hi All  I just shared Spaces and Article.

This key feature is Mediapipe face landmarker
Apache Licensed and trained with own dataset.
Good licensed model who use Flux.1 schnell instead of Dev

[Spaces]
Mediapipe Face detect
https://huggingface.co/spaces/Akjava/mediapipe-face-detect

Face crop and replace
https://huggingface.co/spaces/Akjava/mediapipe-face-crop-and-replace

Mediapipe 68 landmark
https://huggingface.co/spaces/Akjava/mediapipe-68-points-facial-landmark

Mediapipe 68 Face Mask
https://huggingface.co/spaces/Akjava/mediapipe-68-points-facial-mask

[Articles]
Better img2img results with Flux.1 schnell by using ScaleUp or Sharpen or FillColor pre-processing
https://huggingface.co/blog/Akjava/img2img-pre-processing


I turned a CNN into a GNN, then I trained it to play video games. Yup, I used graphs as the visual interface to feed to the model, and it works! I also used the laws of conservation of energy but I can't prove the causation only the correlation there. I call the complete framework I had to build out to pull of this off 'NeuroGraphRL'. Bet you never thought I'd be using graphs as eyeballs did you? I never thought you would be using tokens as words, but here we are!

https://youtu.be/DgTnZgnpg6E


Sometimes, we forget that all these LLMs are trained on just raw text. Ideally, they are simply text completion models. Imagine a model that keeps on writing follow-up questions when you ask, "How to make pizza?" rather than answering you!

That's where Instruction Tuning comes in—it’s a game-changer.

Instruction tuning has revolutionized how we interact with Large Language Models (LLMs), bridging the crucial gap between raw model capabilities and practical applications.

It’s what transforms a GPT into ChatGPT!

Think of instruction tuning as teaching AI to "speak human"—it's the difference between a model that merely predicts the next words and one that truly understands and executes our intentions.

The real magic? It enables zero-shot learning, meaning models can tackle new tasks they've never encountered before, as long as the instructions are clear. This versatility is what makes modern AI assistants so powerful and user-friendly.


DimensionX: Create Any 3D and 4D Scenes from a Single Image with
  Controllable Video Diffusion (2411.04928)

DimensionX is out for you to try and duplicate 🤗
—> https://huggingface.co/spaces/fffiloni/DimensionX

Discuss Paper: https://huggingface.co/papers/2411.04928

Examples by the amazing William Lamkin @phanes


𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉

𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉

𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐀𝐠𝐞𝐧𝐭𝐬 𝐫𝐞𝐚𝐜𝐡𝐞𝐬 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐨𝐟 𝐆𝐀𝐈𝐀 𝐥𝐞𝐚𝐝𝐞𝐫𝐛𝐨𝐚𝐫𝐝! 🥳

We've been improving Transformers Agents a lot lately.

So with @sergeipetrov we set out to prove that it's the best agent framework out there.

To prove this, we went to beat the 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱, the most comprehensive benchmark out there for evaluating LLM agents.
Its questions make you explore different flavours of pain:

🛠 𝗥𝗲𝗾𝘂𝗶𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀, at least a web browser
🔢 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗹𝗼𝗴𝗶𝗰, many questions having strong math aspects
🖼 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹, the agent had to handle all file types: 🔊, 🖼, 🎬...
👣 𝗠𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽, with many questions requiring over 10 steps to be solved.

Some Level 3 questions are crazy hard 😳
> "In NASA’s Astronomy Picture of the Day on 2006 January 21, two astronauts are visible, with one appearing much smaller than the other. As of August 2023, out of the astronauts in the NASA Astronaut Group that the smaller astronaut was a member of, which one spent the least time in space, and how many minutes did he spend in space, rounded to the nearest minute?"
(𝘯𝘰 𝘧𝘪𝘭𝘦 𝘢𝘵𝘵𝘢𝘤𝘩𝘦𝘥 𝘰𝘧 𝘤𝘰𝘶𝘳𝘴𝘦, 𝘵𝘩𝘦 𝘢𝘨𝘦𝘯𝘵 𝘩𝘢𝘴 𝘵𝘰 𝘧𝘪𝘯𝘥 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘪𝘯𝘧𝘰)

➡ We used Transformers Agents' React Code Agent, that writes its actions in code. We created a new planning component that we'll incorporate in the framework. More info soon in a blog post!

𝐑𝐞𝐬𝐮𝐥𝐭𝐬:
🚀 Our submission scores #2 overall on the test set and #1 on the validation set. On both sets we're the leading submission based on a public framework, beating Microsoft's Autogen.
🥇 On both sets we are #1 on the hardest Level 3 questions, reaching nearly 20%.

𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉 https://huggingface.co/spaces/gaia-benchmark/leaderboard


I am delighted to announce the publication of my LegalKit, a French labeled dataset built for legal ML training 🤗

This dataset comprises multiple query-document pairs (+50k) curated for training sentence embedding models within the domain of French law.

The labeling process follows a systematic approach to ensure consistency and relevance:
- Initial Query Generation: Three instances of the LLaMA-3-70B model independently generate three different queries based on the same document.
- Selection of Optimal Query: A fourth instance of the LLaMA-3-70B model, using a dedicated selection prompt, evaluates the generated queries and selects the most suitable one.
- Final Label Assignment: The chosen query is used to label the document, aiming to ensure that the label accurately reflects the content and context of the original text.

Dataset: https://huggingface.co/datasets/louisbrulenaudet/legalkit

Stay tuned for further updates and release information 🔥

@clem, if we can create an "HF for Legal" organization, similar to what exists for journalists, I am available!

Note : My special thanks to @alvdansen for their illustration models ❤

fffiloni ZeroWw Ramikan-BR ssatz davanstrien clem osanseviero baconnier

Ramikan-BR clem umseeker baconnier louisbrulenaudet LeroyDyer sbrandeis


ESM3 is up and running in a Space if you'd like to try it out: https://huggingface.co/spaces/colbyford/esm3

(You'll need to make a HF token and accept their license agreement to use the Space app.)

@AmelieSchreiber


Self-Play Preference Optimization for Language Model Alignment (2405.00675)

We've open-sourced the code and models for Self-Play Preference Optimization (SPPO)! 🚀🚀🚀
🤗paper: https://huggingface.co/papers/2405.00675
 ⭐ code: https://github.com/uclaml/SPPO
🤗models: https://huggingface.co/collections/UCLA-AGI/sppo-6635fdd844f2b2e4a94d0b9a


🤗 Hello from the Project Fluently team!

🥏 We are ready to announce a new series of Supple Diffusion models, these are new generation diffusion models (about 1-2 weeks left before release).

🦾 The new series aims to take diffusion models to the next level, with performance and versatility as the main goal.

🧐 How will our models be better than others? Firstly, we worked on the CLIP models, now they understand your requests better, it will become easier to process. Secondly, we trained the models with high quality, even better than all our previous ones. Thirdly, you won’t have to keep 20 models on your disk; only 4-6 will be enough.

🗺 Roadmap:
1. Create Supple Diffusion Small
2. Creating Supple Diffusion Medium
3. Create Supple Diffusion Large

🎆 Our models are universal for realism, and for cartoons, and for anime, and for caricatures.

💖 The project really needs your support and your recommendations and reviews, please do not hesitate to write comments under this post, thank you!

🖼 Below are demo images made with the pre-release version of Supple Diffusion Small.

ehristoforu YaTharThShaRma999 rossaai Pocholo testin1234 pshriv16 Tanayeb GPT007 rajsinghparihar Arakinas HDiffusion weiyuhsuan ganslmeier danielus Winnougan GooGooPanda dreamdrop-art Nymbo snakeying

ehristoforu YaTharThShaRma999 rossaai Tanayeb louisbrulenaudet GPT007 Winnougan sudzdpn dreamdrop-art Nymbo

ehristoforu YaTharThShaRma999 rossaai Tanayeb ManniX-ITA GPT007 Winnougan too-ai dreamdrop-art Nymbo

ehristoforu YaTharThShaRma999 rossaai NHLOCAL Tanayeb GPT007 Winnougan dreamdrop-art Nymbo

ehristoforu YaTharThShaRma999 rossaai Tanayeb GPT007 fengjj123 Winnougan dreamdrop-art Nymbo


Unsocial Intelligence: an Investigation of the Assumptions of AGI Discourse

I don't agree with some of the assertions made here, but it is an interesting paper and a good overview.

https://arxiv.org/abs/2401.13142


Per popular request, I'm working on a beginning to end LoRA training workflow blog for a style.

It will focus on dataset curation through training on a pre-determined style to give a better insight on my process.

Curious what are some questions you might have that I can try to answer in it?

YaTharThShaRma999 Noeloise louisbrulenaudet fffiloni eljanmahammadli attashe not-lain Imtiazsyed nicholaskao


I'm developing a tool to simplify finding datasets suitable for specific tasks or libraries. Although it's still a work in progress, I've compiled a collection of datasets that likely support DPO: https://huggingface.co/collections/davanstrien/probably-dpo-datasets-667c409a557fe99a9ed39f0b


It is time for some Aura.

First in our series of fully open sourced / commercially available models by @fal-ai: AuraSR - a 600M parameter upscaler based on GigaGAN.

Blog: https://blog.fal.ai/introducing-aurasr-an-open-reproduction-of-the-gigagan-upscaler-2/

HF: https://huggingface.co/fal-ai/AuraSR

Code: https://github.com/fal-ai/aura-sr

Playground: https://fal.ai/models/fal-ai/aura-sr/playground

What other models would you like to see open-sourced and commercially available? :)


Look at that 👀

Actual benchmarks have become too easy for recent models, much like grading high school students on middle school problems makes little sense. So the team worked on a new version of the Open LLM Leaderboard with new benchmarks.

Stellar work from @clefourrier @SaylorTwift and the team!

👉 Read the blog post: https://huggingface.co/spaces/open-llm-leaderboard/blog
👉 Explore the leaderboard: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

anakin87 clefourrier SaylorTwift dillfrescott louisbrulenaudet


𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚 👇

𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚 👇

🔹llamafile 🦙🗂  to run our model locally.

🔹llamafile 🦙🗂  to run our model locally.

🌌 Creating adventures with local LLMs

What if 🤔... Homer Simpson met Spider-Man and they went on a quest for donuts? 🍩
Or if Fred Astaire and Corporal Hicks teamed up to fight xenomorphs? 👾

In the words of Karpathy, LLMs are dream machines...
they seem specially made to simulate these wild scenarios!

𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚 👇
Nous Research / @teknium  recently released https://huggingface.co/datasets/NousResearch/CharacterCodex:
a massive dataset with information on 16k characters, both fictional and real.
I couldn't wait to play it...

After a few attempts, I found that combining the information in this dataset with a good model (like https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) opens the doors to a myriad of chat adventures.

🛠 Stack:
🔹Haystack for orchestration 🏗
🔹llamafile 🦙🗂  to run our model locally.

📓 Check out the notebook: https://t.ly/y6jrZ
(includes a bonus 🕵 Mystery Character Quiz)


LLaVA 1.6 - 34 billion parameters - loaded as 8-bit precision caption. Uses around 37 GB VRAM on Massed Compute with our installers (31 cents per hour with SECourses coupon).  A new tutorial for this will be made hopefully soon.


Just shipped: introduction to vision language models (aka image-text-to-text) https://huggingface.co/tasks/image-text-to-text

Learn about more machine learning tasks at https://huggingface.co/tasks

dblasko gokaygokay John6666 ucsahin damerajee hewliyang


Hi everyone!
I'm Alex, I'm 16, I've been an internship at Hugging Face for a little over a week and I've already learned a lot about using and prompting LLM models.  With @victor as tutor I've just finished a space that analyzes your feelings by prompting an LLM chat model. The aim is to extend it so that it can categorize hugging face posts.

https://huggingface.co/spaces/alex-abb/LLM_Feeling_Analyzer

jgitsolutions dblasko victor nefnef Linaqruf Wauplin clem thomwolf mfuntowicz HugoLaurencon Narsil davidberenstein1957 joaogante ArthurZ SixOpen not-lain Sylvestre meganariley davanstrien pierrci osanseviero DavidGF adamelliotfields louisbrulenaudet nataliaElv lvwerra azzr-duke tengomucho gabrielmbmb julien-c eustlb sayakpaul reach-vb RoacherM agenkit Rexhaif mishig Cuiunbo VishvaNCS sted97 kramp jcudit MKSH pcuenq

Norod78 clem thomwolf OzzyGT mfuntowicz davidberenstein1957 not-lain osanseviero varuy322 reach-vb mishig frederikai pcuenq

davidberenstein1957 osanseviero reach-vb agenkit mishig pcuenq

davidberenstein1957 ArthurZ eustlb reach-vb pcuenq


Hello!

I've been in the lab playing with various data formats today, and jammed out with some plain text to produce a nice list of fallacies and their solutions from Wikipedia's List of fallacies as JSONL for data processing.

Had some bumps along the way, but me and Gemini 1.5 Pro got there in the end. I must really learn to work with Gemini 1.5 Flash more effectively in future.

https://huggingface.co/datasets/MrOvkill/fallacies-list-wikipedia

Enjoy!

```
-<3
```


Looking to move away from OpenAI's closed-source models?

We've made switching from to open-source as easy as possible with a drop-in replacement OpenAI’s chat completion endpoint. You can specify any model you'd like in just a few lines of code. We call it the OpenAI Switch Kit.

Check out the docs here: https://postgresml.org/docs/guides/opensourceai

Or the blog post here: https://postgresml.org/blog/introducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes

Happy building!


Recently, we open-sourced YaFSDP, Yandex’s tool for efficient distributed training of LLMs.

Here are some of the key ideas used in YaFSDP to provide speedup and memory savings over FSDP:
• Allocate and utilize just two buffers throughout the transformer for all collected weights to circumvent the torch memory allocator;
• Gather small normalization layers at the beginning of the iteration and average the gradients only at the end;
• Move gradient division to the very end of the backward pass.

To learn more about how YaFSDP works, check out our latest blog post: https://medium.com/yandex/yafsdp-a-tool-for-faster-llm-training-and-optimized-gpu-utilization-is-no-632b7539f5b3


Hello beautiful people.
I wanted to thank everyone that read my blogpost and I am glad to share that we have achieved 11000 readers 🥳
I couldn't have done this without you, so once again thanks a lot everyone for the support 💖
If you haven't already you can read my blog post at: https://huggingface.co/blog/not-lain/rag-chatbot-using-llama3


🌍 Cohere for AI has announced that this July and August, it is inviting researchers from around the world to join Expedition Aya, a global initiative focused on launching projects using multilingual tools like Aya 23 and Aya 101. 🌐

Participants can start by joining the Aya server, where all organization will take place. They can share ideas and connect with others on Discord and the signup sheet. Various events will be hosted to help people find potential team members. 🤝

To support the projects, Cohere API credits will be issued. 💰

Over the course of six weeks, weekly check-in calls are also planned to help teams stay on track and receive support with using Aya. 🖥

The expedition will wrap up at the end of August with a closing event to showcase everyone’s work and plan next steps. Participants who complete the expedition will also receive some Expedition Aya swag. 🎉

Links:
Join the Aya Discord: https://discord.com/invite/q9QRYkjpwk
Visit the Expedition Aya Minisite: https://sites.google.com/cohere.com/expedition-aya/home


Florence-2 has a great capability of detecting various objects in a zero-shot setting with the task prompt "<OD>". However, if you want to detect specific objects that the base model is not able to in its current form, you can easily finetune it for this particular task. Below I show how to finetune the model to detect tables in a given image, but a similar process can be applied to detect any objects. Thanks to @andito, @merve, and @SkalskiP for sharing the fix for finetuning the Florence-2 model. Please also check their great blog post at https://huggingface.co/blog/finetune-florence2.

Colab notebook: https://colab.research.google.com/drive/1Y8GVjwzBIgfmfD3ZypDX5H1JA_VG0YDL?usp=sharing
Finetuned model: https://huggingface.co/ucsahin/Florence-2-large-TableDetection

andito qubvel-hf John6666 Metin osanseviero sosoai kevinjeswani danelcsb dblasko maddosaientisuto radames


I'm decentralizing my AI end2end, from the AI model distribution to on device AI inferencing. llama-ipfs - llama.cpp integrated with Interplanetary File System for distributing peer2peer and loading AI models without the need for cloud storage or AI model Hub.

llama.cpp now supports decentralized inferencing with RPC, allowing the distribution of workload across all home devices. This functionality can be enhanced with a P2P ad-hoc VPN, enabling the extension of distributed inferencing to any device on any network.

Imagine an open-source AI that's as decentralized as a potluck dinner - everyone brings something to the table, and there's ZERO need for blockchain. It's like a digital fortress, with security and privacy baked right in, not to mention a dollop of integrity and trust. This could be the secret sauce for an enterprise AI platform, complete with an integrated IT policy. It might just be the cherry on top for the next generation of Apple Intelligence and Copilot+ PCs.

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.

rvpierre Ramikan-BR graphicaldot brainhome sa8 pegak


NEW #DecentralizeAI Writing Contest, by InternetComputer.org and HackerNoon.com!  😜 https://www.contests.hackernoon.com/decentralize-ai-writing-contest 🤪

"Not going to beat centralized AI with more centralized AI." - Emad Mostaque

To enter, submit a blog post with the #decentralize-ai tag on HackerNoon.


Use GPT-4o + GPT-4-Turbo-Preview + GPT-3.5-Turbo + BingAI

https://huggingface.co/spaces/NiansuhAI/Copilot


Were you aware that we have a dedicated guide on different prompting mechanisms to improve the image generation quality? 🧨

Takes you through simple prompt engineering, prompt weighting, prompt enhancement using GPT-2, and more.

Check out the guide here 🦯
https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted_prompts

ashikurrahman GPT007 linoyts louisbrulenaudet radames chuangxinlezhi mynkchaudhry


I've noticed  some people are still downloading https://huggingface.co/neph1/sd-seer-griffin-3b
Should I make an update based on a more modern architecture? (griffin-3b is llama (1!))


📢 Interested in #LLM safety?

We have just uploaded a new version of ALERT 🚨 on ArXiv with novel insights into the weaknesses and vulnerabilities of LLMs! 👀 https://arxiv.org/abs/2404.08676

For a summary of the paper, read this blog post: https://huggingface.co/blog/sted97/alert 🤗


📣Thrilled to make public our recent work ENVISIONS !!!

- Without human annotations !
- Without Distilling Strong LLMs !
- Self-improve LLMs in the environment
- Amazing performances on agentic and reasoning tasks
- Insightful analysis on "why" questions

📝 Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models

📎 Repo: https://github.com/xufangzhi/ENVISIONS


🚀 KARAKURI LM 8x7B Instruct v0.1

KARAKURI Inc. has publicly released "KARAKURI LM 8x7B Instruct v0.1", the first domestic Large Language Model (LLM) in Japan to support Function calling and Retrieval-Augmented Generation (RAG). This AI agent can handle tasks across various applications autonomously, significantly reducing implementation costs compared to traditional models.

Model Features:
- Capable of autonomously choosing optimal documents and databases for various tasks.
- Applied extensively in customer support for automating responses and processes, analyzing Voice of Customer (VoC), and predicting optimal outreach timings.

Model URL:
https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-instruct-v0.1

Detailed press release (in Japanese):
https://karakuri.ai/seminar/news/karakuri-lm-8x7b-instruct-v0-1/


Doraemon AI your future friend

https://hf.co/chat/assistant/667a0d8482b5bcd065dd882f


Judging the Judges: Evaluating Alignment and Vulnerabilities in
  LLMs-as-Judges (2406.12624)

𝐂𝐚𝐧 𝐋𝐋𝐌𝐬 𝐬𝐞𝐫𝐯𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐣𝐮𝐝𝐠𝐞𝐬 ⚖?

𝐂𝐚𝐧 𝐋𝐋𝐌𝐬 𝐬𝐞𝐫𝐯𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐣𝐮𝐝𝐠𝐞𝐬 ⚖?

𝗝𝘂𝗱𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗝𝘂𝗱𝗴𝗲𝘀: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗟𝗟𝗠𝘀-𝗮𝘀-𝗝𝘂𝗱𝗴𝗲𝘀
https://huggingface.co/papers/2406.12624

𝐂𝐚𝐧 𝐋𝐋𝐌𝐬 𝐬𝐞𝐫𝐯𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐣𝐮𝐝𝐠𝐞𝐬 ⚖?

We aim to identify the right metrics for evaluating Judge LLMs and understand their sensitivities to prompt guidelines, engineering, and specificity. With this paper, we want to raise caution ⚠ to blindly using LLMs as human proxy.

Blog - https://huggingface.co/blog/singh96aman/judgingthejudges
Arxiv - https://arxiv.org/abs/2406.12624
Tweet - https://x.com/iamsingh96aman/status/1804148173008703509

@singh96aman @kartik727 @Srinik-1 @sankaranv @dieuwkehupkes


How to generate LLM embeddings with open source models from Hugging Face 🤗 in PostgresML.

This article is the first in a multipart series that will show you how to build a post-modern semantic search and recommendation engine.

➡ https://postgresml.org/blog/generating-llm-embeddings-with-open-source-models-in-postgresml

PostgresML is a backend for your AI app that unifies LLMs w/ vector memory + embedding generation + reranking & pruning models — all in a single process for better performance.

We're always looking for ways to make PostgresML better — let us know what you think!


Fine-tune Florence-2 on any task 🔥

Today we release a notebook and a walkthrough blog on fine-tuning Florence-2 on DocVQA dataset @andito @SkalskiP

Blog: https://huggingface.co/blog 📕
Notebook: https://colab.research.google.com/drive/1hKDrJ5AH_o7I95PtZ9__VlCTNAo1Gjpf?usp=sharing 📖
Florence-2 is a great vision-language model thanks to it's massive dataset and small size!

This model requires conditioning through task prefixes and it's not as generalist, requiring fine-tuning on a new task, such as DocVQA 📝

We have fine-tuned the model on A100 (and one can also use a smaller GPU with smaller batch size) and saw that model picks up new tasks 🥹

See below how it looks like before and after FT 🤩
Play with the demo here https://huggingface.co/spaces/andito/Florence-2-DocVQA 🏄♀

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DELLA-Merging: Reducing Interference in Model Merging through
  Magnitude-Based Sampling (2406.11617)

🎉  We are thrilled to share our work on model merging. We proposed a new approach, Della-merging, which combines expert models from various domains into a single, versatile model. Della employs a magnitude-based sampling approach to eliminate redundant delta parameters, reducing interference when merging homologous models (those fine-tuned from the same backbone).

Della outperforms existing homologous model merging techniques such as DARE and TIES. Across three expert models (LM, Math, Code) and their corresponding benchmark datasets (AlpacaEval, GSM8K, MBPP), Della achieves an improvement of 3.6 points over TIES and 1.2 points over DARE.

Paper: https://huggingface.co/papers/2406.11617
Github: https://github.com/declare-lab/della

@soujanyaporia @Tej3


Hello!

Fixed Moondream 2 Multi-Interrogation, ( Use ZeroGPU correctly, Sam. *doink* )
Located here:

 https://huggingface.co/spaces/MrOvkill/moondream-2-multi-interrogation

Also, uploaded pdox-reversed to include some new fields, my bad for not putting the Paradox name in from the start. All good now.

https://huggingface.co/datasets/MrOvkill/pdox-reversed

osanseviero Ramikan-BR dillfrescott kaltre


@dwancin Can you please reset your toggle component's space? It's stuck for some reason. Happy to help

https://huggingface.co/spaces/dwancin/gradio_toggle


A few new styles added as SDXL LoRA:

Midsommar Cartoon
A playful cartoon style featuring bold colors and a retro aesthetic. Personal favorite at the moment.
https://huggingface.co/alvdansen/midsommarcartoon
---
Wood Block XL
I've started training public domain styles to create some interesting datasets. In this case I found a group of images taken from really beautiful and colorful Japanese Blockprints.
https://huggingface.co/alvdansen/wood-block-xl
--
Dimension W
For this model I did actually end up working on an SD 1.5 model as well as an SDXL. I prefer the SDXL version, and I am still looking for parameters I am really happy with for SD 1.5. That said, both have their merits. I trained this with the short film I am working on in mind.
https://huggingface.co/alvdansen/dimension-w
https://huggingface.co/alvdansen/dimension-w-sd15


Last week, Intel's new Xeon CPUs, Sapphire Rapids (SPR), landed on Inference Endpoints and I think they got the potential to reduce the cost of your RAG pipelines 💸

Why ? Because they come with Intel® AMX support, which is a set of instructions that support and accelerate BF16 and INT8 matrix multiplications on CPU ⚡

I went ahead and built a Space to showcase how to efficiently deploy embedding models on SPR for both Retrieving and Ranking documents, with Haystack compatible components: https://huggingface.co/spaces/optimum-intel/haystack-e2e

Here's how it works:

- Document Store: A FAISS document store containing the seven-wonders dataset, embedded, indexed and stored on the Space's persistent storage to avoid unnecessary re-computation of embeddings.

- Retriever: It embeds the query at runtime and retrieves from the dataset N documents that are most semantically similar to the query's embedding.
We use the small variant of the BGE family here because we want a model that's fast to run on the entire dataset and has a small embedding space for fast similarity search. Specifically we use an INT8 quantized bge-small-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

- Ranker: It re-embeds the retrieved documents at runtime and re-ranks them based on semantic similarity to the query's embedding. We use the large variant of the BGE family here because it's optimized for accuracy allowing us to filter the most relevant k documents that we'll use in the LLM prompt. Specifically we use an INT8 quantized bge-large-en-v1.5, deployed on an Intel Sapphire Rapids CPU instance.

Space: https://huggingface.co/spaces/optimum-intel/haystack-e2e
Retriever IE: https://huggingface.co/optimum-intel/fastrag-retriever
Ranker IE: https://huggingface.co/optimum-intel/fastrag-ranker

davidberenstein1957 louisbrulenaudet IlyasMoutawwakil anakin87 mfuntowicz lunarflu not-lain nbroad

IlyasMoutawwakil mfuntowicz lunarflu not-lain jeffboudier nbroad


So far I've implemented more accurate 👌 assessment of LLMs reasoning capabilities in Target Sentiment Analysis (zero-shot mode).  With that, recalculated tables of the related benchmark 📊 also has better separation into categories, with the following 🏆 top 🏆 performing models:

🟩 1. Proprietary models (🏆 GPT-4  🇺🇸 / GPT-3.5-0613  🇷🇺 )
🟥 2. Open and < 100B (🏆 LLaMA-3-70B)
🟧 3. Open and < 10B (🏆LLaMA-3-8B-Instruct 🇺🇸 / Qwen-2-7B-Instruct 🇷🇺)
🟨 4. Open and less 1B (🏆Flan-T5-large 🇺🇸 / Qwen2-0.5B-Instruct 🇷🇺)

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark


I'm decentralizing my AI. I'll be using Radicle for decentralized Git and IPFS for distributing AI models.

I believe there is a significant opportunity to democratize open AI development moving forward. I appreciate that Radicle is open-source, prioritizes local operations, functions offline, seeds data peer-to-peer from my node, is programmable, and incorporates built-in security features.

IPFS is great decentralized data storage, and I have already begun seeding SLMs and LoRa adapters. Tomorrow will add my collection of LLMs, VLMs, etc models and datasets I'm actively using. I have 10Gbps fiber optics at home so my node has enough bandwidth.

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.


Hey everyone!

I'm excited to share a new demo for my ChartInstruct model from our ACL 2024 paper. It excels at various chart understanding tasks like QA, captioning, open-ended QA, fact checking and more!
Thanks to Hugging Face's ZeroGPU program, the demo runs smoothly even with the model's 7B parameters!

Check it out and enjoy!

Demo: https://huggingface.co/spaces/ahmed-masry/ChartInstruct-LLama2
Model: https://huggingface.co/ahmed-masry/ChartInstruct-LLama2
Paper: https://arxiv.org/abs/2403.09028


Hello!

I've made a little evaluation dataset for LLMs that require advanced and convoluted logical reasoning. It's composed of 81 unique paradoxes, with admittedly a couple in the same category ( absolutes. ) It's available here: https://huggingface.co/datasets/MrOvkill/pdox

**Update**: I have upgraded the dataset to v3, ( don't worry about v2, it can be forgotten... ) and placed in a separate repo here:
https://huggingface.co/datasets/MrOvkill/pdox-reversed

Enjoy & Have fun!
`-<3`

YaTharThShaRma999 SaylorTwift osanseviero Ramikan-BR nbroad


Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI

https://youtu.be/HKX8_F1Er_w

Do not skip any part of this tutorial to master how to use Stable Diffusion 3 (SD3) with the most advanced generative AI open source APP SwarmUI. Automatic1111 SD Web UI or Fooocus are not supporting the #SD3 yet. Therefore, I am starting to make tutorials for SwarmUI as well. #StableSwarmUI is officially developed by the StabilityAI and your mind will be blown after you watch this tutorial and learn its amazing features. StableSwarmUI uses #ComfyUI as the back end thus it has all the good features of ComfyUI and it brings you easy to use features of Automatic1111 #StableDiffusion Web UI with them. I really liked SwarmUI and planning to do more tutorials for it.

🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡ https://www.patreon.com/posts/stableswarmui-3-106135985

0:00 Introduction to the Stable Diffusion 3 (SD3) and SwarmUI and what is in the tutorial
4:12 Architecture and features of SD3
5:05 What each different model files of Stable Diffusion 3 means
6:26 How to download and install SwarmUI on Windows for SD3 and all other Stable Diffusion models
8:42 What kind of folder path you should use when installing SwarmUI
10:28 If you get installation error how to notice and fix it
11:49 Installation has been completed and now how to start using SwarmUI
12:29 Which settings I change before start using SwarmUI and how to change your theme like dark, white, gray
12:56 How to make SwarmUI save generated images as PNG
13:08 How to find description of each settings and configuration
13:28 How to download SD3 model and start using on Windows
13:38 How to use model downloader utility of SwarmUI
14:17 How to set models folder paths and link your existing models folders in SwarmUI
14:35 Explanation of Root folder path in SwarmUI
14:52 VAE of SD3 do we need to download?

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I've made an on device AI comparison between open source, Apple Intelligence, and Microsoft Copilot+ PC. This OS and applications level integration will bring GenAI to everyone, be it consumers or businesses, over the next year.

Communities and BigTech hold divergent visions regarding the problems they aim to solve, ways to lock in users and enterprises, as well as their commercialization and GTM strategies.

I'm aware that this table has the potential to expand into an epic 30-page saga during an in-depth analysis, but hey, it's a beginning. Do you think I should throw in a few more comparisons? I'm all ears for your thoughts and critiques!

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it

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What is your favorite part of our Diffusers integration of Stable Diffusion 3?

My personal favorite is the ability to run it on a variety of different GPUs with minimal code changes.

Learn more about them here:
https://huggingface.co/blog/sd3

prithivMLmods KingNish osanseviero jasstionzyf Ramikan-BR radames GPT007


Updated the Journalists on 🤗 community page:
- new text-to-speech tools collection https://huggingface.co/collections/JournalistsonHF/text-to-speech-6675c4dccdaa11e86928a15b
- additional leaderboards in the eval collection: https://huggingface.co/spaces/TTS-AGI/TTS-Arena and https://huggingface.co/spaces/dylanebert/3d-arena
- new tools in the Text-Analysis collection: https://huggingface.co/spaces/gokaygokay/Florence-2, https://huggingface.co/spaces/pdf2dataset/pdf2dataset, https://huggingface.co/spaces/cvachet/pdf-chatbot
- https://huggingface.co/spaces/Xenova/realtime-whisper-webgpu in the Transcription collection
- https://huggingface.co/spaces/radames/flash-sd3-taesd3 in the Image Tools collection
- Last but not least, https://huggingface.co/spaces/okaris/omni-zero in the fun collection for zero-shot stylized portrait creation

Is there any tool you would like to see added?

Find all the curated tools here: https://huggingface.co/collections/JournalistsonHF/

cedpsam osanseviero Javedalam Ramikan-BR


Finally, a good handwriting recognition tool?

I'm impressed by Microsoft's latest vision model, Florence-2 https://huggingface.co/microsoft/Florence-2-large

The results are really good, boasting a remarkably low error rate, as you can see with this letter from George W. Bush to Bill Clinton!

🚀🔒 What’s even better? You can run it locally on your device, ensuring your data stays 100% safe.

👉 Try it out here: https://huggingface.co/spaces/gokaygokay/Florence-2

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@Be-Bo
Dear Mr. Bahaa Shamoon Atia,
My name is Krischan Schoeninger, and I am very impressed with your Llama 3-70B Chatbot that you have made available on Hugging Face. I have been trying to use both your chatbot and the model from Hugging Face via API for a project, and I have found that your model produces significantly better results.
Could you please let me know what changes or optimizations you have made to your model that make it so powerful? Additionally, I am very interested in learning how I can host such a model myself. Could you assist me with this?
I would greatly appreciate your feedback.
Best regards,
Krischan Schoeninger


Several methods/models have recently been shared to generate synthetic data from minimal or no initial seeds, essentially creating data directly from raw text.

IMO, these approaches that rely on smaller models for synthetic data generation are quite valuable for scaling up synthetic data and democratizing access to creating domain-specific synthetic datasets.

I've compiled a collection of Gradio demos showcasing some of these methods here: https://huggingface.co/collections/davanstrien/synthetic-data-generation-demos-667573f248b97360ff3668a5

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4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities (2406.09406)

EPFL and Apple (at @EPFL-VILAB)  just released 4M-21: single any-to-any model that can do anything from text-to-image generation to generating depth masks! 🙀
4M is a multimodal training framework introduced by Apple and EPFL.
Resulting model takes image and text and output image and text 🤩

Models: https://huggingface.co/collections/EPFL-VILAB/4m-models-660193abe3faf4b4d98a2742
Demo: https://huggingface.co/spaces/EPFL-VILAB/4M
Paper: https://huggingface.co/papers/2406.09406

This model consists of transformer encoder and decoder, where the key to multimodality lies in input and output data:

input and output tokens are decoded to generate bounding boxes, generated image's pixels, captions and more!

This model also learnt to generate canny maps, SAM edges and other things for steerable text-to-image generation 🖼

The authors only added image-to-all capabilities for the demo, but you can try to use this model for text-to-image generation as well ☺

Tonic YaTharThShaRma999 radames KingNish LeroyDyer Jebadiah Ramikan-BR louisbrulenaudet

Tonic YaTharThShaRma999 Taylor658 radames LeroyDyer Jebadiah Ukdah Ramikan-BR

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Tonic YaTharThShaRma999 LeroyDyer Jebadiah Ramikan-BR

Tonic YaTharThShaRma999 Jebadiah Ramikan-BR


The new Claude Sonnet 3.5 model from Anthropic AI has been getting good reviews on since last night. It is quite good at coding related tasks. We tried it on the Static Analysis Eval benchmark (https://huggingface.co/datasets/patched-codes/static-analysis-eval) which measures the ability of a LLM to fix vulnerabilities. The model scores 59.21% which is good but not better than other frontier models (like GPT-4, Gemini-1.5 and LLama-3).


Hey all!

Here I take a somewhat strong stance and am petitioning to revisit the default training parameters on the Diffusers LoRA page.

In my opinion and after observing and testing may training pipelines shared by startups and resources, I have found that many of them exhibit the same types of issues. Upon discussing with some of these founders and creators, the common theme has been working backwards from the Diffusers LoRA page.

In this article, I explain why the defaults in the Diffuser LoRA code produce some positive results, which can be initially misleading, and a suggestion on how that could be improved.

https://huggingface.co/blog/alvdansen/revisit-diffusers-default-params


GLiNER multi-task: Generalist Lightweight Model for Various Information
  Extraction Tasks (2406.12925)

We’re thrilled to share our latest technical paper on the multi-task GLiNER model. Our research dives into the following exciting and forward-thinking topics:

🔍 Zero-shot NER & Information Extraction: We demonstrate that with diverse and ample data, paired with the right architecture, encoders can achieve impressive results across various extraction tasks;

🛠 Synthetic Data Generation: Leveraging open labelling by LLMs like Llama, we generated high-quality training data. Our student model even outperformed the teacher model, highlighting the potential of this approach.

🤖 Self-Learning: Our model showed consistent improvements in performance without labelled data, achieving up to a 12% increase in F1 score for initially challenging topics. This ability to learn and improve autonomously is a very perspective direction of future research!

https://huggingface.co/papers/2406.12925
https://huggingface.co/knowledgator/gliner-multitask-large-v0.5
https://huggingface.co/spaces/knowledgator/GLiNER_HandyLab

```
#!pip install gliner -U

from gliner import GLiNER

model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5")

text = """
Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800.
"""

labels = ["founder", "computer", "software", "position", "date"]

entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["text"], "=>", entity["label"])
```


I'm about to start storing files in my TFLOPS count..

mazenandmariah YaTharThShaRma999 lunarflu not-lain


I am excited to share Synthetic Data Workshop, a Space that aims to simplify creating synthetic datasets!

✅ Pre-configured environment
✅ Ready-to-use notebooks
✅ No local GPU needed

You can try the Space here: https://huggingface.co/spaces/davanstrien/synthetic-data-workshop

I also wrote a blog post going into more detail about the motivations for the Space: https://huggingface.co/blog/davanstrien/synthetic-data-workshop


🔍 A recently published technical report introduces MINT-1T, a dataset that will considerably expand open-source multimodal data. It features one trillion text tokens and three billion images and is scheduled for release in July 2024.

Researcher Affiliation:

University of Washington
Salesforce Research
Stanford University
University of Texas at Austin
University of California, Berkeley

Paper:
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
https://arxiv.org/pdf/2406.11271v1.pdf

GitHub:
https://github.com/mlfoundations/MINT-1T

Highlights:

MINT-1T Dataset: Largest open-source multimodal interleaved dataset with 1 trillion text tokens & 3 billion images. 📊🖼
Diverse Sources: Incorporates data from HTML, PDFs, and ArXiv documents. 📄📚
Open Source: Dataset and code will be released at https://github.com/mlfoundations/MINT-1T. 🌐🔓
Broader Domain Representation: Uses diverse data sources for balanced domain representation. 🌍📚
Performance in Multimodal Tasks: The dataset’s scale and diversity should enhance multimodal task performance. 🤖💡

Datasheet Information:

Motivation: Addresses the gap in large-scale open-source multimodal datasets. 🌐📊
Composition: 927.6 million documents, including HTML, PDF, and ArXiv sources. 📄📚
Collection Process: Gathered from CommonCrawl WARC and WAT dumps, with rigorous filtering. 🗂🔍
Preprocessing/Cleaning: Removal of low-quality text, duplicates and anonymization of sensitive information. 🧹🔒
Ethical Considerations: Measures to ensure privacy and avoid bias. ⚖🔏
Uses: Training multimodal models, generating interleaved image-text sequences, and building retrieval systems. 🤖📖


Towards a Dynamic 2.0 Model of Generative Intelligence

https://empereur-pirate.medium.com/towards-a-dynamic-2-0-model-of-generative-intelligence-6128d64fb523

This article explores the limitations of current AI language models, which do not allow user contributions and restrict freedom of expression through corporate censorship. It argues for a participatory approach to AI development, emphasizing the need for open and transparent models that integrate user feedback and contributions. The article highlights the importance of network neutrality and the potential of AI to enhance creativity and collective intelligence. It calls for political and institutional support to foster innovation in AI, ensuring that these technologies respect fundamental rights and promote qualitative performance through inclusivity and transparency.


Florence-2 is a new vision foundation model capable of a wide variety of tasks 🤯
Demo 👉🏻 https://huggingface.co/spaces/gokaygokay/Florence-2
Collection 👉🏻 https://huggingface.co/collections/microsoft/florence-6669f44df0d87d9c3bfb76de

This model can handle tasks that vary from OCR to semantic segmentation.

The difference from previous models is that the authors have compiled a dataset consisting of 126M images with 5.4B annotations labelled with their own data engine pseudolabelled by smaller specialized models and APIs.

The model has a similar architecture to previous models: an image encoder and a multimodality encoder with a text decoder. The authors have compiled the multitask dataset with prompts for each task.

You can also fine-tune this model on any task of choice. The authors also released different results on downstream tasks and reported their results when un/freezing the vision encoder 🤓📉
They have released fine-tuned models too, you can find them in the collection above 🤗

osanseviero afondiel victor gokaygokay John6666 leduckhai louisbrulenaudet KingNish Linksplayer jlzhou

osanseviero Taylor658 victor gokaygokay John6666 omaryshchenko


Check out AutoRound, SOTA LLM quantization algorithm across 2-4 bits without adding any inference overhead to any model
paper: https://arxiv.org/abs/2309.05516
github: https://github.com/intel/auto-round
lowbits leaderboard: https://huggingface.co/spaces/Intel/low-bit-leaderboard


nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion
  Cause in Conversations with Chain-of-Thought on Emotion States (2404.03361)

With the most recent workshop on Semantic Evaluation as a part of NAACL-2024, this year delighted to contribute with 🧪 on Chain-of-Thought fine-tuning concepts  to push forward LLMs reasoning capabilities in:

🧪 1. Reading Comprehension of Numerals in texts  🇨🇳
⭐ https://github.com/GavinZhao19/SemEval24-NumAnalysis-CN
🔒 https://huggingface.co/GavinZhao23/NumAnalysis-Chatglm3-6B

🧪 2. Extracting Emotion-Causes using Reasoning Revision (RR)
⭐ https://github.com/nicolay-r/THOR-ECAC
🔓 https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
 https://huggingface.co/papers/2404.03361

🔑 In short, there are three major takeaways:
✅ 1. The scale of the backboned LLM for SFT matters (>1.1B is preferable)
✅ 2. The language of the input data  matters in LLM reasoning capabilities: transfering data in English and picking English-based LLM is crucial for the most cases!
✅ 3. CoT and RR takes more time ⏳ for inferring and fine-tuning, proportionally to abount of steps in chain / amount of revisions in reasoning 🧠


🚀 Exciting news about https://huggingface.co/spaces/as-cle-bert/proteinviz, your fully open-source protein structure prediction tool!
🧬 I'm thrilled to announce 𝘽𝙪𝙡𝙠𝙋𝙧𝙤𝙩𝙚𝙞𝙣𝙫𝙞𝙯, a new functionality which  supports multiple structure predictions at once: you just need to upload a FASTA file with all the amino-acidic sequences, and you'll be done in minutes!
🏃 This can be really helpful in speeding up your research: give it a shot, if you are curious!🤗

(Demo in the attached video)


@ehartford hey eric, i have been following u since your first dolphin model.... i realy appreciate you doing what you do.  none the less, i have a quick question.  i was wondering what is your favorite smaller uncensored dolphin model that would run well on my m1 -8gb macbook air.. and what user interface do you suggest? thanks so much, and i hope to hear from you soon.


🔥 🔥 Releasing our new paper on AI safety alignment -- Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations 🎯 with Sayan Layek, Somnath Banerjee and Soujanya Poria.

👉 We propose Safety Arithmetic, a training-free framework enhancing LLM safety across different scenarios: Base models, Supervised fine-tuned models (SFT), and Edited models. Safety Arithmetic involves Harm Direction Removal  (HDR) to avoid harmful content and Safety Alignment to promote safe responses.

👉 Paper: https://arxiv.org/abs/2406.11801v1
👉 Code: https://github.com/declare-lab/safety-arithmetic


Forget about all the captioning datasets you've tried before!

PixelProse is a captioning dataset of 16M image-caption pairs, with less toxicity and higher details ✨
https://huggingface.co/datasets/tomg-group-umd/pixelprose

The existing suite of captioning datasets consists of web scrapes that have alt text that is either irrelevant or not descriptive. The authors of this paper have taken those datasets, filtered for CSAM, passed it with a prompt to Gemini Vision Pro. They also removed PII and detoxified the resulting dataset.

darknoon KingNish AtAndDev louisbrulenaudet


⚗ Looking to get started with Synthetic data and AI Feedback?

I created this cool notebook for a workshop @davanstrien and I gave it a couple of weeks back. It uses https://distilabel.argilla.io/dev/ and I think it is a good entry point for anyone with a practical interest in the topic.

https://colab.research.google.com/github/davanstrien/data-for-fine-tuning-llms/blob/main/03-synthetic-data-generation.ipynb

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On-Device deployment ready MobileNet-V4 models:
https://huggingface.co/collections/byoussef/mobilenetv4-conv-pretrained-tflite-models-66726c945040fc61f5a50b63

Models converted from timm's pretrained Pytorch weights to TFLite (fp32 & fp16).
All conv models fully compatible with TFLite Gpu Delegate on Android for fast on-device inference 🚀
Can also run super fast on Google-Edge-TPU on Pixel phones 🚀


🤯🤯🤯VERY ROBUST TOOL to control camera motion for videos!!!

Even doesn't need any additional finetuning! It uses inference process of video diffusion directly!!

Try it on your own video diffusion model and generate CINEMATIC SHOTS!📸🎥🫢

Check at https://lifedecoder.github.io/CamTrol/

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Principled Instructions Are All You Need for Questioning LLaMA-1/2,
  GPT-3.5/4 (2312.16171)

https://huggingface.co/papers/2312.16171 I normally use this to make prompts in the form of a RAG (Retrieval Augmented Generation). For example, here's one from Gemma 7B about articles.

"Please summarize the main ideas of the article '[Article Title]' in a concise and informative manner. Focus on highlighting the key points and arguments presented in the article. Keep the summary to around [desired length] words."

Has anyone else tried this? Do you like the results you are getting?


⚗ distilabel 1.2.0 is out and it comes with improved support for structured generation, new tasks for generating datasets for training embedding models, new steps for loading data, MixtureOfAgentsLLM and improved docs.

We would love to see a few new datasets for training embedding models built with distilabel on the Hub! ❤

monsoon-nlp clem osanseviero victor abunchofrandomwords


I've fine-tuned three types of PaliGemma image captioner models for generating prompts for Text2Image models. They generate captions similar to prompts we give to the image generation models. I used google/docci and google/imageinwords datasets for fine-tuning.

This one gives you longer captions.

https://huggingface.co/spaces/gokaygokay/SD3-Long-Captioner

This one gives you middle size captions.

https://huggingface.co/spaces/gokaygokay/SD3-Long-Captioner-V2

And this one gives you shorter captions.

https://huggingface.co/spaces/gokaygokay/SDXL-Captioner

clem osanseviero victor OjciecTadeusz emanuelevivoli Warlord-K John6666 pcuenq FiditeNemini AmmarNTNU12


With the CVPR conference (https://cvpr.thecvf.com) in full swing this week in Seattle 🏙, the competition details for NeurIPS 2024 have just been released.🚀

Some of the competitions this year include:

🦾 MyoChallenge 2024: Physiological dexterity in bionic humans.
🌌 FAIR Universe: Handling uncertainties in fundamental science.
🧪 BELKA: Chemical assessment through big encoded libraries.
🏆 HAC: Hacker-Cup AI competition.
💰 Large-Scale Auction Challenge: Decision-making in competitive games.
📶 URGENT Challenge: Signal reconstruction and enhancement.
🛡 LASC 2024: Safety in LLM and AI agents.

For more details, check out: https://blog.neurips.cc/2024/06/04/neurips-2024-competitions-announced


Impressive to see Depth Anything V2. See this example I just took with a lot of different depths.

If you want to learn more about it, this TLDR by @merve is👌 https://huggingface.co/posts/merve/568638914646708


I love Depth Anything V2 😍
It’s Depth Anything, but scaled with both larger teacher model and a gigantic dataset!

Here's a small TLDR of paper with a lot of findings, experiments and more.
I have also created a collection that has the models, the dataset, the demo and CoreML converted model 😚 https://huggingface.co/collections/merve/depth-anything-v2-release-6671902e798cd404513ffbf5

The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out what’s up with using synthetic images vs real images for MDE:

🔖  Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc) and there are many details overlooked

🔖 Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but there’s a distribution shift between real and synthetic images, and they have restricted scene coverage

The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model can’t generalize well (but large models generalize inherently anyway) 🧐
But they still fail encountering real images that have wide distribution in labels (e.g. diverse instances of objects) 🥲

Depth Anything v2 framework is to..

🦖 Train a teacher model based on DINOv2-G based on 595K synthetic images
🏷 Label 62M real images using teacher model
🦕 Train a student model using the real images labelled by teacher
Result: 10x faster and more accurate than Marigold!

The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!

osanseviero Ramikan-BR fynnkroeger SixOpen kingabzpro Taylor658 clem Hev832 shadinpira80 xu3kev radames louisbrulenaudet

Ramikan-BR Tuana kramp clem super-cinnamon

Ramikan-BR clem melmass shaising radames


Hey All!

I've been asked a lot of share more on how I train LoRAs. The truth is I don't think my advice is very helpful without also including more contextual, theoretical commentary on how I **think** about training LoRAs for SDXL and other models.

I wrote a first article here about it - let me know what you think.

https://huggingface.co/blog/alvdansen/thoughts-on-lora-training-1

Edit: Also people kept asking where to start so I made a list of possible resources:
https://huggingface.co/blog/alvdansen/thoughts-on-lora-training-pt-2-training-services

victor shaising merve not-lain netynet dillfrescott KingNish philipp-zettl kramp

digiplay victor merve not-lain dillfrescott


💰 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 ⇒ 𝘁𝗼𝗸𝗲𝗻𝗰𝗼𝘀𝘁

I've just found out about 𝙰𝚐𝚎𝚗𝚝𝙾𝚙𝚜-𝙰𝙸/𝚝𝚘𝚔𝚎𝚗𝚌𝚘𝚜𝚝 (https://github.com/AgentOps-AI/tokencost).
𝗧𝗵𝗶𝘀 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗰𝗮𝗹𝗹𝘀 𝘁𝗼 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜: OpenAI, Anthropic, Mistral, AWS or Databricks...

For any model, you can use as input either string prompts or messages, and get as outputs either the price or token count.

Congrats to the AgentOps-AI team: this will be very useful when trying to get a ballpark estimate of a project's price, to compare APIs, or for precise monitoring of usage!

✨ Daily reminder: 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗻 𝗔𝟭𝟬𝟬 𝗰𝗼𝘀𝘁𝘀 𝘆𝗼𝘂 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 $𝟬.𝟬𝟬/𝗵𝗼𝘂𝗿 (or 0.00€ in current exchange rates) on a HF space with ZeroGPU!
Learn more on ZeroGPU 👉 https://www.datacenterdynamics.com/en/news/hugging-face-launches-zerogpu-project-to-democratize-ai-gives-away-10-million-worth-of-compute/

umair894 Pavarissy kramp victor gabrielmbmb Taylor658 sigridjineth maywell radames sosoai prithivMLmods introvoyz041


https://huggingface.co/datasets/nevmenandr/incoming-students-ma-dh-hse-university
Dataset visualized by datawrapper

```html
<div style="min-height:494px"><script type="text/javascript" defer src="https://datawrapper.dwcdn.net/q3waH/embed.js?v=2" charset="utf-8"></script><noscript><img src="https://datawrapper.dwcdn.net/q3waH/full.png" alt="" /></noscript></div>
```
Data from my talk in February: https://www.youtube.com/watch?v=ZfXqvIzl5fo . Slides: https://nevmenandr.github.io/slides/2024-02-02/slides.pdf.


Remember Will Smith eating Spaghetti? 🍝😆

AI has come a long way from generating hilariously low-quality videos to almost unrealistic realistic videos 🎥✨

But most models like @OpenAI Sora, @Kling_ai , etc are not publicly available. 🚫🖥

But now we have @LumaLabsAI Dream Machine, which is publicly available for free! 🎉🆓

Here is the dilemma, Sora and Kling posted some excellent examples of what the AI was capable of, and so did Luma AI. 🌟🤖

But in actual use, they leave so much to be desired. 😕 Are we back to cherry-picking examples and leaking benchmarks in training data? 🍒📊

Try Dream Machine 👉 https://lumalabs.ai/dream-machine 🌐


Hey guys! @mk230580 @wikeeyang @Yasirkh & others asked how to run the Hugging Face spaces outside of the HF environment locally with their source editor and Google Colab. Here is how to do that simply 👇👇.

📍I have just created a step-by-step procedure with a Colab demo link also attached in the repository's README.md.

🔗: https://github.com/prithivsakthiur/how-to-run-huggingface-spaces-on-local-machine-demo

Thanks for a read !!

wikeeyang osanseviero xi0v ashail mk230580 ezzdev prithivMLmods


🚀 Sarashina1-65B

SB Intuitions has announced the release of Japanese Large Language Models (LLMs) with 7 billion, 13 billion, and 65 billion parameters to aid academic and industrial research and development. The company plans to develop a 390 billion parameter model by the end of 2024. The models, named Sarashina1 and Sarashina2, show significant performance improvements, especially Sarashina2 which is an enhanced version of Sarashina1.

Performance evaluations using five Japanese language datasets reveal that Sarashina2 outperforms other models, including continued pre-trained models. The name "Sarashina" originates from a historical diary linked to the headquarters' location in Tokyo's Takeshiba area, symbolizing the company's ambition to create globally utilized models from Japan.

Model URL:
- https://huggingface.co/sbintuitions/sarashina1-65b
- https://huggingface.co/sbintuitions/sarashina2-13b

Detailed press release (in Japanese):
https://www.sbintuitions.co.jp/news/press/20240614_01/

osanseviero julien-c victor zafina yjernite Nulooper


Repurposing Diffusion-Based Image Generators for Monocular Depth
  Estimation (2312.02145)

Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds (2312.04962)

DGInStyle: Domain-Generalizable Semantic Segmentation with Image
  Diffusion Models and Stylized Semantic Control (2312.03048)

Join us at our remaining CVPR presentations this week! Members of PRS-ETH will be around to connect with you and discuss our presented and ongoing works:

💐 Marigold: Discover our work on sharp diffusion-based computer vision techniques, presented in Orals 3A track on "3D from Single View", Thu, June 20, 9:00-9:15 AM. Also, drop by Poster Session 3 later that day for more tangible matters! 🌚
Project page: https://marigoldmonodepth.github.io/
Paper: https://huggingface.co/papers/2312.02145
Collection: https://huggingface.co/collections/prs-eth/marigold-6669e9e3d3ee30f48214b9ba
Space: https://huggingface.co/spaces/prs-eth/marigold-lcm
Diffusers 🧨 tutorial: https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage

⚙ Point2CAD: Learn about our mechanical CAD model reconstruction from point clouds, presented in Poster Session 1, Wed, June 19, 10:30 AM - 12:00 PM.
Project page: https://www.obukhov.ai/point2cad.html
Paper: https://huggingface.co/papers/2312.04962

🎭 DGInStyle: Explore our generative data synthesis approach as a cost-efficient alternative to real and synthetic data, presented in the Workshop on Synthetic Data for Computer Vision, Tue, June 18, at Summit 423-425.
Details and schedule: https://syndata4cv.github.io/
Project page: https://dginstyle.github.io/
Paper: https://huggingface.co/papers/2312.03048
Model: https://huggingface.co/yurujaja/DGInStyle


A nice improvement for Hugging Face on Sheets: You can now customize your prompt and select the model of your choice directly on the sheet.

Thanks to @louisbrulenaudet for the contribution. Really cool to see the community improving this tool!

Try it here: https://huggingface.co/spaces/JournalistsonHF/huggingface-on-sheets

anakin87 osanseviero davanstrien Ramikan-BR not-lain thomwolf cminja thangvip boapps


📁✨ Meet Corpus Creator!

This Gradio app (https://huggingface.co/spaces/davanstrien/corpus-creator) takes you from your local files to a Hugging Face  Dataset via Llama Index.

The goal of the tool is to make it quicker and easier to quickly get some local files you want to get ready for ML tasks into a Hugging Face Dataset. Perfect for  building datasets for:
-  synthetic data pipelines
- annotation
- RAG
- Other ML tasks that start from a HF dataset

I'll share something more substantial that uses this tomorrow 🤗


Finally @CVPR2024 is here! 🩷
Have you claimed your papers and linked your models/datasets/demos?
This will increase visibility and impact of your paper 💫

To index your papers, go here
https://huggingface.co/spaces/CVPR2024/CVPR2024-papers
Find your paper, click on paper page link, index the paper, then click on your name (workflow is below 👇🏻)
If you'd like to add links to your paper, go here https://huggingface.co/spaces/CVPR2024/update-CVPR2024-papers
login, find your paper's id, retrieve the paper, fill in the info and submit!


🧪 RAG Evaluation with 🔥 Prometheus 2 + Haystack

📝 Blog post: https://haystack.deepset.ai/blog/rag-evaluation-with-prometheus-2
📓 Notebook: https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prometheus2_evaluation.ipynb

─── ⋆⋅☆⋅⋆ ───

When evaluating LLMs' responses, 𝐩𝐫𝐨𝐩𝐫𝐢𝐞𝐭𝐚𝐫𝐲 𝐦𝐨𝐝𝐞𝐥𝐬 like GPT-4  are commonly used due to their strong performance.
However, relying on closed models presents challenges related to data privacy 🔒, transparency, controllability, and cost 💸.

On the other hand, 𝐨𝐩𝐞𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 typically do not correlate well with human judgments and lack flexibility.

🔥 Prometheus 2  is a new family of open-source models designed to address these gaps:
🔹 two variants: https://huggingface.co/prometheus-eval/prometheus-7b-v2.0; https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0
🔹 trained on open-source data
🔹 high correlation with human evaluations and proprietary models
🔹 highly flexible: capable of performing direct assessments and pairwise rankings, and allowing the definition of custom evaluation criteria.

See my experiments with RAG evaluation in the links above.


My weekened project ended up being doing some testing between torchtune, axolotl, and unsloth. I *think* it's a 1:1 comparison of what LoRA fine-tuning performance looks like between the different hardware I have in my dev boxes (4090, 3090, 7900 XTX, W7900) with a few other interesting tidbits.

Tonight I wrote up a WandB report (the panel editor is super broken in Firefox 😔) that sums up some of the more interesting bits from the results: https://wandb.ai/augmxnt/train-bench/reports/torchtune-vs-axolotl-vs-unsloth-Trainer-Comparison--Vmlldzo4MzU3NTAx

brainhome louisbrulenaudet Weyaxi ManniX-ITA


🌟 Progress in the German FineWeb edu reproduction 🌟

We're delighted to share the launch of our new Data Quality Classification Model, designed specifically for evaluating educational content in German. This tool uses advanced machine learning techniques to assess texts across all educational levels, from primary school to university.

🔍 Inspired by Huggingface's fine web edu dataset, we've worked hard to refine data classification methods ensuring educators and learners access top-quality resources.
We're excited about the future as we continue improving our models and expanding our datasets.

Access the model here: https://huggingface.co/pL-Community/GermanEduScorer-Qwen2-1.5b

🙏 A huge thank you to David and Daryoush from Vago Solutions; Björn and Jan from Ellamind / DiscoResearch for their expert insights throughout this project. Your support has been crucial.
This project was made possible by the support of PrimeLine AI.


Mixtral or Llama 70B on Google Spreadsheet thanks to Hugging Face's Serverless Inference API 🤗

The Add-on is now available on the HF repo "Journalists on Hugging Face" and allows rapid generation of synthetic data, automatic translation, answering questions and more from simple spreadsheet cells 🖥

Link to the 🤗 Space : https://huggingface.co/spaces/JournalistsonHF/huggingface-on-sheets

Although this tool was initially developed for journalists, it actually finds a much wider inking among daily users of the Google suite and the remaining use cases to be explored are numerous.

Only a free Hugging Face API key is required to start using this no-code extension.

Do not hesitate to submit ideas for features that we could add!

Thanks to @fdaudens for initiating this development.

victor meiaienable JuliusSandmann wsuff SengTak kargaranamir umair894 Lucianocosta aidystark kramp

victor JuliusSandmann MaziyarPanahi nvhf louisbrulenaudet


A great work based on ChemLLM from Open-source community!
Automatic Scientific Discovery guided by LLM!
https://github.com/zyzisastudyreallyhardguy/LLM4SD


📊 Lovely to share the unique reasoning capabilities 🧠 findings of Qwen2-7B 🇨🇳 in Target Sentiment Analysis (TSA) for original texts (🇷🇺) and their translated version in English (🇺🇸), in zero-shot-learning mode.
Since the last update on 1.5, I have to say:
☑ 1.  Qwen2-7B is the first model in my list that reasons 🔥 better 🔥 in Russian rather than in English; it strongly surpasses other 7B LLMs and LLaMA3-70B by correctly distributing sentiment cases (F1(PN) metric).
☑ 2. Surprisingly, but Qwen2-7B significantly underperformed to the "earlier bro" Qwen1.5-7B on texts in English. The key problem is that ~17% of answers has mixed entries of labels, so for such cases the automatic and accurate assessment is difficult. Therefore, I believe it is more about particular evaluation, rather something wrong with the model in TSA domain.

What's next? I have to checkout https://huggingface.co/Qwen/Qwen2-72B-Instruct then 🧪 If you know the best hosting for infering, please let me know 🙏

Model: https://huggingface.co/Qwen/Qwen1.5-7B-Chat


I had a backlog of LoRA model weights for SDXL that I decided to prioritize this weekend and publish. I know many are using SD3 right now, however if you have the time to try them, I hope you enjoy them.

I intend to start writing more fully on the thought process behind my approach to curating and training style and subject finetuning, beginning this next week.

Thank you for reading this post! You can find the models on my page and I'll drop a few previews here.

fffiloni alvdansen LaughterOnWater mmarrder thliang01 dicksondickson MaziyarPanahi victor Brukolakos s3nh not-lain xumijiezi622 Citaman LizzyAsf ijohn07 louisbrulenaudet BennyKok yagmurx zhaoyafei multimodalart MQSI adamelliotfields dillfrescott SengTak milkowski KingNish comarproject ZekiJ E2GO Cordobian Viktor1233 clem

Norod78 Brukolakos clefourrier Dennyanswer s3nh not-lain zhaoyafei multimodalart dillfrescott Cordobian clem


Observability and Retrieval Augmented Generation in 10 lines of Code

Tutorial: https://www.youtube.com/watch?v=VCQ0Cw-GF2U

This video covers:
- Why we need observability?
- Implementation of RAG using BeyondLLM
- Monitor and Track LLM Observability using Phoenix


🖥 Do you have 1TB+ VRAM?

🎉 Well, good news for you!

👨🔬 Good folks at @nvidia have released Nemotron 4 340B, the new open-source LLM king, rivalling GPT-4! 🚀

📊 340B parameter models in 3 flavours: base, reward, and instruct models

🎯 It's a dense model, not MoE

👓 4k context window

📚 9T tokens training data, 2 phase training (8T pre-train + 1T continued pre-training)

🌍 Trained on 50+ languages and 40+ coding languages (70% training data is English, 15% multi-lingual, 15% code)

📅 June 2023 training data cut-off

💻 To deploy needs 8x H200/ 16x H100/ 16x A100 80GB for BF16 Inference (about 8x H100 in int4)

🏆 Of course, it beats Llama 3 70B on MMLU (81.1), Arena Hard (54.2), and GSM8K (92.4)

🤖 But beaten by Qwen 2 on HumanEval and MTBench which is a 72B parameter model

🔧 Used SFT, DPO, and RPO. RLHF via Nemo Aligner framework to align the model

📊 98% of alignment data was synthetically generated

📄 Nvidia open licence with commercial use allowed

¯\_(ツ)_/¯
😅 Glad to see more open models but this is one confusing fellow!
🤨340B parameter model that is narrowly beating 70B models? Starts failing against 72B models? Sounds like a model for synthetic data generation! But then it has 4k context?

🔗 Models: https://huggingface.co/collections/nvidia/nemotron-4-340b-666b7ebaf1b3867caf2f1911

📑 Paper: https://research.nvidia.com/publication/2024-06_nemotron-4-340b


https://huggingface.co/nevmenandr/w2v-russian-tolstoy

```python
import gensim
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
sns.set_style("darkgrid")

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

modelLNT2 = Word2Vec.load("cbow_300_10.model")

# skip some code... for full version see model's card

tsnescatterplot(modelLNT2, 'жизнь_S', [i[0] for i in modelLNT2.wv.most_similar(negative=["жизнь_S"])])
```

life by Tolstoy (w2v):


Just published "CryptGPT: A Simple Approach to Privacy-Preserving Language Models Using the Vigenere Cipher".

https://huggingface.co/blog/diwank/cryptgpt-part1

tl;dr - we pretrained a gpt-2 tokenizer and model from scratch on a dataset encrypted with Vigenere cipher and it performs as well as regular gpt-2. Except in order to use it, you need to know the encryption key.

links:
https://github.com/creatorrr/cryptgpt
https://huggingface.co/diwank/cryptgpt
https://huggingface.co/diwank/cryptgpt-large


I've tried out my new Space for copying Websites with Gemini 1.5 Flash and i gave it a image of Huggingchat. The results were interesting, but you can see it for yourself.

Space:
https://huggingface.co/spaces/L-AI/Gemini-UI-Generator

Website-Demo:
https://leunos.com/hf-chat-fake

Screenshot:


Hello!

I was experimenting with multi-bot interactions for practical solutions, such as code synthesis and editing. This has, so far, led to many well-made templates and no working code, but I still feel  a template this lovely is worthy of use. Enjoy! 🤗

https://colab.research.google.com/gist/SMeyersMrOvkill/2ed6cbc305bc5bd62fcf1f7aab15f7b9/voice_memos.ipynb


Together MoA is a really interesting approach based on open source models!

"We introduce Mixture of Agents (MoA), an approach to harness the collective strengths of multiple LLMs to improve state-of-the-art quality. And we provide a reference implementation, Together MoA, which leverages several open-source LLM agents to achieve a score of 65.1% on AlpacaEval 2.0, surpassing prior leader GPT-4o (57.5%)."

Read more here: https://www.together.ai/blog/together-moa

PS: they provide some demo code: (https://github.com/togethercomputer/MoA/blob/main/bot.py) - if someone release a Space for it it could go 🚀

KingNish GPT007 SixOpen Choms SizorCloud nicolay-r osanseviero louisbrulenaudet


📊 Just measured reasoning capabilities 🧠  of Qwen1.5-7B 🇨🇳 in Target Sentiment Analysis (TSA) both for original texts (🇷🇺) and translated in English (🇺🇸), in zero-shot-learning mode.  Here is what I've noticed:
☑ 1. Huge gap 📈 with the smaller Qwen1.5 and Qwen2 (1.8B and 1.8B).  Qwen1.5-7B strongly outperforms their "smaller bros" so that case when scale of the model matters.
☑ 2.  Qwen1.5-7B in english (🇺🇸) behaves similar but slightly underperforming 📉 to the most latest 7B alternatives ... and even including Phi-3-small (3.4B)
☑ 3. On texts in (🇷🇺) there is a certain underperforming  📉  gap between the most latest 7B alternatives: F1=34.1, other 7B starts with 40.23.

In terms of responses, for non-english texts  (🇷🇺) model answers strict and behaves similar to FlanT5.
Curious about improvements in Qwen2-7B 🔥

Model: https://huggingface.co/Qwen/Qwen1.5-7B-Chat
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101


I've published several older versions of Vokan! Sometimes, they may sound more natural, but less like the target speaker.

Please check em out!
https://huggingface.co/spaces/Korakoe/Vokan-V0.5
https://huggingface.co/ShoukanLabs/Vokan


𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗯𝗶𝗴 𝘀𝗼𝗰𝗶𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗶𝘀 𝗻𝗼𝘁 🦋, 𝗶𝘁'𝘀 𝗛𝘂𝗯 𝗣𝗼𝘀𝘁𝘀! [INSERT STONKS MEME WITH LASER EYES]

See below: I got 105k impressions since regularly posting Hub Posts, coming close to my 275k on Twitter!

⚙ Computed with the great dataset https://huggingface.co/datasets/maxiw/hf-posts
⚙ Thanks to Qwen2.5-Coder-32B for showing me how to access dict attributes in a SQL request!

cc @merve who's far in front of me

BrigitteTousi clem cfahlgren1 Nymbo not-lain andrewrreed Overlaiapp FranckAbgrall celinah pierrci Fishfishfishfishfish veeraleto Niansuh davidberenstein1957 jhparmar jcudit jsulz s3nh

clem Nymbo not-lain maxiw celinah pierrci fffiloni Fishfishfishfishfish davidberenstein1957 s3nh


LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation (2411.04997)

Microsoft released LLM2CLIP: a CLIP model with longer context window for complex text inputs 🤯
All models with Apache 2.0 license here https://huggingface.co/collections/microsoft/llm2clip-672323a266173cfa40b32d4c

TLDR; they replaced CLIP's text encoder with various LLMs fine-tuned on captioning, better top-k accuracy on retrieval.
This will enable better image-text retrieval, better zero-shot image classification, better vision language models 🔥
Read the paper to learn more: https://huggingface.co/papers/2411.04997


🎙 Listen to the audio "Podcast" of every single Hugging Face Daily Papers.

Now, "AI Paper Reviewer" project can automatically generates audio podcasts on any papers published on arXiv, and this is integrated into the GitHub Action pipeline. I sounds pretty similar to hashtag#NotebookLM in my opinion.

🎙 Try out yourself at https://deep-diver.github.io/ai-paper-reviewer/

This audio podcast is powered by Google technologies: 1) Google DeepMind Gemini 1.5 Flash model to generate scripts of a podcast, then 2) Google Cloud Vertex AI's Text to Speech model to synthesize the voice turning the scripts into the natural sounding voices (with latest addition of "Journey" voice style)

"AI Paper Reviewer" is also an open source project. Anyone can use it to build and own a personal blog on any papers of your interests. Hence, checkout the project repository below if you are interested in!
: https://github.com/deep-diver/paper-reviewer

This project is going to support other models including open weights soon for both text-based content generation and voice synthesis for the podcast. The only reason I chose Gemini model is that it offers a "free-tier" which is enough to shape up this projects with non-realtime batch generations. I'm excited to see how others will use this tool to explore the world of AI research, hence feel free to share your feedback and suggestions!


@maxiw just created a dataset of the posts on the Hub and gathered some stats: https://huggingface.co/posts/maxiw/833289193510507
The :heart: is winning on the Hub!


⚠ People selling AI chatbots for websites hate us.
Add an open source chat assistant on your website in 5 minutes: https://github.com/phospho-app/ai-chat-bubble

How does it work ?
- You give an URL
- The AI assistant crawls the website content and embed it
- Add it to your frontend in one line of code
- People on your website can ask the assistant questions

Powered by https://huggingface.co/BAAI/bge-small-en-v1.5 and Mistral AI

flams oulianov John6666 victor julien-c blacktok wsuff pierrci no-mad jgitsolutions Platinn Aurelien-Morgan


ever wondered how you can make an API call to a visual-question-answering model without sending an image url 👀

you can do that by converting your local image to base64 and sending it to the API.

recently I made some changes to my library "loadimg"  that allows you to make converting images to base64 a breeze.
🔗 https://github.com/not-lain/loadimg

API request example 🛠:
```python
from loadimg import load_img
from huggingface_hub import InferenceClient

# or load a local image
my_b64_img = load_img(imgPath_url_pillow_or_numpy ,output_type="base64" )

client = InferenceClient(api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")

messages = [
	{
		"role": "user",
		"content": [
			{
				"type": "text",
				"text": "Describe this image in one sentence."
			},
			{
				"type": "image_url",
				"image_url": {
					"url": my_b64_img # base64 allows using images without uploading them to the web
				}
			}
		]
	}
]

stream = client.chat.completions.create(
    model="meta-llama/Llama-3.2-11B-Vision-Instruct",
	messages=messages,
	max_tokens=500,
	stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")
```


I'm curating an AI-powered web search software timeline at https://github.com/felladrin/awesome-ai-web-search

The list covers three main categories:

1. Web Search with LLM summarization and follow-up capabilities
2. LLM chat interfaces with Web Search integration
3. Agent-driven research tools using LLM + Web Search

The timeline helps track the evolution of this space and serves as a reference for anyone looking for alternatives. If you know of any tools that should be included, please contribute by:
- opening a PR to edit the readme: https://github.com/felladrin/awesome-ai-web-search/edit/main/readme.md
- creating an issue in the repository: https://github.com/felladrin/awesome-ai-web-search/issues/new/choose
- or sharing in the comments below.


I’ve published a new dataset to simplify model merging 🤗

This dataset facilitates the search for compatible architectures for model merging with @arcee_ai’s mergekit, streamlining the automation of high-performance merge searches 📖

Dataset : https://huggingface.co/datasets/louisbrulenaudet/mergekit-configs

prithivMLmods ethanker not-lain djuna John6666


✍ the last few weeks has been very intense!
🔴 I have been out all weekends
🔴 Participated in 4 hackathons in a row (2 more to come!)
🔴 Even threw a big hackathon myself!

Nonetheless, I am in school again 🏫, which meant... ✨homework✨

➡ Head out to here https://x.com/_fracapuano/status/1856415612202799243 to read more about how I used @mistralai models to help me with my assignments (not how you think I did hihi 😏)

➡ Check outhttps://huggingface.co/spaces/fracapuano/texstral if you want to use the tool yourself!


My Hypothesis:

Concepts like entropy, energy, and the second law of thermodynamics are not intrinsic to physical matter but are emergent properties of any sufficiently complex system where probabilistic decision-making, optimization, and information flow occur. These principles arise naturally in artificial environments that are structured with rules governing uncertainty, even without explicit definitions of physical thermodynamic laws.

Proven Via:

The Second Law of Thermodynamics
Geometric Langlands Program
Lagrangean Mechanics

TL;DR: When I create a simulated environment, I do not need to code entropy and energy into the simulated environment. I can utilize Entropy and the Second Law of Thermodynamics and I can use Conservation of Energy, but I do not need to explicitly code these into the environment. That is peculiar.

I made a video with a clickbait title but a bunch of code that breaks this observation down further. Would love for someone to prove my simple observation false: https://youtu.be/8n7SXLj7P1o


🚀 llava-calm2-siglip

CyberAgent Inc. has announced the public release of "llava-calm2-siglip," a 7.5 billion parameter Vision Language Model (VLM) for Japanese, available for commercial use. This model, trained primarily on a high-quality Japanese dataset, is accessible on Hugging Face Hub under an Apache-2.0 license. The advancement aims to improve Japanese language-specific VLMs, which are fewer compared to English-centric models.

Model URL:
https://huggingface.co/cyberagent/llava-calm2-siglip

Demo URL:
https://huggingface.co/spaces/cyberagent/llava-calm2-preview

Detailed press release (in Japanese): https://www.cyberagent.co.jp/news/detail/id=30344


Chatting with llava is tricky

https://huggingface.co/spaces/nroggendorff/llava


🔍 Remember Tensorboard graph visualizer?

🚀 @Google just released Model Explorer, a Tensorboard graph visualizer on steroids 💪.

🛠 Model Explorer is a graph visualization tool designed to improve understanding, debugging, and optimizing machine learning (ML) models, especially large ones.

🎯 It addresses challenges in traditional graph visualization tools by implementing a hierarchical layout and GPU-accelerated graph rendering, which enhances performance and usability.

🌐 The tool supports visualization of large-scale ML models by displaying hierarchical information, which simplifies understanding complex model architectures.

🔑 Key features include layer-by-layer exploration 🔍, side-by-side graph comparison for debugging conversion errors 🐛, and per-node data overlays for identifying performance issues 📈.

👨💻 Originally developed for Google's internal use, Model Explorer is now available publicly as part of the Google AI Edge family of products and even runs directly in colab!

🔗 Colab: https://github.com/google-ai-edge/model-explorer/blob/main/example_colabs/quick_start.ipynb

📰 Blog: https://research.google/blog/model-explorer/


Wow, impressive 340B model by nvidia with a nice permissive license! 🚀 The technical report is full of insights and seems to use a different learning rate schedule than cosine, probably a variant of WSD. Hope to get more info on that! 👀

 https://huggingface.co/collections/nvidia/nemotron-4-340b-666b7ebaf1b3867caf2f1911


Me: I want on device AI: fast, without latency, with real privacy, convenient for use and development.

Microsoft: The best I can do is Copilot+. You need a special Qualcomm chip and Windows 11 24H2. Today I can give you only Recall, taking screenshots and running a visual model to write context about what you are doing in the unencrypted Semantic Index database for embeddings. I'm giving you SLMs Phi Silica, accessible only via API and SDK. In the autumn I can give you the developer tools for C#/C++ and you can use them.

Apple: The best I can do is Apple Intelligence. You need a special Apple chip and macOS 15. Today I can give you only marketing. In the autumn I can give you on-device 3B quantized to 3.5bit mysterious SLMs and diffusion models with LoRA adapters. We will have an encrypted Semantic Index database for embeddings and agentic flows with function calling. We will call all of them with different names. In the autumn I will give you the developer tools in Swift and you can use them.

Open Source: The best I can do is llama.cpp. You can run it on any chip and OS. Today you can run AI inferencing on device and add other open source components for your solution. I can give you local AI models SLMs/LLMs - from wqen2-0.5B to Llama3-70B. You can have an encrypted local embeddings database with PostgreSQL/pgvector or SQLite-Vec. I can give you a wide choice of integrations and open-source components for your solution- from UIs to agentic workflows with function calling. Today I can give you the developer tools in Python/C/C++/Rust/Go/Node.js/JS/C#/Scala/Java and you can use them.

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.

Ramikan-BR nrmtvv DeathGodlike adamelliotfields SizorCloud lout33 ToKrCZ noh-au-00 TechWTy ArslanMehmood

Ramikan-BR yukiarimo nrmtvv DeathGodlike lout33 nicoism

dashfunnydashdash GPT007 Metin Zeldazackman


@CHANEFO  suite à votre post.

Je peux essayer peut-être de vous guider ?

« Si je souhaite paramétrer un assistant orienté vers un sujet spécifique concernant l'application du droit du travail dans mon entreprise, comment procéder ?
Le but de faire référence à un ensemble de document en lien avec des accords collectif qui sont dans des document type PDF ou WORD. Quel limite sur la taille des documents et ou téléchargé les fichier pour y faire référence ? »


Wow, this is amazing! 🤯
Samba is a powerful hybrid model with an unlimited context length, combining Mamba, MLP, Sliding Window Attention, and MLP stacking. Samba largest version, Samba-3.8B, trained on 3.2 trillion tokens, excels in benchmarks like MMLU, GSM8K, and HumanEval, and shines in long-context tasks with minimal tuning.
---
Official implementation of "Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling"
Github: https://github.com/microsoft/Samba

Srulikbdd anakin87 seyf1elislam ZeroWw dillfrescott satyamt Felladrin den0620 victor GPT007 mihaylovnikitos NHLOCAL AtAndDev


Tools Ready!
Thanks to ChemCrow's great work, ChemLLM supports proficiency toolkits Now, Include,
Molecule Name Retrivel
Molecule Property Query
Patent Check
Molecule Safety Query
Try it on chemllm.org


📢 Suprisingly, there are so many works on imputing personalities in LLM and vice versa. However, there is a gap in literature novels  📚  for mining that personalities from book itself. With that I am happy to release worflow that 🔥 solely 🔥 relies on book  content only 📖 for personalities extraction:
https://github.com/nicolay-r/book-persona-retriever

💡 The downstream goal of this workflow is to enhance charactes understanding ... and not just through their mentions in books, but through their personalities (⛏ retrieved with the given lexicon from the 📖 itself)

The most closest studies such as PERSONA-CHAT (arXiv:1801.07243v5),  BookEmbeddingEval (2022.findings-acl.81.pdf), ALOHA-Chatbot ( arXiv:1910.08293v4), Meet your favorite Character (arXiv:2204.10825), and PRODIGy (arXiv:2311.05195v1) were so valuable 💎 ! 👏

Curious on existance of the fine-tuned LLM for detecting personalities in text passages on huggingface hub  🤗 If you aware about the one coud be potentially embedded into system for further advances, please feel free to recomend 🙌

spacemonkAI87 ThatoH Ervist victor Wolter8711 MrF-Qwerty diwank


Today is a huge day in Argilla’s history. We couldn’t be more excited to share this with the community: we’re joining Hugging Face!

We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.

Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching https://huggingface.co/argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing https://huggingface.co/datasets/argilla/OpenHermesPreferences, one of the largest open preference tuning datasets

After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.

To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.

As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.

Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.

Would love to answer any questions you have so feel free to add them below!

osanseviero gabrielmbmb fdaudens marcsun13 Leiyre alvarobartt victor loubnabnl frascuchon Wauplin lhoestq Xenova burtenshaw lysandre Narsil andrewrreed mcpotato plaguss mlabonne lunarflu ybelkada VictorSanh louisguitton Molbap alielfilali01 ceyda Citaman jeffboudier eliebak pierrci MKdir98 philschmid kevinostoll giux78 jphme Taylor658 mgoin wath5 strickvl tomaarsen not-lain julien-c GloriaMK alexsherstinsky merve nara-simba KingNish bartowski monsoon-nlp santiviquez KonradSzafer pcuenq olivierrousseau07 prepsyched cloderic bergr7f chargoddard haritzpuerto 4eyez GPT007 yjernite deanwampler mattupson rbgo MoritzLaurer oza75 sergiopaniego abidlabs gsarti medmac01 radames lewtun munish0838 eitanturok sugatoray flozi00 acelik louisbrulenaudet philipp-zettl Lin-Rexter alexcombessie drt14 mikv39 amcasari severo pacoid nthakur bullerwins JJoe206 ngxson petricevich mohamedsaeed823 jessicagab aust-t clem beta3 jimmy agenkit bielrv davanstrien Prakash-chokalingam Ramikan-BR taufiqdp blefaudeux afondiel zaanind HappyCat123

vicgalle gabrielmbmb nataliaElv marcsun13 alvarobartt victor loubnabnl frascuchon Wauplin lhoestq Xenova burtenshaw lysandre andrewrreed mcpotato plaguss mlabonne lunarflu ybelkada mpjuhasz VictorSanh Molbap alielfilali01 ceyda jeffboudier eliebak pierrci philschmid Yasbok kevinostoll giux78 strickvl tomaarsen not-lain julien-c GloriaMK alexsherstinsky KingNish monsoon-nlp KonradSzafer pcuenq osanseviero NickyNicky 4eyez mdouglas yjernite InferenceIllusionist mattupson rbgo MoritzLaurer sergiopaniego abidlabs gsarti medmac01 radames lewtun munish0838 secularwarden bewizz FM-1976 louisbrulenaudet Lin-Rexter drt14 ZennyKenny JJoe206 stanbiryukov clem agenkit Ramikan-BR Haleyok sbrandeis

burtenshaw andrewrreed plaguss mlabonne lunarflu ybelkada VictorSanh alielfilali01 jeffboudier eliebak philschmid kevinostoll giux78 strickvl tomaarsen not-lain julien-c Hev832 GloriaMK alexsherstinsky santiviquez KonradSzafer pcuenq bergr7f 4eyez peter2000 yjernite mattupson rbgo ijohn07 gsarti medmac01 Moibe lewtun munish0838 louisbrulenaudet Lin-Rexter drt14 shaikatasif LizzyAsf amcasari anakin87 JJoe206 krisshen clem agenkit Ankush-Chander Ramikan-BR

Narsil andrewrreed plaguss mlabonne lunarflu ybelkada Aurelien-Morgan VictorSanh Molbap sgans alielfilali01 ceyda Citaman jeffboudier eliebak philschmid kevinostoll giux78 tomaarsen not-lain julien-c GloriaMK alexsherstinsky alvarobartt KingNish KonradSzafer pcuenq 4eyez mdouglas yjernite mattupson rbgo sergiopaniego gsarti lewtun munish0838 kramp Lin-Rexter drt14 JJoe206 apol clem agenkit Ramikan-BR

kevinostoll not-lain julien-c GloriaMK pcuenq mdouglas rbgo ABX-AI gsarti lewtun munish0838 drt14 apol clem Ramikan-BR

shamy777 FilipeR drt14 clem jimmy Ramikan-BR Danioken


Automatically generate docstrings for your code using LLMs. We just released a new patchflow that can generate docstrings - https://github.com/patched-codes/patchwork/tree/main/patchwork/patchflows/GenerateDocstring

Here is an example PR that does it - https://github.com/codelion/example-java-maven/pull/4

You can check out other patchflows to automate developer chores with patchwork https://github.com/patched-codes/patchwork


I've spent some time checking the promises vs reality of on-device AI between Apple Intelligence and Microsoft Copilot+. Reading the marketing documentation is good, but not enough. Hands-on tests are the best, unfortunately, both are not there yet.

Both are looking to lock developers behind local API to the SLM inferencing engine and SDK mix of open source and proprietary code. Both can not work air-gapped and offline for meaningful workflows, only some basic ones and both require the hybrid AI local/remote plane calling back either APIs on Azure or the Apple Private Cloud Compute.

Some of the Copilot+ functionally is available in Windows App SDK 1.6 exp2. It's focused on the old-school enterprise developers and not sure if they will be the early adaptors of GenAI-backed apps... I still have the Recall on my dev-PC as they have removed it.

Apple Intelligence is hard to get beyond the vague description and the State of the Union video. Even the current beta of macOS 15 and xcode don't have any "A.I." in them. At the moment it is all promises and a lack of technical documentation and code.

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.


Luma AI has just launched Dream Machine, a Sora and Kling AI-like tool that generates videos from simple text and images. 🎥
Dream Machine is out of beta and offers a free tier to test it out.

I tried this extremely simple prompt with the pic below and thought the capture of my prompt into a drone camera-like video was decent:

```
You are a drone operator. Create a 30-second video from a drone heading eastbound over the western suburbs of Bismarck, North Dakota, looking east towards the city on an overcast summer evening during the golden hour from an altitude of 200 ft.
```

Dream Machine also has a paid tier. However, like its paid tier text-to-image brethren from 2023 (who all fared EXTREMELY badly once good text-to-image capabilities became the norm in open and closed source LLMs), time will tell if the pay tier model will work for text and image to video. ⏳

This will be evident in 3 to 5 months once GPT-5, Gemini-2, Mistral-9, Llama 4, et al., all models with enhanced multimodal capabilities, are released. 🚀

louisbrulenaudet victor lunarflu damerajee thethinkmachine privategeek24 sugatoray angelfail Artples


Hello everyone,

I am pleased to announce that I have founded the University of Glasgow organization on Huggingface. If you are affiliated with the University of Glasgow or have a relative who is, you can log in through the relevant link.

https://huggingface.co/UniversityofGlasgow

yunusserhat trade1co nbroad osanseviero julien-c SixOpen not-lain lunarflu thomwolf gsarti SaylorTwift


There are 2.2 billion active @Apple devices 🍏 and all of them just got smarter thanks to Apple Intelligence (AI) 🧠

Well, almost all devices... 🤔

Your device needs:
- A17 Pro chip or later if it's an iPhone 📱,
- M1 chip or later if iPad 📱,
- M1 chip or later if Mac 💻.

All this aside, this is probably the largest deployment of on-device LLMs 🌍.

Here is the technical goodness:
- AI will run ~3B LLM on device (Mac, iPhone, iPad) with grouped-query-attention, activation, and embedding quantization (Talaria bit rate selection) running on the neural engine 🚀.
- Will be using fine-tuned LoRA Adapters for different tasks, claiming to outperform other 7B and 3B LLMs! 🥇
- iPhone 15 Pro 0.6 ms time-to-first-token with 30 tokens/second latency ⏱.
- No server model size or details 🤐.
- Will be dynamically loading, caching, and swapping LoRA adapters (think LoRA Land) 🔄.
- On-device model has 49K vocab size, while the server model goes 100K 📚.
- Using rejection sampling fine-tuning and RLHF in post-processing 📈.
- A rejection sampling fine-tuning algorithm with teacher committee 🎓.
- And reinforcement learning from human feedback (RLHF) algorithm with mirror descent policy optimization and a leave-one-out advantage estimator 🧮.
- Used synthetic data generation (from bigger models, does not mention which) for tasks like summaries 📝.
- 750 evaluation samples for each production use case to evaluate summarization (dataset not released) 📊.
- No mention of multilingual support 🌐.
- Used Apple's AXLearn framework (JAX) and FSP to train on TPUs and GPUs 💪.
- 3B + Adapter outperforms Phi-3 mini, Gemma 7B, Mistral 7B on summarization 🏆.
- 3B + Adapter achieves 78.7% on IFEval beating Phi-3 mini, Gemma 7B, Mistral 7B; Server Model matches GPT-4-Turbo and beats Mixtral 8x22B and GPT-3.5-turbo ✨.

LoRA for the win! 🎉

Blog: https://machinelearning.apple.com/research/introducing-apple-foundation-models

louisbrulenaudet zzffss SamChaTICE thethinkmachine


MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)

MobileNetV4 weights are now in timm!  So far these are the only weights for these models as the offiicial Tensorflow impl remains weightless.

Guided by paper hparams with a few tweaks, I've managed to match or beat the paper results training the medium models. I'm still working on large and improving the small result. They appear to be solid models for on-device use.

https://huggingface.co/collections/timm/mobilenetv4-pretrained-weights-6669c22cda4db4244def9637

https://huggingface.co/papers/2404.10518

osanseviero merve adamelliotfields lathashree01 kylewascher


Stable Diffusion 3: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Demo(Zero A100): https://huggingface.co/spaces/kadirnar/Stable-Diffusion-3

Paper: https://arxiv.org/pdf/2403.03206
Model Page:  https://huggingface.co/stabilityai/stable-diffusion-3-medium

Thanks ❤  @dn6   @sayakpaul

victor Norod78 osanseviero MrDigger DiamanteAmarelo Hev832 merve Sohaibsoussi medmac01 sugatoray whitebill


New Appearance from Ollama Open WebUI!
And Also web search, Realtime talking and File RAG!
https://chemllm.org/


🤗 I trained what is probably the smallest (600k ~) TinyStories model! It really can write grammatically correct stories!

https://huggingface.co/raincandy-u/TinyStories-656K

Try this space based on this minuscule model!

https://huggingface.co/spaces/raincandy-u/Story-Teller

Edit: Moreover, the model weight size is only 1.31MB under bf16, and can be reduced to the 700KB level when using Q8_0 quantization U•ェ•*U

Edit: Now 1000K params chat model!

https://huggingface.co/raincandy-u/TinyChat-1776K


Advanced RAG - Hybrid Search using HuggingFace Models

Chat with PDF in 10 lines of code:

```
# pip install beyondllm
# pip install llama-index-embeddings-fastembed

from beyondllm import source,retrieve,embeddings,llms,generator
import os
from getpass import getpass
os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:")

data = source.fit("sample.pdf", dtype="pdf")
embed_model = embeddings.FastEmbedEmbeddings()

retriever = auto_retriever(
    data=data, embed_model=embed_model,
    type="hybrid", top_k=5, mode="OR"
)

llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2")
pipeline = generator.Generate(question="<replace-with-your-query>",llm=llm,retriever=retriever)
print(pipeline.call())
```

Cookbook: https://github.com/aiplanethub/beyondllm/blob/main/cookbook/Implementing_Hybrid_Search.ipynb

Support the project by giving a ⭐ to the repo

victor daltheman lucifertrj Ramikan-BR lunarflu ToKrCZ catastropiyush


📢Delighted to share personal findings  🔎 East Asian LLM Qwen1.5 🇨🇳 reasoning capabilities 🧠 Target Sentiment Analysis (TSA).  Starting with the one of the smallest Qwen1.5-1.8B-Chat version, for the original Eastern-European texts (🇷🇺) and their translated versions (🇺🇸) in zero-shot-learning mode setup, the key takeaways of such experiments were as follows:

✅ 1. Model is capable to perform reasoning in Eastern Eropean languages (🇷🇺) (remember it is 1.8B), switching to Qwen2 results in strong improvement, with results that surpasses LLaMA2-70B-chat (more on difference below).
✅ 2. Despite the size of 1.8B, reasoning in English has a significant gap in underpeforming (F1=~34%) to the most closest Flan-T5-XL (2.8B) which showcases F1=43%.

💡 The most intriguing fact that Qwen1.5-1.8B-Chat:
it generates new words in Russian I've never seen before: "неретеневая" (negativitively), and imputes the entries in Chinese. The reason of such a low results, is that model was not been able to follow the input instruction and shares all the opinions per each class.  All of that has been improved though in Qwen2-1.5B.

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101

victor kristaller486 louisbrulenaudet ZeroWw