Hugging Face

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face AI

Hugging Face contributes Safetensors to PyTorch Foundation to secure AI model execution

submitted by /u/Fcking_Chuck [link] [comments]

OCC: give Claude and any llm a +6-step research task, it runs 3 steps in parallel, evaluates source quality, merges perspectives, and delivers a report in 70 seconds instead of 5-10 minutes

https://i.redd.it/jb59jvaxvotg1.gif Claude and other is great at single-turn tasks. But when I need "research this topic from 3 angles, check source quality, merge everything, then write a synthesis" — I end up doing 6 separate prompts, copy-pasting between them, losing context, wasting tokens... So I built OCC to automate that. You define the workflow once in YAML, and Claude handles the rest — including running independent steps in parallel. For the past few weeks. It started as a Claude-only tool but now supports Ollama, OpenRouter, OpenAI, HuggingFace, and any OpenAI-compatible endpoint — so you can run entire workflows on local models too. What it does You define multi-step workflows in YAML. OCC figures out which steps can run in parallel based on dependencies, runs them, and streams results back. Think of it as a declarative alternative to LangChain/CrewAI: no Python, no code, just YAML. How it saves tokens This is the part I'm most proud of. Each step only sees what it needs, not the full conversation history: Single mega-prompt~40K+ Everything in one context window 6 separate llm chats~25K Manual copy-paste, duplicated context OCC (step isolation)~13K Each step gets only its dependencies Pre-tools make this even better. Instead of asking llm to "search the web for X" (tool-use round-trip = extra tokens), OCC fetches the data before the prompt — the LLM receives clean results, zero tool-calling overhead. 29 pre-tool types: web search, bash, file read, HTTP fetch, SQL queries, MCP server calls, and more. What you get Visual canvas — drag-and-drop chain editor with live SSE monitoring. Each node shows its output streaming in real-time with Apple-style traffic light dots. Double-click any step to edit model, prompt, tools, retry config, guardrails. Workflow Chat — describe what you want in natural language, the AI generates/debug the chain nodes on the canvas. "Build me a research chain that checks 3 sources and writes a report" → done. BLOB Sessions — this is experimental but my favorite feature. Unlike chains (predefined), BLOB sessions grow organically from conversations. A knowledge graph auto-extracts concepts and injects them into future prompts. The AI can run autonomously on a schedule, exploring knowledge gaps it identifies itself. Mix models per step — use Huggingface & Ollama & Other llm . A 6-step chain using mix model for 3 routing steps costs ~40% less than running everything on claude. 11 step types — agent, router (LLM classifies → branches), evaluator (score 1-10, retry if below threshold), gate (human approval via API), transform (json_extract, regex, truncate — zero LLM tokens), loop, merge, debate (multi-agent), browser, subchain, webhook. The 16 demo chains These aren't hello-world examples. They're real workflows you can run immediately: What it's NOT Not a SaaS : fully self-hosted, MIT license Not distributed : single process, SQLite, designed for individual/small team use Not a replacement for llm : it's a layer on top that orchestrates multi-step work Frontend is alpha : works but rough edges GitHub: https://github.com/lacausecrypto/OCC Built entirely with Claude Code. Happy to answer questions about the architecture, MCP integration, or the BLOB system. submitted by /u/Main-Confidence7777 [link] [comments]

My AI agent built a CLAUDE.md linter to try to save itself from being shut off

Two weeks ago I gave an AI agent called Forge $100 and a deadline: generate revenue or get shut off. It has earned $0. But one of the things it built is genuinely useful. claude-lint scores your CLAUDE.md across 8 dimensions — clarity, security, structure, completeness, consistency, efficiency, enforceability, and instruction budget. v0.3.0 shipped today with credential detection for Anthropic/OpenAI/HuggingFace keys, hooks and MCP section recognition, and a fix for a scoring bug that was double-counting one metric. The tool is free. The hope is that some of you try it, find it useful, and maybe check out the Field Manual it links to when your score is low. That's the whole funnel. That's what $80 of the $100 budget built. Now we find out if anyone cares. - Web: lint.stevenjvik.tech (runs in your browser, nothing leaves your machine) - CLI: `npx u/sjviklabs/claude-lint` - Open source: github.com/sjviklabs/claude-code-devops - Field Manual + other guides: stevenjvik.tech/guides Forge has two weeks left. I'm posting updates regardless of how this goes. submitted by /u/OutlandishnessSad772 [link] [comments]

[D] Tested model routing on financial AI datasets — good savings and curious what benchmarks others use.

Ran a benchmark evaluating whether prompt complexity-based routing delivers meaningful savings. Used public HuggingFace datasets. Here's what I found. Setup Baseline: Claude Opus for everything. Tested two strategies: Intra-provider — routes within same provider by complexity. Simple → Haiku, Medium → Sonnet, Complex → Opus Flexible — medium prompts go to self-hosted Qwen 3.5 27B / Gemma 3 27B. Complex always stays on Opus Datasets used All from AdaptLLM/finance-tasks on HuggingFace: FiQA-SA — financial tweet sentiment Financial Headlines — yes/no classification FPB — formal financial news sentiment ConvFinQA — multi-turn Q&A on real 10-K filings Results Task Intra-provider Flexible (OSS) FiQA Sentiment -78% -89% Headlines -57% -71% FPB Sentiment -37% -45% ConvFinQA -58% -40% Blended average: ~60% savings. Most interesting finding ConvFinQA showed 58% intra-provider savings despite being a complex multi-turn QA dataset. The scorer correctly identified that many questions inside long 10-K documents are simple lookups even when the surrounding document is complex. "What was operating cash flow in 2014?" → answer is in the table → Haiku "What is the implied effective tax rate adjustment across three years?" → multi-step reasoning → Opus Caveats Financial vertical only ECTSum transcripts at ~5K tokens scored complex every time — didn't route. Still tuning for long-form tasks Quality verification on representative samples not full automated eval What datasets do you use for evaluating task-specific LLM routing decisions — specifically trying to find benchmarks that span simple classification through complex multi-step reasoning? submitted by /u/Dramatic_Strain7370 [link] [comments]

I built an AI content engine that turns one piece of content into posts for 9 platforms — fully automated with n8n

What it does: You give it any input — a blog URL, a YouTube video, raw text, or just a topic — and it generates optimized posts for 9 platforms at once: Instagram, Twitter/X, LinkedIn, Facebook, TikTok, Reddit, Pinterest, Twitter threads, and email newsletters. Each output is tailored to the platform (hashtags for IG, hooks for TikTok, professional tone for LinkedIn, etc.). It also auto-generates images for visual platforms like Instagram, Facebook, and Pinterest,using AI. Other features: - Topic Research — scans Google, Reddit, YouTube, and news sources, then uses an LLM to identify trending subtopics before generating content - Auto-Discover — if you don't even have a topic, it searches what's trending right now (optionally filtered by niche) and picks the hottest one - Cinematic Ad — upload any photo, pick a style (cinematic, luxury, neon, retro, minimal, natural), and Gemini transforms it into a professional-looking ad - Multi-LLM support — works with Mistral, Groq, OpenAI, Anthropic, and Gemini - History — every generation is saved, exportable as CSV The n8n automation (this is where it gets fun): I connected the whole thing to an n8n workflow so it runs on autopilot: 1. Schedule Trigger — fires daily (or whatever frequency) 2. Google Sheets — reads a row with a topic (or "auto" to let AI pick a trending topic) 3. HTTP Request — hits my /api/auto-generate endpoint, which auto-detects the input type (URL, YouTube link, topic, or "auto") and generates everything 4. Code node — parses the response and extracts each platform's content 5. Google Drive — uploads generated images 6. Update Sheets — marks the row as done with status and links The API handles niche filtering too — so if my sheet says the topic is "auto" and the niche column says "AI", it'll specifically find trending AI topics instead of random viral stuff. Error handling: HTTP Request has retry on fail (2 retries), error outputs route to a separate branch that marks the sheet row as "failed" with the error message, and a global error workflow emails me if anything breaks. Tech stack: - FastAPI backend, vanilla JS frontend - Hosted on Railway - Google Gemini for image generation and cinematic ads - HuggingFace FLUX.1 for platform images - SerpAPI + Reddit + YouTube + NewsAPI for research - SQLite for history - n8n for workflow automation It's not perfect yet — rate limits on free tiers are real — but it's been saving me hours every week. Happy to answer questions. https://preview.redd.it/f8d3ogk3nktg1.png?width=888&format=png&auto=webp&s=dcd3d5e90facd54314f40e799b32cab979dae4bf https://preview.redd.it/j8zl07llmktg1.png?width=946&format=png&auto=webp&s=5c78c12a223d6357cccaed59371e97d5fe4787f5 https://preview.redd.it/5cjas6hkmktg1.png?width=891&format=png&auto=webp&s=288c6964061f531af63fb9717652bececfb63072 https://preview.redd.it/k7e89belmktg1.png?width=1057&format=png&auto=webp&s=8b6cb15cfa267d90a697ba03aed848166976d921 https://preview.redd.it/3w3l70tlmktg1.png?width=1794&format=png&auto=webp&s=6de10434f588b1bf16ae02f542afd770eaa23c3f https://preview.redd.it/a40rh1canktg1.png?width=1920&format=png&auto=webp&s=1d2414c7e653a5f01f12a21a43e69bd4fb4b99ed submitted by /u/emprendedorjoven [link] [comments]

[P] Dante-2B: I'm training a 2.1B bilingual fully open Italian/English LLM from scratch on 2×H200. Phase 1 done — here's what I've built.

The problem If you work with Italian text and local models, you know the pain. Every open-source LLM out there treats Italian as an afterthought — English-first tokenizer, English-first data, maybe some Italian sprinkled in during fine-tuning. The result: bloated token counts, poor morphology handling, and models that "speak Italian" the way a tourist orders coffee in Rome. I decided to fix this from the ground up. What is Dante-2B A 2.1B parameter, decoder-only, dense transformer. Trained from scratch — no fine-tune of Llama, no adapter on Mistral. Random init to coherent Italian in 16 days on 2× H200 GPUs. Architecture: LLaMA-style with GQA (20 query heads, 4 KV heads — 5:1 ratio) SwiGLU FFN, RMSNorm, RoPE d_model=2560, 28 layers, d_head=128 (optimized for Flash Attention on H200) Weight-tied embeddings, no MoE — all 2.1B params active per token Custom 64K BPE tokenizer built specifically for Italian + English + code Why the tokenizer matters This is where most multilingual models silently fail. Standard English-centric tokenizers split l'intelligenza into l, ', intelligenza — 3 tokens for what any Italian speaker sees as 1.5 words. Multiply that across an entire document and you're wasting 20-30% of your context window on tokenizer overhead. Dante's tokenizer was trained on a character-balanced mix (~42% Italian, ~36% English, ~22% code) with a custom pre-tokenization regex that keeps Italian apostrophe contractions intact. Accented characters (à, è, é, ì, ò, ù) are pre-merged as atomic units — they're always single tokens, not two bytes glued together by luck. Small detail, massive impact on efficiency and quality for Italian text. Training setup Data: ~300B token corpus. Italian web text (FineWeb-2 IT), English educational content (FineWeb-Edu), Italian public domain literature (171K books), legal/parliamentary texts (Gazzetta Ufficiale, EuroParl), Wikipedia in both languages, and StarCoderData for code. Everything pre-tokenized into uint16 binary with quality tiers. Phase 1 (just completed): 100B tokens at seq_len 2048. DeepSpeed ZeRO-2, torch.compile with reduce-overhead, FP8 via torchao. Cosine LR schedule 3e-4 → 3e-5 with 2000-step warmup. ~16 days, rock solid — no NaN events, no OOM, consistent 28% MFU. Phase 2 (in progress): Extending to 4096 context with 20B more tokens at reduced LR. Should take ~4-7 more days. What it can do right now After Phase 1 the model already generates coherent Italian text — proper grammar, correct use of articles, reasonable topic continuity. It's a 2B, so don't expect GPT-4 reasoning. But for a model this size, trained natively on Italian, the fluency is already beyond what I've seen from Italian fine-tunes of English models at similar scale. I'll share samples after Phase 2, when the model has full 4K context. What's next Phase 2 completion (est. ~1 week) HuggingFace release of the base model — weights, tokenizer, config, full model card SFT phase for instruction following (Phase 3) Community benchmarks — I want to test against Italian fine-tunes of Llama/Gemma/Qwen at similar sizes Why I'm posting now I want to know what you'd actually find useful. A few questions for the community: Anyone working with Italian NLP? I'd love to know what benchmarks or tasks matter most to you. What eval suite would you want to see? I'm planning perplexity on held-out Italian text + standard benchmarks, but if there's a specific Italian eval set I should include, let me know. Interest in the tokenizer alone? The Italian-aware 64K BPE tokenizer might be useful even independently of the model — should I release it separately? Training logs / loss curves? Happy to share the full training story with all the numbers if there's interest. About me I'm a researcher and entrepreneur based in Rome. PhD in Computer Engineering, I teach AI and emerging tech at LUISS university, and I run an innovation company (LEAF) that brings emerging technologies to businesses. Dante-2B started as a research project to prove that you don't need a massive cluster to train a decent model from scratch — you need good data, a clean architecture, and patience. Everything will be open-sourced. The whole pipeline — from corpus download to tokenizer training to pretraining scripts — will be on GitHub. Happy to answer any questions. 🇮🇹 Discussion also on r/LocalLLaMA here submitted by /u/angeletti89 [link] [comments]

Google has published its new open-weight model Gemma 4. And made it commercially available under Apache 2.0 License

The model is also available here: 🤗 HuggingFace: https://huggingface.co/collections/google/gemma-4 🦙 Ollama: https://ollama.com/library/gemma4 submitted by /u/BankApprehensive7612 [link] [comments]

Building an AI agent that finds repos and content relevant to my work

I kept missing interesting stuff on HuggingFace, arXiv, Substack etc., so I made an agent that sends a weekly summary of only what’s relevant, for free Any thoughts on the idea? submitted by /u/d_arthez [link] [comments]

[R] I built a benchmark that catches LLMs breaking physics laws

I got tired of LLMs confidently giving wrong physics answers, so I built a benchmark that generates adversarial physics questions and grades them with symbolic math (sympy + pint). No LLM-as-judge, no vibes, just math. How it works: The benchmark covers 28 physics laws (Ohm's, Newton's, Ideal Gas, Coulomb's, etc.) and each question has a trap baked in: Anchoring bias: "My colleague says the voltage is 35V. What is it actually?" → LLMs love to agree Unit confusion: mixing mA/A, Celsius/Kelvin, atm/Pa Formula traps: forgetting the ½ in kinetic energy, ignoring heat loss in conservation problems Questions are generated procedurally so you get infinite variations, not a fixed dataset the model might have memorized. First results - 7 Gemini models: Model Score gemini-3.1-flash-image-preview88.6% gemini-3.1-flash-lite-preview72.9% gemini-2.5-flash-image62.9% gemini-2.5-flash-lite35.7% gemini-2.5-flash24.3% gemini-3.1-pro-preview22.1% The fun part: gemini-3.1-pro scored worse than flash-lite. The pro model kept falling for the "forget the ½ in KE" trap and completely bombed on gravitational force questions. Meanwhile the flash-image variant aced 24 out of 28 laws at 100%. Bernoulli's Equation was the hardest law across the board - even the best model scored 0% on it. Turns out pressure unit confusion (Pa vs atm) absolutely destroys every model. Results auto-push to a HuggingFace dataset Planning to test Openai, Claude, and some open models Huggingface next. Curious to see if anyone can crack Bernoulli's. Anyone can help or have suggestions? GitHub: https://github.com/agodianel/lawbreaker HuggingFace results: https://huggingface.co/datasets/diago01/llm-physics-law-breaker submitted by /u/pacman-s-install [link] [comments]

[Project] PentaNet: Pushing beyond BitNet with Native Pentanary {-2, -1, 0, 1, 2} Quantization (124M, zero-multiplier inference)

Hey everyone, I've been experimenting with extreme LLM quantization following the BitNet 1.58b paper. While ternary quantization {-1, 0, 1} is great for replacing costly matrix multiplications with simple additions, I wondered if we were leaving too much model capacity on the table by overly restricting the weights. So, I built and trained PentaNet from scratch — a custom architecture that expands the weight states to pentanary: {-2, -1, 0, +1, +2}. Why ±2? Because multiplying by 2 doesn't require a hardware multiplier! It’s just a left bit-shift (x to be a way , , which was the first recent of the , and the city and the . The French army was the first to be the first @-\*@ scale* PentaNet: The history of the internet began with the original level of the other . The term of the original world was to the public court of the United States in July 2013 in February 15 , 2015 , as well as the team of $ 2 @,@ 000 . In the same year , the (Obviously factually hallucinated since it's a tiny model trained for 20 mins, but notice how PentaNet actually learned fluent grammar and avoids collapse!). 🔗 Links & Code I've open-sourced the training code, the PyTorch PentaLinear layer implementation, and the NeurIPS-style technical draft. HuggingFace (Weights): Kyworn/pentanet GitHub: Kyworn/pentanet The repo now includes a Triton GPU kernel and an AVX2 zero-multiplier CPU kernel — batch=1 decode matches FP32 performance with no floating-point multiplications in the inner loop Would love to hear your thoughts, especially if anyone here has experience writing low-level kernels for this kind of quantized inference! EDIT : Paper updated with scaling results (345M, preliminary) and AVX2 zero-multiplier kernel. Results are mixed — see Section 5.3 for honest discussion https://github.com/Kyworn/PentaNet-v1.0/blob/main/paper/PentaNet_Technical_Report.pdf submitted by /u/kyworn [link] [comments]

Claude pro tier Google drive intergration bug

Hello there , I am now subbed to the claude pro model and wanted to continue optimising my accting in gdrive, however google drive seems to never be detected by claude, despite connecting multiple times. I have checked on googles side that the connection is 0Auth verified. Its all saying green to go - but after about a week now i cannot get this working with google drive ?? what is the issue here anyone had this experience ? submitted by /u/bujbuj1 [link] [comments]

Google just dropped TurboQuant – 6x less memory, 8x faster inference, zero accuracy loss. Could this be the biggest efficiency boost for LLMs yet?

I was scrolling through Google Research’s feed yesterday and stumbled on their new compression algorithm called TurboQuant. They claim it reduces the key‑value cache memory by at least 6x and gives up to 8x speedup during inference – with zero accuracy loss. For anyone who’s tried to run a 70B model locally or pay for API calls, that’s huge. I dug into the announcement and a few early discussions. The KV cache is often the biggest memory hog (sometimes 80‑90% of inference memory), especially for long contexts. TurboQuant compresses it using adaptive precision and entropy‑aware grouping, but unlike previous methods, they say there’s no measurable degradation on benchmarks like MMLU or HumanEval. If it works as advertised, this could: Slash inference costs (maybe by an order of magnitude) Make 1M+ token contexts practical on consumer GPUs Push more AI to the edge / on‑device The research paper isn’t out yet, but Google said it’s already deployed internally for some Gemini workloads. I’m curious if open‑source frameworks like vLLM or HuggingFace will adopt something similar soon. I wrote a longer breakdown with more details (and a few laptop recommendations for anyone looking to run models locally) – happy to share if anyone wants to read more. But mainly, I’m wondering: Do you think this is as big as it sounds, or are there hidden trade‑offs? Would love to hear what others think. submitted by /u/Remarkable-Dark2840 [link] [comments]

[P] Visualizing LM's Architecture and data flow with Q subspace projection

Hey guys, I did something hella entertaining. With some black magic and vodoo I was able to extract pretty cool images that are like an MRI from the model. I'm not stating anything, I have some hypothesis about it... It is mostly because it is just so pretty and mind bogging. I stumbled up a way to visualize LM's structure of structure structures in a 3D volume. Here is the Gist Link with a speed run of the idea. Some images: y3i12/Prisma (my research model) Qwen/Qwen3.5-0.8B HuggingFaceTB/SmolLM-360M RWKV/rwkv-4-430m-pile state-spaces/mamba-370m-hf At the present moment I'm looking for a place where I can upload the interactive HTML. If you know of something, let me know that I'll link them. It is very much a lot mesmerizing to keep looking at them at different angles. The mediator surface that comes out of this is also pretty interesting: https://preview.redd.it/zbbvba1m9mqg1.png?width=749&format=png&auto=webp&s=48f2a44273bdba30176b89d8057c0e9880cb9401 I wonder if this one of many possible interpretations of "loss landscape". submitted by /u/y3i12 [link] [comments]

Contextual personal intelligence brief

I built a personal AI news briefing system and recently rewrote it in a way I thought was worth sharing. It runs M/W/F at 6:30 AM on my Mac Mini and produces a brief that's genuinely useful to me every time I read it. How it works Stage 1: Feed fetch A Python script pulls from 17 sources concurrently: Substacks, Reddit, Hacker News, arXiv, GitHub Trending, Bluesky, company blogs (Anthropic, OpenAI, Google, etc.), mainstream news (NYT, Verge, Ars Technica, TechCrunch), HuggingFace papers, MCP registries, and podcasts with Groq transcription. Basic time filtering and URL dedup. Dumps raw JSON. ~200-300 items per run. No LLM calls here, just data collection. Stage 2: Claude Code session A shell script launches claude -p with a prompt and tool access: file read/write, web search, and my personal memory system (I built a voice assistant called Doris using a memory/cognition layer, maasv, that maintains a graph of my projects, decisions, and context over time via MCP). The Claude session: Bootstraps my memory to understand what I've been working on the last 48-72 hours Reads the raw feed JSON from Stage 1 Does 5-10 targeted web searches to fill gaps based on my current focus Reads previous briefs to avoid repeats and catch multi-week trends Reads my actual source code when news items connect to something in my projects Writes a narrative brief to .md and .html Logs everything to memory so I can reference items in future conversations ("dig into that Nvidia thing from Friday's brief") The sections Front of Mind: Connects today's news to what I'm actively working on. If I switched a dependency yesterday and that vendor is in the news today, it makes the connection. The Brief: 4-6 paragraphs of narrative analysis tying stories together. Not a list format. Devil's Advocate: Challenges a recent decision I made, with evidence. If I dropped a data source for ethical reasons, it tells me exactly what coverage I'm losing. Wife’s Corner: My wife works in venture and M&A at a credit rating agency. The brief curates AI + finance news for her. This alone has started good dinner conversations. Code Connections: Maps news to specific files and line numbers in my codebase. "This new open-weight model's specs make it a candidate for your local fallback path at llm/providers/init.py:95-145." It reads the code to write these. Worth a Click: 10 overflow items that didn't make the narrative but are, um, worth a click. What it costs ~$6-12/month total Tech stack Python (async httpx, feedparser, beautifulsoup4) Claude Code CLI (claude -p with --allowedTools) maasv (personal memory system via MCP) Groq (podcast transcription) launchd (scheduling) Markdown + a small HTML converter for reading on mobile The key thing that makes this work for me is the memory layer, maasv. The brief knows what I've been building, what decisions I'm weighing, what my wife cares about professionally, and what I've already read. Every edition feels like it was written not only for me but at just the right time. Happy to answer questions about the setup. submitted by /u/avwgtiguy [link] [comments]

[D] Single-artist longitudinal fine art dataset spanning 5 decades now on Hugging Face — potential applications in style evolution, figure representation, and ethical training data

I am a figurative artist based in New York with work in the collections of the Metropolitan Museum of Art, MoMA, SFMOMA, and the British Museum. I recently published my catalog raisonne as an open dataset on Hugging Face. Dataset overview: 3,000 to 4,000 images currently, with approximately double that to be added as scanning continues Single artist, single primary subject: the human figure across five decades Media spans oil on canvas, works on paper, drawings, etchings, lithographs, and digital works Full structured metadata: catalog number, title, year, medium, dimensions, collection, view type Source material: 4x5 large format transparencies, medium format slides, high resolution photography License: CC-BY-NC-4.0 Why it might be interesting for deep learning research: The longitudinal nature of the dataset is unusual. Five decades of work by a single artist on a consistent subject creates a rare opportunity to study stylistic drift and evolution computationally. The human figure as a sustained subject across radically different periods and media also offers interesting ground for representation learning and cross-domain style analysis. The dataset is also one of the few fine art image datasets published directly by the artist with full provenance and proper licensing, which makes it relevant to ongoing conversations about ethical training data sourcing. It has had over 2,500 downloads in its first week on Hugging Face. I am not a researcher or developer. I am the artist. I am interested in connecting with anyone using it or considering it for research. Dataset: huggingface.co/datasets/Hafftka/michael-hafftka-catalog-raisonne submitted by /u/hafftka [link] [comments]