for extreme efficiency.
Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!

Check out the model and the experiment logic here: Sri-Vigneshwar-DJ/qwen-tamil-chaos-v1

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Shaping capabilities with token-level data filtering

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