Sri-Vigneshwar-DJ (DJ Sri Vigneshwar)

reacted to AdinaY's post with 🔥 25 days ago

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Seed-X 🔥 a suite of multilingual translation models released by ByteDance.

ByteDance-Seed/seed-x-6878753f2858bc17afa78543

✨ instruction/reinforcement learning/reward model
✨ Supports 28 languages, bidirectional translation
✨ Optimized for deployment & inference: 7B with mistral architecture
✨ Excels across domains: science, law, finance, literature & more

reacted to BramVanroy's post with 🚀 25 days ago

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📢💾 Introducing the Common Crawl Creative Commons Corpus (C5)!

C5 is a large-scale effort to heavily filter web-crawled data, as collected by the non-profit Common Crawl, to only documents that are Creative Commons-licensed such as cc-by-4.0 or public domain cc0. At this stage 150 billion tokens have been collected.

---
📄 data: BramVanroy/CommonCrawl-CreativeCommons
🧰 software: https://github.com/BramVanroy/CommonCrawl-CreativeCommons
---

</> To build C5, HTML pages are scrutinized and all links (if any) to CC licenses are collected, both in regular hyperlinks as well as in metadata. Additional data fields are included such as "was the license found in the head?" or "if multiple licenses were found, do they contradict each other?", which makes further filtering a breeze.

🌐 In this first version of C5, 8 languages are included (Afrikaans, German, English, French, Frysian, Italian, Dutch and Spanish). The language set was limited for two reasons: computational and storage limitations, and a collaboration with GPT-NL, which requested CC data for these languages to train a Dutch-focused, copyright-conscious LLM. In total, this V1 release contains almost 150 thousand documents and 150 billion tokens. This data was not filtered on quality nor deduplicated so that you can decide for yourself how much data to keep. To give some quality indication, a dataset field is present to describe whether a document is included in the FineWeb(-2) datasets, which are of high quality.

🔍 More work needs to be done! Only 7 out of 100+ Common Crawl crawls have been processed so far. That's encouraging because it means there is a lot more Creative Commons data to be collected! But to get there I need help in terms of compute. The current processing was already heavily sponsored by the Flemish Supercomputer but more is needed. If you have the compute available and which to collaborate in an open and transparent manner, please get in touch!

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reacted to reach-vb's post with 🔥 25 days ago

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hey hey @mradermacher - VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥

as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!

in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.

p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage

p.p.s. this is fully backwards compatible so everything will work as it should! 🤗

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reacted to Kseniase's post with 🔥 25 days ago

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12 Foundational AI Model Types

Let’s refresh some fundamentals today to stay fluent in the what we all work with. Here are some of the most popular model types that shape the vast world of AI (with examples in the brackets):

1. LLM - Large Language Model (GPT, LLaMA) -> Large Language Models: A Survey (2402.06196)
+ history of LLMs: https://www.turingpost.com/t/The%20History%20of%20LLMs
It's trained on massive text datasets to understand and generate human language. They are mostly build on Transformer architecture, predicting the next token. LLMs scale by increasing overall parameter count across all components (layers, attention heads, MLPs, etc.)

2. SLM - Small Language Model (TinyLLaMA, Phi models, SmolLM) A Survey of Small Language Models (2410.20011)
Lightweight LM optimized for efficiency, low memory use, fast inference, and edge use. SLMs work using the same principles as LLMs

3. VLM - Vision-Language Model (CLIP, Flamingo) -> An Introduction to Vision-Language Modeling (2405.17247)
Processes and understands both images and text. VLMs map images and text into a shared embedding space or generate captions/descriptions from both

4. MLLM - Multimodal Large Language Model (Gemini) -> A Survey on Multimodal Large Language Models (2306.13549)
A large-scale model that can understand and process multiple types of data (modalities) — usually text + other formats, like images, videos, audio, structured data, 3D or spatial inputs. MLLMs can be LLMs extended with modality adapters or trained jointly across vision, text, audio, etc.

5. LAM - Large Action Model (InstructDiffusion, RT-2) -> Large Action Models: From Inception to Implementation (2412.10047)
Understands and generates action sequences by predicting action tokens (discrete/continuous instructions) that guide agents. Trained on behavior datasets, LAMs generalize across tasks, environments, and modalities - video, sensor data, etc.

Read about LRM, MoE, SSM, RNN, CNN, SAM and LNN below👇

Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe

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reacted to reach-vb's post with 🔥 25 days ago

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Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🤯

Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥

Go, play with it today: https://huggingface.co/blog/inference-providers-featherless

P.S. They're also bringing on more GPUs to support all your concurrent requests!

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reacted to cbensimon's post with 🔥 25 days ago

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🚀 ZeroGPU now supports PyTorch native quantization via torchao

While it hasn’t been battle-tested yet, Int8WeightOnlyConfig is already working flawlessly in our tests.

Let us know if you run into any issues — and we’re excited to see what the community will build!

import spaces
from diffusers import FluxPipeline
from torchao.quantization.quant_api import Int8WeightOnlyConfig, quantize_

pipeline = FluxPipeline.from_pretrained(...).to('cuda')
quantize_(pipeline.transformer, Int8WeightOnlyConfig()) # Or any other component(s)

@spaces.GPU
def generate(prompt: str):
    return pipeline(prompt).images[0]

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reacted to AdinaY's post with 🚀 25 days ago

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Kimi-Dev 💻 New coding model by Moonshot AI

moonshotai/Kimi-Dev-72B

✨ 72B - MIT license
✨ 60.4% on SWE-bench Verified
✨ RL-trained to patch real repos in Docker
✨ Only rewarded if full test suite passes

reacted to merve's post with 🔥🧠 25 days ago

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IN: video fine-tuning support for

facebook V-JEPA 2 in HF transformers 🔥

it comes with
> four models fine-tuned on Diving48 and SSv2 dataset facebook/v-jepa-2-6841bad8413014e185b497a6
> FastRTC demo on V-JEPA2 SSv2 qubvel-hf/vjepa2-streaming-video-classification
> fine-tuning script on UCF-101 https://gist.github.com/ariG23498/28bccc737c11d1692f6d0ad2a0d7cddb
> fine-tuning notebook on UCF-101 https://colab.research.google.com/drive/16NWUReXTJBRhsN3umqznX4yoZt2I7VGc?usp=sharing
we're looking forward to see what you will build! 🤗

reacted to merve's post with 🔥 25 days ago

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fav open-source multimodal reasoning model just got an update 🔥

moonshotai/Kimi-VL-A3B-Thinking-2506 has
> smarter with less tokens, small size (only 3B active params!!!)
> better accuracy
> video reasoning
> higher resolution 🤯
Read their blog https://huggingface.co/blog/moonshotai/kimi-vl-a3b-thinking-2506

reacted to blaise-tk's post with 🚀 25 days ago

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A few months ago, I shared that I was building with @deeivihh something like "the Steam for open source apps"...

🚀 Today, I’m excited to announce that Dione is now open source and live in public beta!

Our mission is simple: make it easier to discover, use, and contribute to open source applications.

🔗 GitHub: https://github.com/dioneapp/dioneapp
💬 Join the community: https://discord.gg/JDFJp33vrM

Want to give it a try? I’d love your feedback! 👀

reacted to kanaria007's post with 👀 25 days ago

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✅ New Article on Hugging Face: Teaching AI to Learn from Its Own Thinking

Title:
🔁 Understanding the Pattern Learning Bridge: Adaptive Learning from Problem-Solving Experience
🔗 Read it here: https://huggingface.co/blog/kanaria007/understanding-the-pattern-learning-bridge

Summary:
After exploring structural selfhood in the Identity-Construct Protocol, this new piece introduces a next step in cognitive development: learning from one’s own problem-solving patterns.

The Pattern Learning Bridge equips AI with the ability to reflect structurally — not just on results, but on *why* certain reasoning paths succeed or fail.

This protocol enables agents to:

• Log reasoning attempts in a structured format
• Analyze success/failure correlations across problem types
• Extract reusable frame-jump patterns with confidence scoring
• Proactively adapt future reasoning choices

It’s not just about having memory — it’s about having *experience*.

Key Features:

• Detects recurring reasoning traps
• Weighs outcome likelihood and trap risk
• Enables confidence-weighted approach selection
• Evolves pattern reliability through continual use

The Pattern Learning Bridge integrates tightly with:

• jump-generator (reasoning mode selection)
• failure-trace-log (trap-driven diagnostics)
• memory-loop (pattern reuse)
• problem-readiness (pre-jump structural scanning)

🧠 Protocol Dataset: kanaria007/agi-structural-intelligence-protocols

Useful for:

• Developers building adaptive reasoning agents
• Researchers exploring AI metacognition and structural learning
• Architects designing traceable and self-correcting cognitive systems
• Anyone interested in how an AI can remember *why* its choices matter

This is not reinforcement learning.
This is structured learning from structured thought.
And it opens the door to systems that don’t just *solve* —
but *learn how they solved*.

reacted to fdaudens's post with 🔥 25 days ago

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You might not have heard of Moonshot AI — but within 24 hours, their new model Kimi K2 shot to the top of Hugging Face’s trending leaderboard.

So… who are they, and why does it matter?

Had a lot of fun co-writing this blog post with @xianbao , with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks.

🧵 A few standout facts:

1. From zero to $3.3B in 18 months:
Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan.

2. A CEO who thinks from the end:
Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs.

3. A trillion-parameter model that’s surprisingly efficient:
Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks.

4. The secret weapon: Muon optimizer:
A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications.

Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.”

👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs:
https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained

posted an update 7 months ago

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Checkout phi-4 from Microsoft, dropped a day ago... If you ❤️ the Phi series, then here is the GGUF - Sri-Vigneshwar-DJ/phi-4-GGUF. phi-4 is a 14B highly efficient open LLM that beats much larger models at math and reasoning - check out evaluations on the Open LLM.

Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting

reacted to cfahlgren1's post with 🔥 7 months ago

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Wow, I just added Langfuse tracing to the Deepseek Artifacts app and it's really nice 🔥

It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.

It was just added as a one click Docker Space template, so it's super easy to self host 💪

posted an update 7 months ago

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Just sharing a thought: I started using DeepSeek V3 a lot, and an idea struck me about agents "orchestrating during inference" on a test-time compute model like DeepSeek V3 or the O1 series.

Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.

posted an update 7 months ago

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Combining smolagents with Anthropic’s best practices simplifies building powerful AI agents:

1. Code-Based Agents: Write actions as Python code, reducing steps by 30%.
2. Prompt Chaining: Break tasks into sequential subtasks with validation gates.
3. Routing: Classify inputs and direct them to specialized handlers.
4. Fallback: Handle tasks even if classification fails.

https://huggingface.co/blog/Sri-Vigneshwar-DJ/building-effective-agents-with-anthropics-best-pra

reacted to as-cle-bert's post with 🔥 7 months ago

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🎉𝐄𝐚𝐫𝐥𝐲 𝐍𝐞𝐰 𝐘𝐞𝐚𝐫 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬🎉

Hi HuggingFacers🤗, I decided to ship early this year, and here's what I came up with:

𝐏𝐝𝐟𝐈𝐭𝐃𝐨𝐰𝐧 (https://github.com/AstraBert/PdfItDown) - If you're like me, and you have all your RAG pipeline optimized for PDFs, but not for other data formats, here is your solution! With PdfItDown, you can convert Word documents, presentations, HTML pages, markdown sheets and (why not?) CSVs and XMLs in PDF format, for seamless integration with your RAG pipelines. Built upon MarkItDown by Microsoft
GitHub Repo 👉 https://github.com/AstraBert/PdfItDown
PyPi Package 👉 https://pypi.org/project/pdfitdown/

𝐒𝐞𝐧𝐓𝐫𝐄𝐯 𝐯𝟏.𝟎.𝟎 (https://github.com/AstraBert/SenTrEv/tree/v1.0.0) - If you need to evaluate the 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 performance of your 𝘁𝗲𝘅𝘁 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 models, I have good news for you🥳🥳
The new release for 𝐒𝐞𝐧𝐓𝐫𝐄𝐯 now supports 𝗱𝗲𝗻𝘀𝗲 and 𝘀𝗽𝗮𝗿𝘀𝗲 retrieval (thanks to FastEmbed by Qdrant) with 𝘁𝗲𝘅𝘁-𝗯𝗮𝘀𝗲𝗱 𝗳𝗶𝗹𝗲 𝗳𝗼𝗿𝗺𝗮𝘁𝘀 (.docx, .pptx, .csv, .html, .xml, .md, .pdf) and new 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀!
GitHub repo 👉 https://github.com/AstraBert/SenTrEv
Release Notes 👉 https://github.com/AstraBert/SenTrEv/releases/tag/v1.0.0
PyPi Package 👉 https://pypi.org/project/sentrev/

Happy New Year and have fun!🥂

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reacted to AdinaY's post with 👀 8 months ago

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Sailor 2 🚢 open multilingual model for Southeast Asia by Sea AI Lab🔥

sailor2
sail/Sailor2-20B-Chat

✨ Fully open code & ALL datasets 🙌
✨ 1B/ 8B/20B base & chat expanded on Qwen2.5
✨ Apache 2.0
✨ Supports 15 languages including English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray🇬🇧🇨🇳🇱🇦🇲🇾🇲🇲🇻🇳🇹🇭

reacted to di-zhang-fdu's post with 🚀 8 months ago

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The first version of LLaMA-O1 has been uploaded to HF now!Here We Come!
Supervised:
SimpleBerry/LLaMA-O1-Supervised-1129
Base(Pretrain):
SimpleBerry/LLaMA-O1-Base-1127
Supervised Finetune Dataset:
SimpleBerry/OpenLongCoT-SFT
Pretraining Dataset:
SimpleBerry/OpenLongCoT-Pretrain-1202
RLHF is on the way! View our GitHub Repo:
https://github.com/SimpleBerry/LLaMA-O1
Our ongoing related researches:
Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B (2406.07394)
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning (2410.02884)
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning (2411.18203)
@AdinaY @akhaliq @jwu323
------
GGUF:https://huggingface.co/Lyte/LLaMA-O1-Supervised-1129-Q4_K_M-GGUF
online Demo (CPU-only): SimpleBerry/LLaMA-O1-Supervised-1129-Demo

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DJ Sri Vigneshwar PRO

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DJ Sri Vigneshwar PRO

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