Ghost 8B Beta 1608: Empowering Your AI Assistant
📦 Unlock the Power of Ghost 8B Beta 1608: Build Your Personal AI Companion
Ghost 8B Beta 1608 empowers you to create a safe and multilingual AI assistant tailored to your needs, directly on your personal computer. 🧑‍💻 Leverage AI's capabilities within your own space! 🚀 Ghost 8B Beta 1608 is ready to become your AI companion.
~
📦 개인용 AI 보조 도구로 Ghost 8B Beta 1608를 활용하세요!
Ghost 8B Beta 1608, AI의 힘을 활용하여 안전하고 개인화된 언어 지원을 제공하는 AI 보조 도구를 직접 구축할 수 있습니다. 🧑‍💻 개인 컴퓨터에서 AI의 혜택을 누리세요! 🚀 Ghost 8B Beta 1608는 당신의 AI 파트너가 될 준비가 되어 있습니다.
lamhieu/ghost-8b-beta-8k

ghost-x/ghost-8b-beta-668ead6179f93be717db4542

Looking for Generative AI trainer/speaker for AI accelerator program (Virtual/Online sessions).

To get more context about the program, please visit the program landing page: https://llamadesigndrive.com

Llamadesigndrive

Llama Design Drive

Llama Design Drive is an initiative by Meta to inspire impactful Large Language Model use cases in the business landscape of the region

a new shape-optimized SigLIP just dropped 👀
google/siglip-so400m-patch14-224

🌟 Liger Kernel: Efficient Triton Kernels for LLM Training

LIGER "is a [Hugging Face-compatible] collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%."

GitHub: https://github.com/linkedin/Liger-Kernel

GitHub

GitHub - linkedin/Liger-Kernel: Efficient Triton Kernels for LLM Training

Efficient Triton Kernels for LLM Training. Contribute to linkedin/Liger-Kernel development by creating an account on GitHub.

Neural Network  (1 Byte explainer for everybody)

Just like our brain, a Neural Network is made up of interconnected "neurons". These neurons work together by learning from (input) data and getting better at tasks (in the hidden layer) to give (output) predictions or decisions.

𝗔𝗜𝟮𝟭 𝗶𝘁𝗲𝗿𝗮𝘁𝗲𝘀 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗝𝗮𝗺𝗯𝗮 𝟭.𝟱 𝗿𝗲𝗹𝗲𝗮𝘀𝗲: 𝗡𝗲𝘄 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗹𝗼𝗻𝗴-𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘂𝘀𝗲-𝗰𝗮𝘀𝗲𝘀!🏅

@ai21labs used a different architecture to beat the status-quo Transformers models: Jamba architecture combines classic Transformers layers with the new Mamba layers, for which the complexity is a linear (instead of quadratic) function of the context length.

What does this imply?

➡️ Jamba models are much more efficient for long contexts: faster (up to 2.5x faster for long context), takes less memory, and also performs better to recall everything in the prompt.

That means it’s a new go-to model for RAG or agentic applications!

And the performance is not too shabby: Jamba 1.5 models are comparable in perf to similar-sized Llama-3.1 models! The largest model even outperforms Llama-3.1 405B on Arena-Hard.

✌️ Comes in 2 sizes: Mini (12B active/52B) and Large (94B active/399B)
📏 Both deliver 256k context length, for low memory: Jamba-1.5 mini fits 140k context length on one single A100.
⚙️ New quanttization method: Experts Int8 quantizes only the weights parts of the MoE layers, which account for 85% of weights
🤖 Natively supports JSON format generation & function calling.
🔓 Permissive license *if your org makes <$50M revenue*

Available on the Hub 👉
ai21labs/jamba-15-66c44befa474a917fcf55251

Read their release blog post 👉 https://www.ai21.com/blog/announcing-jamba-model-family

Ai21

The Jamba 1.5 Open Model Family: The Most Powerful and Efficient Long Context Models

The new family of open models from AI21, offering unrivaled speed, efficiency, and quality and the longest context window among open models.

The latest timm validation & test set results are now viewable by a leaderboard space:
timm/leaderboard


As of yesterday, I updated all of the results for ImageNet , ImageNet-ReaL, ImageNet-V2, ImageNet-R, ImageNet-A, and Sketch sets. The csv files can be found in the GH repo https://github.com/huggingface/pytorch-image-models/tree/main/results

Unfortunately the latest benchmark csv files are not yet up to date, there are some gaps in dataset results vs throughput/flop numbers impact the plots.

h/t to @MohamedRashad for making the first timm leaderboard.

GitHub

pytorch-image-models/results at main · huggingface/pytorch-image-models

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V...

Huge updates and improvements for FLUX LoRA training : https://www.patreon.com/posts/kohya-flux-lora-110293257

10 GB, 16 GB, 24 GB and 48 GB GPU configs added - 10 GB config is like 3x to 5x slower sadly

Massed Compute, RunPod and Windows Kohya SS GUI LoRA installers added to the zip file

Also right now testing new 16 GB FLUX LoRA training config and new way of regularization images. Moreover testing Apply T5 Attention Mask too. Lets see if Kohya FLUX LoRA workflow will become even better or not

Also massive grids comparisons shared here : https://www.reddit.com/r/StableDiffusion/comments/1eyj4b8/kohya_ss_gui_very_easy_f

Patreon

Kohya FLUX LoRA and Fine Tuning Training Full Tutorial For Local Windows and Cloud RunPod and Massed Compute for Research & Development…

Get more from SECourses: Tutorials, Guides, Resources, Training, FLUX, MidJourney, Voice Clone, TTS, ChatGPT, GPT, LLM, Scripts by Furkan Gözü on Patreon

Shoutout to everyone who participated in BigScience! Doesn't get enough credit but IMO paved the way for open-source LLMs!

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2211.05100)

bigscience/bloom

bigscience/bloomz
https://huggingface.co/bigscience/bloom

huggingface.co

bigscience/bloom · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

ACL 2024: The Missing Papers

Apparently, some papers from the ACL 2024 are still not listed in the ACL Anthology. While this issue will hopefully be fixed soon, we should give those papers additional spotlight.

Some of my favorites:

1. Dolma is an English corpus that encompasses 3 trillion tokens. Additionally, it is accompanied by an exceptional software package that consdierably advances the state-of-the-art in preparing data for LLM pretraining. (Source: I am currently using Dolma.)
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2402.00159)


2. In the paper "Same Task, More Tokens: the Impact of Input Length on
the Reasoning Performance of Large Language Models", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.
Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models (2402.14848)


This was brought to my attention through a Linkedin post by @ShayeghB, who is also affected:
Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree Averaging (2403.00143)


View all the missing papers here:
https://theshayegh.github.io/ACL2024MissingPapers/

ACL2024MissingPapers

ACL 2024 Missing Papers

A list of the missing papers in the proceedings of the ACL 2024.

v2ray/deepgelbooru

A Danbooru tag image tagger, maybe better than WD14 at some images.
Training code, inference code, dataset included.
:3
https://huggingface.co/v2ray/deepgelbooru

huggingface.co

v2ray/deepgelbooru · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

I can't believe this... Phi-3.5-mini (3.8B) running in-browser at ~90 tokens/second on WebGPU w/ Transformers.js and ONNX Runtime Web! 🤯 Since everything runs 100% locally, no messages are sent to a server — a huge win for privacy!
- 🤗 Demo:
webml-community/phi-3.5-webgpu

- 🧑‍💻 Source code: https://github.com/huggingface/transformers.js-examples/tree/main/phi-3.5-webgpu

GitHub

transformers.js-examples/phi-3.5-webgpu at main · huggingface/transformers.js-examples

A collection of 🤗 Transformers.js demos and example applications - huggingface/transformers.js-examples

You can now use DoRA for your embedding layers!

PR: https://github.com/huggingface/peft/pull/2006

I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.

Blog Post: https://huggingface.co/blog/ariG23498/peft-dora

Huge thanks to @BenjaminB for all the help I needed.

GitHub

[Add] DoRA Embedding by ariG23498 · Pull Request #2006 · huggingface/peft

This PR adds DoRA for the embedding layer.
TODOs:

Check whether this implementation is correct
Write tests for the implementation

@BenjaminBossan can you give me an initial review to set the to...

🌐 Check out the new dataset sourced from Fishki.net, one of the popular entertainment and news portals in the Russian Internet, known for its diverse content including humor, interesting facts, and viral stories -
nyuuzyou/fishkinet-posts
.

📊 Dataset highlights:
- 369,180 posts
- Includes original posts with titles, content, images, and metadata
- Each entry contains URL, title, author, date, tags, content, and image URLs
- Primarily in Russian language
- Covers a wide range of topics in entertainment, news, and social media content
- Spans nearly two decades of posts, likely from early 2000s to 2024
- Dedicated to public domain under Creative Commons Zero (CC0) license

Alan Turing's mind-bender: "Can machines think?" in its glorified form. This 74yr old paper laid the foundation for how we think about AI and machine intelligence today. The level of detail, clarity and foresight is just phenomenal - he was way ahead of his time 🧠🤖

Original copy: https://archive.org/details/MIND--COMPUTING-MACHINERY-AND-INTELLIGENCE

Internet Archive

I.—COMPUTING MACHINERY AND INTELLIGENCE : A.M Turing : Free Download, Borrow, and Streaming : Internet Archive

I.—COMPUTING MACHINERY ANDINTELLIGENCE1. The Imitation Game.I PROPOSE to consider the question, ' Can machines think ? 'This should begin with definitions of...

g/ - /ldg/ - Local Diffusion General - Technology

>Beginner UI
EasyDiffusion: https://easydiffusion.github.io
Fooocus: https://github.com/lllyasviel/fooocus
Metastable: https://metastable.studio

>Advanced UI
Automatic1111: https://github.com/automatic1111/stable-diffusion-webui
ComfyUI: https://github.com/comfyanonymous/ComfyUI
InvokeAI: https://github.com/invoke-ai/InvokeAI
SD.Next: https://github.com/vladmandic/automatic
SwarmUI: https://github.com/mcmonkeyprojects/SwarmUI

GitHub

GitHub - lllyasviel/Fooocus: Focus on prompting and generating

Focus on prompting and generating. Contribute to lllyasviel/Fooocus development by creating an account on GitHub.

Introducing HelpingAI2-9B, an emotionally intelligent LLM.
Model Link :
OEvortex/HelpingAI2-9B

Demo Link:
Abhaykoul/HelpingAI2


This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.

Key Features:
-----------------

* It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.
* It gives responses in empathetic and supportive manner.

Must try our demo:
Abhaykoul/HelpingAI2

NEW math-instruct model + dataset!

ValiantLabs/Llama3.1-8B-Cobalt
is our new math-instruct model.
Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here:
sequelbox/Polytope


More to come soon :)

Supercool Weekend Read🤖
Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.

Details on Techniques (Simplified):
They started off with a large pre-trained language model (15B params), then:

1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.

What Happens When RAG System Become Fully Vision-Language Model-Based?
HF Demo:
bokesyo/MiniCPMV-RAG-PDFQA

Multimodal Dense Retriever:
RhapsodyAI/minicpm-visual-embedding-v0

Generation Model:
openbmb/MiniCPM-V-2_6

Github: https://github.com/RhapsodyAILab/MiniCPM-V-Embedding-v0-Train

The Vision-Language Model Dense Retriever MiniCPM-Visual-Embedding-v0 reads PDFs directly -- no OCR required. With strong OCR capability and visual understanding capability, it generates multimodal dense representations, allowing you to build and search through your personal library with ease.

Ask a question, it retrieves the most relevant pages. Then, MiniCPM-V-2.6 provides answers based on the retrieved pages, with strong multi-image understanding capabilities.

Whether you’re working with a visually-intensive or text-oriented PDF, it helps you quickly find the information you need. You can also build a personal library with it.

It operates just like a human: reading, storing, retrieving, and answering with full visual comprehension.

Currently, the online demo supports PDFs with up to 50 pages due to GPU time limits. For longer PDFs or entire books, you can deploy it on your own machine.
https://cdn-uploads.huggingface.co/production/uploads/6415818a986557e8cac252bf/sjtQD7CFgox46h9EVHCG_.png

GitHub

GitHub - RhapsodyAILab/MiniCPM-V-Embedding

Contribute to RhapsodyAILab/MiniCPM-V-Embedding development by creating an account on GitHub.