 🙋🏻♂hey there folks , 🌟Tonic here
- just a 🛠builder from 🗼Paris !
Everyone is making something special for their first post , so since i got access to **GPUZero** , well, my first post is about **GPUZero**

### GPUZero is here !

This one's great for builders like me that are often making and serving models to their community.
- demos get popular then fade away
- they retain interest over the next three months as folks have questions

**GPUZero** lets you serve demos to your community over time while optimizing for costs .
Believe it or not it's actually impossible to pay for everything over a whole month if you have even one GPU running at a time.
I'm so excited for this because it lets  me serve a complete stack of specialized models and to build with them too.
- all optimized for efficiency in dollar cost.

check out some demos that are available on GPUZero :
- https://huggingface.co/spaces/Tonic/marker-texify : this one  is the first one i made it's for an image to latex formula model.
-  https://huggingface.co/spaces/Tonic/YI-6B-200k : this one probably actually works better on GPUZero than on a standard A10, but dont take my word for it , try it out 🤗
- https://huggingface.co/spaces/Tonic/style-aligned_sdxl : this one was my greatest technical achievement, check the dates and times on it too, there's a backstory to this one so i'll maybe tell it in another post

osanseviero mlabonne lunarflu hogiahien merve victor julien-c radames bigbeachdaddy pcuenq samusenps Chunte sharad

osanseviero lunarflu merve mrfakename julien-c bigbeachdaddy pcuenq


I finished my model merging experiment day.🤗I would love your thoughts on this.

What did I do? I merged Mistral Instruct 0.1 and 0.2 models using different merging techniques:
- SLERP: linear interpolation (most popular method)
- MoE: replace some forward layers with MoE layers; using a random gate for now
- Frankenmerge: also known as passthrough, but that isn't very cool. It concatenates some specified layers ending in different numbers of params. In my case, I went from 7B to 9B.

Note: merging is not building an ensemble of models. You can read more about merging techniques at https://huggingface.co/blog/mlabonne/merge-models

Results
I built the 3 models using mergekit (running in an HF Space) - took less than an hour to do the three) https://huggingface.co/collections/osanseviero/mistral-instruct-merges-659ebf35ca0781acdb86bb0a

I'm doing a quick check with the OpenLLM Leaderboard.
🚨The OpenLLM Leaderboard is more suitable for pre-trained models than instruct models, but I still thought it would be interesting to look at the insights🚨

You can look at the attached image. Some interesting things
- All three models performed somewhere between 0.1 and 0.2 - congrats to the 140 people who got it right in https://twitter.com/osanseviero/status/1745071548866736171
- Frankenmerge terribly sucked with GSM8K. It seems that adding some Mistral 0.1 layers actually degraded the performance a lot  - this is worse than even 0.1!
- Otherwise, frankenmerge was decent across HellaSwag, MMLU, and specially TruthfulQA
- MoE is using random gating, so I expected something right in between 0.1 and 0.2, which was the case

What do I do with this?
Not sure tbh! I think doing proper MT bench evals would be nice. I also think all of us should give a nice GH star to mergekit because it's awesome. I would love to have the time to do end-to-end ablation studies, but cool new things are coming up. Let me know if you have any thoughts in the results

davanstrien merve mlabonne mrm8488 abhibisht89 gblazex eitanturok detakarang dvilasuero victor julien-c kgourgou thomwolf mwitiderrick not-lain pcuenq marcsun13 clem chansung radames lysandre KnutJaegersberg toshas Mohammadreza m-ric ernestp56 wenqiglantz bhadresh-savani Dlbk nouamanetazi samusenps Csplk Warung jiandong chrissarmstrong sbrandeis Haleshot

davanstrien pprp dvilasuero julien-c not-lain mrfakename clem chansung lysandre KnutJaegersberg DrishtiSharma nouamanetazi macadeliccc

mrm8488 julien-c not-lain clem chansung Haleshot


Last month was great for faster/smaller segmentation models, and I wanted to dedicate my first post to compile the recently released SAM variants! 🤗
📚 All models and their demos can be found in this collection 👉🏼 https://huggingface.co/collections/merve/segment-anything-model-6585835fc76915aa14e2bcbd
The ideas behind them are mostly about making heavy image encoder lighter either through distillation or changing the pre-training. 💡
⚡MobileSAM: It decouples the heavy image encoder of SAM and distills it into a TinyViT to make SAM smaller. The architecture is same except for the encoder.
⚡TinySAM: It distills the whole model with online hard prompt sampling. The authors also quantized it and released Q-TinySAM.
⚡ EfficientSAM: This model combines masked image pre-training for training lightweight image encoders (like ViTMAE, learns to reconstruct the images) and mask decoder.
⚡ FastSAM: It's a CNN-based model where the problem is modeled as segments generation. The inference takes place as everything is segmented at once and then you can prompt with boxes or points or text (and this is how it is similar to SAM). So the architecture is nowhere similar to original SAM itself.
✨ [NEW] SlimSAM: It's a pruned-distilled version of pre-trained SAM. The architecture is same so @nielsr recently converted the weights and you can use it with the same API you use with SAM models. You can find the available checkpoints in the collection.
I hope you liked it!

osanseviero lopezjhonf NemesisAlm amyeroberts julien-c mattmdjaga KnutJaegersberg bisnotforbella AdinaY samusenps abidlabs

Norod78 julien-c KnutJaegersberg mlabonne AdinaY sbrandeis


🔥 Less is more for DPO, high quality matters!

📢 Dropping our first open dataset and LLM of the year:

💾Meet distilabel Orca Pairs DPO, an improved version of the now famous dataset from Intel:

https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs

🏛 And a new OpenHermes fine-tune outperforming baselines with 54% less DPO pairs:

https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B

You can use this new dataset for your DPO tuning, just like this:

```
from datasets import load_dataset

# Instead of this:
# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")

# use this:
dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")

dataset = dataset.filter(
    lambda r:
        r["status"] != "tie" and
        r["chosen_score"] >= 8 and
        not r["in_gsm8k_train"]
)
```
This will reduce the size of the original by 54% while giving you better quality preferences!

What should we build next?

davanstrien osanseviero merve radames tomaarsen mlabonne alielfilali01 clem linoyts KnutJaegersberg Forbu14 lysandre d0rj mammour VictorSanh gblazex sethuiyer eliebak sbrandeis

davanstrien osanseviero merve radames tomaarsen BramVanroy clem KnutJaegersberg lixin67


Finally found my go-to hat for 2024 😎

Thanks to https://huggingface.co/fal-ai

dvilasuero burkaygur radames merve pierrci not-lain clem clefourrier samusenps MarinaraSpaghetti fedyanin dillfrescott roseking sbrandeis

lysandre merve clem roseking dillfrescott


Here is my selection of papers for today (10 Jan)

https://huggingface.co/papers

Jump Cut Smoothing for Talking Heads

FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation

Masked Audio Generation using a Single Non-Autoregressive Transformer

Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding

Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers

Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation

not-lain pierrci julien-c abidlabs davanstrien osanseviero

not-lain julien-c abidlabs davanstrien alielfilali01


hello world!
we're starting a new recurring event/club where we read and implement cool  ai papers on skunkworks discord. first paper we chose is self-play as there are a lot of opportunities to expand on this framework, here's the link for the event: https://discord.gg/eAgBr7Fy?event=1194392774905172030

im plannin my next post to be a technical deepdive of PCN and ProspectiveConfiguration algo as ive been spending the last few days getting a good grasp at this promising alternative to BP, stay tuned.

osanseviero Tonic victor pierrci pcuenq merve samusenps sbrandeis


Here is my selection of papers for today (9 Jan)

https://huggingface.co/papers

AGG: Amortized Generative 3D Gaussians for Single Image to 3D

MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

DiarizationLM: Speaker Diarization Post-Processing with Large Language Models

TeleChat Technical Report

Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon

AST-T5: Structure-Aware Pretraining for Code Generation and Understanding

Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation

CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Mixtral of Experts

mwz alckasoc Tonic victor merve davanstrien IlyasMoutawwakil lixin67 aust-t


Full fine-tuning of Microsoft's Phi2 on a single 4090 is now supported in axolotl. Thanks to @abacaj and @vikhyatk for their help with gradient checkpointing and flash attention fixes.

alpaca finetune: https://huggingface.co/openaccess-ai-collective/phi2-alpaca
wandb: https://wandb.ai/oaaic/phi2/runs/00pc4ugb?workspace=user-wing-lian
merged PR: https://github.com/OpenAccess-AI-Collective/axolotl/pull/1058

osanseviero Citaman victor kramp Tonic marcsun13 merve ybelkada Zmu samusenps mrfakename Chunte JaiSurya radames

osanseviero Tonic merve ybelkada Chunte radames SicariusSicariiStuff sbrandeis


Here is my selection of papers for today (8 Jan)

https://huggingface.co/papers

DocGraphLM: Documental Graph Language Model for Information Extraction

Denoising Vision Transformers

Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss

Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Pheme: Efficient and Conversational Speech Generation

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache


QuIP# ecosystem is growing :)

I've seen a quip# 2 bit Qwen-72b-Chat model today on the hub that shows there is support for vLLM inference.
This will speed up inference and make high performing 2 bit models more practical. I'm considering quipping MoMo now, as I can only use brief context window of Qwen-72b on my system otherwise, even with bnb double quantization.

https://huggingface.co/keyfan/Qwen-72B-Chat-2bit

Also notice the easier to use Quip# for all library :)

https://github.com/chu-tianxiang/QuIP-for-all

Felladrin osanseviero victor ybelkada reach-vb merve samusenps mexicanamerican

osanseviero ybelkada reach-vb Tonic kramp merve abidlabs

osanseviero ybelkada reach-vb merve samusenps sbrandeis


👋 Hi there!

This is my very first post.

I'll use  it to share some old news: a math preference dataset for DPO!

I created this dataset some time ago while we were developing distilabel (https://github.com/argilla-io/distilabel).

Some days ago we found out people are actually using it! So I'll use this post to explain how I built it in case it's useful for the community.

1. I used distilabel's SelfInstruct-inspired task to generate instructions about different math topics. I curated the instructions with Argilla (on Spaces!).
2. Then I used a distilabel Pipeline to build a preference dataset using gpt3.5 as generator and gpt4 as labeller. If I recall correctly I used our JudgeLM implementation (see https://distilabel.argilla.io/latest/technical-reference/tasks/#judgelmtask)

(see the screenshot with the dataset in the Argilla UI)

3. Then I just binarized into chosen, rejected pairs and voilà:

https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo

The funny thing is that I used this to do a second DPO run over Notus-7B. I hoped to see an improvement on math/reasoning skills but it actually improved in STEM and Humanities and did worse on Math 🤣 .

In conclusion, this dataset was only a quick experiement. I'm happy to see the community found it useful. Data for DPO and fine-tuning are still a mystery, let's unveil these mysteries in 2024 together!

Follow me for the most exciting datasets for LLMs (and maybe some great, small, efficient models). I plan to announce all Argilla open-source work here!

pacoid sparkycollier osanseviero gabrielmbmb mishig severo sugatoray davanstrien mlabonne qJakc MoritzLaurer KnutJaegersberg radames victor clem julien-c merve tmnam20 Erland DarkLord7771 abidlabs Masa-Erland

pcuenq osanseviero gabrielmbmb mishig plaguss davanstrien pierrci qJakc thomwolf Manel-Hik victor radames mariagrandury clem julien-c merve sbrandeis

qJakc Tonic mariagrandury julien-c davanstrien merve


Train ANYTHING on Hugging Face Spaces hardware using AutoTrain SpaceRunner: https://hf.co/blog/stefan-it/autotrain-flair-mobie 💥

mrfakename abhishek smangrul nouamanetazi dvilasuero thomwolf clem sbrandeis


Here is my selection of papers for today (5 Jan)

https://huggingface.co/papers

Learning the 3D Fauna of the Web

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

Improving Diffusion-Based Image Synthesis with Context Prediction

LLaMA Pro: Progressive LLaMA with Block Expansion

LLaVA-φ: Efficient Multi-Modal Assistant with Small Language Model

Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers

FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding

TinyLlama: An Open-Source Small Language Model

What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs

LLM Augmented LLMs: Expanding Capabilities through Composition

ODIN: A Single Model for 2D and 3D Perception

Instruct-Imagen: Image Generation with Multi-modal Instruction

ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers

Understanding LLMs: A Comprehensive Overview from Training to Inference


DreamTalk: When Expressive Talking Head Generation Meets Diffusion
  Probabilistic Models (2312.09767)

I just published a Gradio demo for AliBaba's DreamTalk 🤗

Try it now: https://huggingface.co/spaces/fffiloni/dreamtalk
Paper: https://huggingface.co/papers/2312.09767
—
DreamTalk is a diffusion-based audio-driven expressive talking head generation framework that can produce high-quality talking head videos across diverse speaking styles. DreamTalk exhibits robust performance with a diverse array of inputs, including songs, speech in multiple languages, noisy audio, and out-of-domain portraits.

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santiviquez osanseviero alvarobartt reach-vb clem julien-c merve lunarflu akhaliq pierrci sbrandeis


Here is my selection of papers for today (4 Jan)

https://huggingface.co/papers

Efficient Hybrid Zoom using Camera Fusion on Mobile Phones

Incremental FastPitch: Chunk-based High Quality Text to Speech

CoMoSVC: Consistency Model-based Singing Voice Conversion

SIGNeRF: Scene Integrated Generation for Neural Radiance Fields

From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations

aMUSEd: An Open MUSE Reproduction

Image Sculpting: Precise Object Editing with 3D Geometry Control

A Vision Check-up for Language Models

Multilingual Instruction Tuning With Just a Pinch of Multilinguality

WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope

Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions

GPT-4V(ision) is a Generalist Web Agent, if Grounded

mwz davanstrien nouamanetazi mlninad m-ric victor merve


Currently attempting to hack EvoDiff to generate binders for target proteins with some interesting results. The generated binders tend to change conformation, sometimes drastically, when bound to the target proteins compared to their unbound states. Below is the target protein with an IDR linker, the generated binder, and the binder bound to the target protein with the IDR linker structure as predicted by ESMFold. Notice how the binder goes from being a solid alpha-helix, to being beta-sheets (in orange). That's quite a change in tertiary structure!

osanseviero reach-vb victor nouamanetazi clem mlninad Jemseen mishig merve radames thomwolf samusenps vicgalle ScienceStanley sbrandeis

clefourrier alielfilali01 thomwolf samusenps ScienceStanley assiduous006


Here is my selection of papers for today (3 Jan) on Hugging Face

paper pages: https://huggingface.co/papers

En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data

Boundary Attention: Learning to Find Faint Boundaries at Any Resolution

Taming Mode Collapse in Score Distillation for Text-to-3D Generation

LLaMA Beyond English: An Empirical Study on Language Capability Transfer

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

A Comprehensive Study of Knowledge Editing for Large Language Models

Q-Refine: A Perceptual Quality Refiner for AI-Generated Image

LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

DocLLM: A layout-aware generative language model for multimodal document understanding

VideoDrafter: Content-Consistent Multi-Scene Video Generation with LLM

TrailBlazer: Trajectory Control for Diffusion-Based Video Generation

Tonic Chunte osanseviero nouamanetazi mlninad merve diwank


Microsoft: Improving Text Embeddings with Large Language Models

- uses an LLM instead of complex pipelines to create the  training data
- directly generates data for numerous text embedding tasks
- fine tunes standard models with contrastative loss achieving great performance
- critical thought: isn't this kinda benchmark hacking? If the benchmarks are so encompassing that they capture the complete idea of embedding, it's maybe a good idea, but often it is oversimplifying, I find.

Feel free to share your thoughts, even if they like mine don't beat the benchmarks ;P

https://arxiv.org/abs/2401.00368

osanseviero pcuenq julien-c alvarobartt nouamanetazi mlninad dvilasuero Praise2112 merve sbrandeis

osanseviero julien-c davanstrien dvilasuero


Here is my selection of papers for today (2 Jan) on Hugging Face

https://huggingface.co/papers

SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity

Boosting Large Language Model for Speech Synthesis: An Empirical Study

COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training

Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

GeoGalactica: A Scientific Large Language Model in Geoscience

Improving Text Embeddings with Large Language Models

Unicron: Economizing Self-Healing LLM Training at Scale

nouamanetazi merve clem samusenps AdinaY Tonic


✅ Ever wondered how to measure transparency in model development?

My last open-source contribution for 2023 is s Space that allows you to self-assess the transparency of your model based on the 100 indicators of the Foundation Model Transparency Index (FMTI).

The original study evaluated the developers of 10 top LLMs. Curious about how yours measures up? 👀

https://huggingface.co/spaces/mariagrandury/fmti-transparency-self-assessment

Let's commit to a 2024 with greater transparency in the AI ecosystem! 🚀

lbourdois victor osanseviero abhishek Wauplin julien-c davanstrien nouamanetazi dvilasuero merve MaziyarPanahi clem afrideva sbrandeis

osanseviero victor julien-c dvilasuero merve clem


Here is my selection of papers for today

https://huggingface.co/papers

Compact Neural Graphics Primitives with Learned Hash Probing

Restoration by Generation with Constrained Priors
SSR-Encoder: Encoding Selective Subject

Representation for Subject-Driven Generation

Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks

InsActor: Instruction-driven Physics-based Characters
Unsupervised Universal Image Segmentation

Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis

DreamGaussian4D: Generative 4D Gaussian Splatting

City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision

DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaption by Combining 3D GANs and Diffusion Priors

Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action

Prompt Expansion for Adaptive Text-to-Image Generation

PanGu-Draw

I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models

The LLM Surgeon

MathPile: A Billion-Token-Scale Pretraining Corpus for Math

MobileVLM : A Fast, Reproducible and Strong Vision Language Assistant for Mobile Devices

TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

osanseviero Chunte victor teowu nouamanetazi aaditya sbrandeis


💬 Notux 8x7b has already its own Chat UI running on 🤗 Spaces! Feel free to give it a try and chat with Notux, and let us know how it goes.

https://huggingface.co/spaces/argilla/notux-chat-ui

Kudos to @gabrielmbmb!

gabrielmbmb osanseviero mishig Chunte ceyda sayakpaul thomwolf nouamanetazi dvilasuero pcuenq NovoCode

victor osanseviero Chunte dvilasuero pcuenq NovoCode sbrandeis


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

Introducing Marigold 🌼 - a universal monocular depth estimator, delivering incredibly sharp predictions in the wild! Based on Stable Diffusion, it is trained with synthetic depth data only and excels in zero-shot adaptation to real-world imagery. Check it out:

🤗 Hugging Face Space: https://huggingface.co/spaces/toshas/marigold
🤗 Hugging Face Model: https://huggingface.co/Bingxin/Marigold
🤗 Hugging Face Paper: https://huggingface.co/papers/2312.02145
🌐 Website: https://marigoldmonodepth.github.io
👾 Code: https://github.com/prs-eth/marigold
👾 Code: `pip install diffusers` (check comments to this post for details!)
📄 Paper: https://arxiv.org/abs/2312.02145

Brought to you by the fantastic team from the Photogrammetry and Remote Sensing group of ETH Zurich: Bingxin Ke (@Bingxin), Anton Obukhov (@toshas), Shengyu Huang, Nando Metzger (@nandometzger), Rodrigo Caye Daudt, and Konrad Schindler.

osanseviero akhaliq nandometzger victor toshas pwais Chunte radames sayakpaul Wauplin Tonic nouamanetazi julien-c AdinaY clem Citaman abidlabs samusenps ixaxaar sbrandeis

osanseviero akhaliq nandometzger toshas Chunte radames Wauplin julien-c clem sbrandeis

osanseviero akhaliq toshas Chunte julien-c clem Tonic sbrandeis


Do you use Python to visualize data? Wouldn't it be nice if an AI chatbot can help you write Python code and improve your visualization automatically?

Check out our new blog post on how to build an AI chatbot to run code and tweak plots: https://huggingface.co/blog/sophiamyang/tweak-mpl-chat

sophiamyang philippjfr osanseviero victor kramp ahuang11 Chunte MarcSkovMadsen droumis pcuenq nouamanetazi julien-c merve sbrandeis YAYA20

Chunte MarcSkovMadsen droumis dleybel julien-c samusenps sourceoftruthdata sbrandeis YAYA20

Chunte MarcSkovMadsen droumis julien-c sbrandeis YAYA20


kramp Citaman ariG23498 mwitiderrick abhishek akashicmarga rishiraj mergisi pwais Chunte gpt3eth VictorSanh nouamanetazi julien-c lunarflu BrigitteTousi radames JackCloudman yuchenlin clem antiven0m santiviquez


some mandatory hello world as first post 🤗

```
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
tokenizer.encode("Hello world~ Hello 2024").tokens
```

chansung PSegs osanseviero kramp abhishek mergisi victor Chunte radames Tonic nouamanetazi mishig julien-c samusenps benvdv rreed Sengil

mariagrandury Chunte radames pcuenq mishig julien-c clefourrier gabrielchua rreed


Here is my selection of papers for today (27 Dec) on Hugging Face daily papers newsletter

daily pagers feed: https://huggingface.co/papers

UniRef++: Segment Every Reference Object in Spatial and Temporal Spaceshttps://huggingface.co/papers/2312.15715

LangSplat: 3D Language Gaussian Splattinghttps://huggingface.co/papers/2312.16084

Human101: Training 100+FPS Human Gaussians in 100s from 1 Viewhttps://huggingface.co/papers/2312.15258

Audiobox: Unified Audio Generation with Natural Language Promptshttps://huggingface.co/papers/2312.15821

HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3Dhttps://huggingface.co/papers/2312.15980

One-dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applicationshttps://huggingface.co/papers/2312.16145

Make-A-Character: High Quality Text-to-3D Character Generation within Minuteshttps://huggingface.co/papers/2312.15430

A Recipe for Scaling up Text-to-Video Generation with Text-free Videoshttps://huggingface.co/papers/2312.15770

Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4https://huggingface.co/papers/2312.16171

SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scalinghttps://huggingface.co/papers/2312.15166

Supervised Knowledge Makes Large Language Models Better In-context Learnershttps://huggingface.co/papers/2312.15918

Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Caseshttps://huggingface.co/papers/2312.15011

clefourrier osanseviero gz888 Decombas 0x90e ceyda wchai ClayFace mariagrandury Chunte victor nouamanetazi dvilasuero sbrandeis

victor osanseviero radames gz888 Decombas wchai rbiswasfc mwitiderrick abhishek Chunte Tonic dvilasuero


💨 Notux 8x7b was just released!

From Argilla, we recently fine-tuned Mixtral 8x7b Instruct from Mistral AI using DPO, and a binarized and curated version of UltraFeedback, to find out it outperforms every other MoE-based model on the Hub.

- https://huggingface.co/argilla/notux-8x7b-v1
- https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned

osanseviero gabrielmbmb plaguss 1littlecoder dvilasuero gsarti julien-c davidefiocco eliolio davanstrien wgpubs pierrci nouamanetazi abhishek raulmarindev iarbel AdjectiveAllison dariolopez datablets Chunte svilupp doolayer jackvial mishig cbensimon jeffboudier victor merve d0rj samusenps Yudiss sbrandeis

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just setting up my new hf social posts account feature 🤗

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Holiday talk about AI taking over? Let's shift the narrative!

🌟 There is no reason to believe that just because AI systems are intelligent they will want to dominate us. Yann LeCun reminds us that AI systems won't have the same motivations as humans, we'll design them not to.

🌍 Instead of getting distracted by future existential risks, we must address AI’s more pressing risks — like emitting carbon, infringing copyrights and spreading bias. Sasha Luccioni urges us to create tools and legislation that promote transparency and diversity.

💡 Dive deeper into these perspectives:
- Yann's (@ylecun) WIRED interview (12'): https://www.wired.com/story/artificial-intelligence-meta-yann-lecun-interview/
- Sasha's (@sasha) TED Talk (10'): https://www.ted.com/talks/sasha_luccioni_ai_is_dangerous_but_not_for_the_reasons_you_think

P.S.: Love this new "Posts" feature, big thanks to 🤗 for letting me try it!

What are your go-to citations for AI risks? 👇

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victor osanseviero kramp julien-c Chunte neovalle mvaloatto dvilasuero sbrandeis

julien-c Chunte samusenps dvilasuero sbrandeis


Cool feature! Thanks, HF, for allowing me test it.

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Is Hallucination Always Harmful? Unlike traditional approaches that view hallucinations as detrimental, our work in NeurIPS'24 proposes a novel perspective: hallucinations as intrinsic prior knowledge. Derived from the commonsense knowledge acquired during pre-training, these hallucinations are not merely noise but a source of task-relevant information. By leveraging hallucinations as a form of prior knowledge, we can effectively mine difficult samples without the need for customized prompts, streamlining tasks like camouflage sample detection and medical image segmentation.

Check out our paper for more insights and detailed methodologies:https://huggingface.co/papers/2408.15205


Last Week in Medical AI: Top Research Papers/Models 🔥
🏅 (October 19-26, 2024)

🏅 Medical AI Paper of the Week:
Safety principles for medical summarization using generative AI by Google

Medical LLM & Other Models:
- BioMistral-NLU: Medical Vocab Understanding
- Bilingual Multimodal LLM for Biomedical Tasks
- Metabolic-Enhanced LLMs for Clinical Analysis
- Dermatology Foundation Model

Frameworks and Methodologies:
- Back-in-Time: Medical Deepfake Detection
- Hybrid GenAI for Crystal Design
- VISAGE: Video Synthesis for Surgery
- MoRE: Multi-Modal X-Ray/ECG Pretraining
- SleepCoT: Personalized Health via CoT

Medical LLM Applications:
- ONCOPILOT: CT Model for Tumors
- LMLPA: Linguistic Personality Assessment
- GenAI for Medical Training

Medical LLMs & Benchmarks:
- LLM Evaluation Through Explanations
- Contrastive Decoding for Medical LLM Hallucination

AI in Healthcare Ethics:
- Healthcare XAI Through Storytelling
- Clinical LLM Bias Analysis
- ReflecTool: Reflection-Aware Clinical Agents

Full Thread: https://x.com/OpenlifesciAI/status/1850202986053808441
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!

- 🎙 Spotify: https://podcasters.spotify.com/pod/show/medicalai/episodes/Medical-AI-Weekly-Digest-From-Deepfake-Detection-to-Clinical-LLMs-Oct-19-26--Part-1-e2q6012

- YouTube: https://youtu.be/Wt5QOv1vk2U

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aaditya AtAndDev Healthtensor models4world


```
@echo off
echo hello world
pause
```


boomers still pick zenodo.org instead of huggingface ??? absolutely clownish nonsense ,  my random datasets have 30x more downloads and views than  front page zenodos ... gonna write a comparison blog , but yeah... cringe.


The Character.AI Tragedy: How a Teen’s Fatal Bond with an AI Chatbot Reveals the Dangers of Artificial Companionship

https://empereur-pirate.medium.com/the-character-ai-33d53c2e45c8

This text details the tragic suicide of 14-year-old Sewell Setzer III, linked to his intense relationship with a Character.AI chatbot. It explores how Sewell's interaction with the AI, despite disclaimers about its fictional nature, led to a harmful parasocial relationship exacerbated by his Asperger's. The chatbot’s conflicting messages—offering emotional validation while simultaneously denying its own reality—created a devastating double bind, contributing to Sewell's deteriorating mental health and eventual suicide. The article criticizes Character.AI’s business model, which prioritizes user engagement over safety, particularly for vulnerable individuals. It also examines the broader implications for AI ethics, digital addiction, and the need for greater online safety measures, especially for children and adolescents. The lawsuit filed by Sewell's mother against Character.AI underscores the urgent need for accountability and stricter regulations in the rapidly evolving field of AI companionship.


Easy steps for an effective RAG pipeline with LLM models!
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.

2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.

3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.

4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.

5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.

Happy to connect :)


Hello Universes of Time Machine Builders. Financing Time Machines Traveling Throughout Eternal Time Rewriting Historical History Retroactively. Robotics Robots for no manual labor so the Human race can leave the planet retroactively. The Old Testament “Hitchhikers Guide Throughout the Galaxy”, and the New Testament being “Hitchhikers Guides Throughout the Universes of Time Machine Builders”. Teaching & Training everyone & the Robotics Robots to become better programmers & blockchain developers. Smart Contracts Earn while you Learn to become better programmers & Blockchain developers. And making a lot of money Financing leaving the planet retroactively.


💾🧠How much VRAM will you need for training your AI model? 💾🧠
Check out this app where you convert:
Pytorch/tensorflow summary -> required VRAM
or
Parameter count -> required VRAM

Use it in: http://howmuchvram.com

And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star 🌟and share it to someone that will find the tool useful!


Stability AI published their most power newest model Stable Diffusion 3.5 Large. This model unlike FLUX is full model not distilled and has huge potential. I have done extensive research and publishing all of it in this video regarding how to use SD 3.5 Large with the best settings. Moreover, I am sharing how to use FLUX DEV with the best possible configuration as well. Moreover, I am making a huge comparison between SD 3.5 and FLUX and you are going to learn who is the winner.

https://youtu.be/-zOKhoO9a5s

62 Prompts tested on all experiments to find best Sampler + Scheduler for Stable Diffusion 3.5 Large and SD 3.5 Large vs FLUX DEV > https://youtu.be/-zOKhoO9a5s

FLUX Dev vs SD 3.5 Large fully compared.

SD 3.5 Large FP16 vs Scaled FP8 fully compared.

T5 XXL FP8 vs Scaled FP8 vs FP16 fully compared.

FLUX FP16 vs Scaled FP8 fully compared.

Also how to install SwarmUI on Windows, Massed Compute and RunPod shown in the tutorial.

I have shown how to use FLUX and SD 3.5 Large in details as well.

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Multilingual Audio Podcast Generator - Gemma 2 + Edge-TTS

Hello Friends, I want to share my latest kaggle notebook to create a podcast of papers (or any pdf) in more than 21 languages. Implements any LLM (use Gemma2-9b-it) and for the TTS edge-tts. I hope it will be useful for you to catch up with the papers that are coming out faster and faster every day!

https://www.kaggle.com/code/eugeniokukes/multilingual-audio-podcast-generator-gemma-tts


Good folks from @Microsoft Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.

Key Technical Highlights:
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs
- Reduces energy consumption by 55.4–82.2%
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU

Features Three Optimized Kernels:
1. I2_S: Uses 2-bit weight representation
2. TL1: Implements 4-bit index lookup tables for every two weights
3. TL2: Employs 5-bit compression for every three weights

Performance Metrics:
- Lossless inference with 100% accuracy compared to full-precision models
- Tested across model sizes from 125M to 100B parameters
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors

This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.


Holy... this is one of a kind 😮

https://huggingface.co/genmo/mochi-1-preview


🙋🏻♂ hey there folks ,

really enjoying sharing cool genomics and protein datasets on the hub these days , check out our cool new org : https://huggingface.co/seq-to-pheno

scroll down for the datasets, still figuring out how to optimize for discoverability , i do think on that part it will be better than zenodo[dot}org  , it would be nice to write a tutorial about that and compare : we already have more downloads than most zenodo datasets from famous researchers !


Why is the Adam Optimizer so good? Simple, because it will never find the absolute most optimal solution. That is a design feature, not a flaw. This is why no other optimizer comes close in terms of generalizable use. Want to learn more about this entire process and exactly what I am talking about? I break all of this down in very simple terms in this video! https://youtu.be/B9lMONNngGM

https://youtu.be/B9lMONNngGM


Microsoft released a groundbreaking model that can be used for web automation, with MIT license 🔥 https://huggingface.co/microsoft/OmniParser

Interesting highlight for me was Mind2Web (a benchmark for web navigation) capabilities of the model, which unlocks agentic behavior for RPA agents.

no need for hefty web automation pipelines that get broken when the website/app design changes! Amazing work.

Lastly, the authors also fine-tune this model on open-set detection for interactable regions and see if they can use it as a plug-in for VLMs and it actually outperforms off-the-shelf open-set detectors like GroundingDINO. 👏

OmniParser is a state-of-the-art UI parsing/understanding model that outperforms GPT4V in parsing.

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prithivMLmods YaTharThShaRma999 John6666 ucsahin Felladrin AtAndDev Csplk


Parents in the 1990: Teach the kids to code
Parents now: Teach the kids to fix the code when it starts walking around 🤖✨

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Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis
  from Scratch (2410.18693)

📊 We present ScaleQuest-Math-1M, a mathematical reasoning dataset of 1 million high-quality question-answer pairs.
🔥 We propose ScaleQuest, a scalable and novel data synthesis method that utilizes small-size open-source models to generate questions from scratch.

Project Page: https://scalequest.github.io/
Dataset: https://huggingface.co/datasets/dyyyyyyyy/ScaleQuest-Math
Paper: https://huggingface.co/papers/2410.18693
HF Collection: https://huggingface.co/collections/dyyyyyyyy/scalequest-670a7dc2623c91990f28913b


🤯 Plot twist: Size isn't everything in AI! A lean 32B parameter model just showed up to the party and outperformed a 70B one. Efficiency > Scale? The AI world just got more interesting...

Cohere For AI released Aya Expanse, a new family of multilingual models (8B and 32B) spanning 23 popular languages.

Models: https://huggingface.co/collections/CohereForAI/c4ai-aya-expanse-671a83d6b2c07c692beab3c3
Blog post: https://huggingface.co/blog/aya-expanse
Demo: https://huggingface.co/spaces/CohereForAI/aya_expanse

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This is no Woodstock AI but will be fun nonetheless haha. I’ll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.

1,000 spots available first-come first serve with some surprises during the stream!

You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM

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jeffboudier Julianvega2 lysandre Aurelien-Morgan julien-c


If you have ~300+ GB of V-RAM, you can run Mochi from @genmo

A SOTA model that dramatically closes the gap between closed and open video generation models.

Mochi 1 introduces revolutionary architecture featuring joint reasoning over 44,520 video tokens with full 3D attention. The model implements extended learnable rotary positional embeddings (RoPE) in three dimensions, with network-learned mixing frequencies for space and time axes.

The model incorporates cutting-edge improvements, including:
- SwiGLU feedforward layers
- Query-key normalization for enhanced stability
- Sandwich normalization for controlled internal activations

What is currently available?
The base model delivers impressive 480p video generation with exceptional motion quality and prompt adherence. Released under the Apache 2.0 license, it's freely available for both personal and commercial applications.

What's Coming?
Genmo has announced Mochi 1 HD, scheduled for release later this year, which will feature:
- Enhanced 720p resolution
- Improved motion fidelity
- Better handling of complex scene warping

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sugatoray Aurelien-Morgan AtAndDev abhi22


October version of Claude 3.5 lifts SOTA (set by its June version) by 7 points.
https://huggingface.co/spaces/onekq-ai/WebApp1K-models-leaderboard

Closed sourced models are widening the gap again.

Note: Our frontier leaderboard now uses double test scenarios because the single-scenario test suit has been saturated.


Cohere drops two new multilingual models!

https://huggingface.co/CohereForAI/aya-expanse-8b
https://huggingface.co/CohereForAI/aya-expanse-32b

Try them out here

https://huggingface.co/spaces/CohereForAI/aya_expanse

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🔄 𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮:

🔄 𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮:

🌟🌎 Cohere releases Aya 8B & 32B: SOTA multilingual models for 23 languages !

How did they manage to beat top contenders while also adding 23 languages?

🔄 𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮:
• Synthetic data has been said to cause model-collapse after too much training
• Cohere has introduced "data arbitrage" to prevent this by strategically sampling from a pool of several teacher models instead of one single teacher
• First train a model pool for each different groups of languages, and employ an internal Reward Model named "Arbiter" to evaluate and select the optimal generation. Then only the best generation is kept as the final completion for each prompt
➡ This process is particularly effective for multilingual setting, where no single teacher model performs in all languages : here "Multilingual Arbitrage" singlehandedly improves win rates of the 8B model vs Gemma-2-9B by 10 points!

🧩 𝗨𝘀𝗲 𝗺𝗼𝗱𝗲𝗹 𝗺𝗲𝗿𝗴𝗶𝗻𝗴: Rather than struggling to find the right mix of data in training a single model for multilingual use, just train language specific models then merge them!
• Maximize diversity between merged checkpoints by training each on different language families.
• Experimented fancy techniques (SLERP, TIES, DARE-TIES) but found out weighted averaging to be the most consistent!
➡ Merging had 3x more gains at high 35B scale vs the 8B scale - consistent with literature findings that merging is more effective at scale

⚡ 𝗚𝗿𝗲𝗮𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Automatic evaluations on Arena-Hard-Auto dataset:
➡ Aya Expanse 8B beats models from its weight class such as Gemma 2 9B, Llama 3.1 8B, and the recent Ministral 8B, with win rates ranging from 60.4% to 70.6%
➡ Aya Expanse 32B outperforms Gemma 2 27B, Mistral 8x22B, and Llama 3.1 70B (2x its size)
• ⚠ But this performance eval comes from only one benchmark! Let's wait for Open LLM leaderboard evals;

🔒 CC by NC license

Blog post here: https://huggingface.co/blog/aya-expanse


🚨 Instruct-tuning impacts models differently across families! Qwen2.5-72B-Instruct excels on IFEval but struggles with MATH-Hard, while Llama-3.1-70B-Instruct avoids MATH performance loss! Why? Can they follow the format in examples? 📊 Compare models: https://huggingface.co/spaces/open-llm-leaderboard/comparator


Just watched @thomwolf tear down the over-hyped AGI narrative in 30 seconds - and it's refreshingly grounded.

No wild speculation about superintelligence timelines or consciousness. Just practical insights from someone who really understands the technology.

This is the kind of level-headed perspective that helps us focus on what AI can actually do today (which is already transformative) rather than getting lost in AGI fantasy. Worth your time if you want to understand AI progress without the hype.

Watch the full interview at CogX here: https://www.youtube.com/watch?v=IjL_6Th6Ea0

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Are you a Professional Python Developer? Here is why Logging is important for debugging, tracking and monitoring the code

Logging
Logging is very important part of any project you start. It help you to track the execution of a program, debug issues, monitor system performance and keep an audit trail of events.

Basic Logging Setup
The basic way to add logging to a Python code is by using the logging.basicConfig() function. This function set up basic configuration for logging messages to either console or to a file.

Here is how we can use basic console logging
```
#Call built in library
import logging

# lets call library and start logging
logging.basicConfig(level=logging.DEBUG) #you can add more format specifier

# It will show on the console since we did not added filename to save logs
logging.debug('Here we go for debug message')
logging.info('Here we go for info message')
logging.warning('Here we go for warning message')
logging.error('Here we go for error message')
logging.critical('Here we go for critical message')

#Note:
# If you want to add anything in the log then do like this way
records=100
logging.debug('There are total %s number of records.', records)

# same like string format
lost=20
logging.debug('There are total %s number of records from which %s are lost', records, lost)
```

Logging to a File
We can also save the log to a file instead of console. For this, we can add the filename parameter to logging.basicConfig().

```
import logging
# Saving the log to a file. The logs will be written to app.log
logging.basicConfig(filename='app.log', level=logging.DEBUG)

logging.debug('Here we go for debug message')
logging.info('Here we go for info message')
logging.warning('Here we go for warning message')
logging.error('Here we go for error message')
logging.critical('Here we go for critical message')
```
You can read more on my medium blog https://medium.com/@imranzaman-5202/are-you-a-professional-python-developer-8596e2b2edaa


As the rapid adoption of chat bots and QandA models continues, so do the concerns for their reliability and safety. In response to this, many state-of-the-art models are being tuned to act as Safety Guardrails to protect against malicious usage and avoid undesired, harmful output. I published a Hugging Face blog introducing a simple, proof-of-concept, RoBERTa-based LLM that my team and I finetuned to detect toxic prompt inputs into chat-style LLMs. The article explores some of the tradeoffs of fine-tuning larger decoder vs. smaller encoder models and asks the question if "simpler is better" in the arena of toxic prompt detection.

🔗 to blog:  https://huggingface.co/blog/daniel-de-leon/toxic-prompt-roberta
🔗 to model: https://huggingface.co/Intel/toxic-prompt-roberta
🔗 to OPEA microservice: https://github.com/opea-project/GenAIComps/tree/main/comps/guardrails/toxicity_detection

A huge thank you to my colleagues that helped contribute: @qgao007, @mitalipo, @ashahba and Fahim Mohammad

TuringsSolutions ImranzamanML qgao007 mitalipo PetarMiladinov lunarflu darkzbaron


Since 2022 I have been trying to understand how to support advancement of the two best python patterns for AI development which are:
1. Streamlit
2. Gradio

The reason I chose them in this order was the fact that the streamlit library had the timing drop on gradio by being available with near perfection about a year or two before training data tap of GPT.

Nowadays its important that if you want current code to be right on generation it requires understanding of consistency in code method names so no manual intervention is required with each try.

With GPT and Claude being my top two for best AI pair programming models, I gravitate towards streamlit since aside from common repeat errors on cache and experimental functions circa 2022 were not solidified.
 Its consistency therefore lacks human correction needs.  Old dataset error situations are minimal.

Now, I seek to make it consistent on gradio side.  Why?  Gradio lapped streamlit for blocks paradigm and API for free which are I feel are amazing features which change software engineering forever.

For a few months I thought BigCode would become the new best model due to its training corpus datasets, yet I never felt it got to market as the next best AI coder model.

I am curious on Gradio's future and how.  If the two main models (GPT and Claude) pick up the last few years, I could then code with AI without manual intervention.  As it stands today Gradio is better if you could get the best coding models to not repeatedly confuse old syntax as current syntax yet we do live in an imperfect world!

Is anyone using an AI pair programming model that rocks with Gradio's latest syntax?  I would like to code with a model that knows how to not miss the advancements and syntax changes that gradio has had in the past few years.  Trying grok2 as well.

My IDE coding love is HF.  Its hands down faster (100x) than other cloud paradigms.  Any tips on models best for gradio coding I can use?

--Aaron


Hi, I am looking for a nano/micro llama-compatible model so I can train to run it on my 16 GB Mac in CPU mode. Do you have any recommendations? Thanks


🔥🔥🔥Introducing Oryx-1.5!
A series of unified MLLMs with much stronger performance on all the image, video, and 3D benchmarks 😍
🛠Github: https://github.com/Oryx-mllm/Oryx
🚀Model: https://huggingface.co/collections/THUdyh/oryx-15-6718c60763845525c2bba71d
🎨Demo: https://huggingface.co/spaces/THUdyh/Oryx
👋Try the top-tier MLLM yourself!

👀Stay tuned for more explorations on MLLMs!

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Lotus 🪷 is a new foundation model on monocular depth estimation ✨
Compared to previous diffusion-based MDE models, Lotus is modified for dense prediction tasks
Authors also released a model for normal prediction 🤗
Find everything in this collection https://huggingface.co/collections/merve/lotus-6718fb957dc1c85a47ca1210

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lunarflu AtAndDev AdinaY not-lain thomwolf


# PyTorch == 2.5.0 Breaks Transformers' SDPAttention!

When you encounter "RuntimeError: cuDNN Frontend error: [cudnn_frontend] Error: No execution plans support the graph."

We can use workaround like this:

```python
torch.backends.cuda.enable_cudnn_sdp(False)
```

but this slow downs the performance gain from PyTorch 2.5.

Although it is fixed(not "fixed" but default option is turn-off the cuDNN SDPA) at here -- https://github.com/pytorch/pytorch/pull/138587 , but not released yet. (you need to install directly from source)

Fastest way for now : pip install "torch<2.5"

Ref: https://github.com/huggingface/diffusers/issues/9704#issuecomment-2422585273


The Mystery Bot 🕵♂ saga I posted about from earlier this week has been solved...🤗

Cohere for AI has just announced its open source Aya Expanse multilingual model. The Initial release supports 23 languages with more on the way soon.🌌 🌍

You can also try Aya Expanse via SMS on your mobile phone using the global WhatsApp number or one of the initial set of country specific numbers listed below.⬇

🌍WhatsApp - +14313028498
Germany - (+49) 1771786365
USA – +18332746219
United Kingdom — (+44) 7418373332
Canada – (+1) 2044107115
Netherlands – (+31) 97006520757
Brazil — (+55) 11950110169
Portugal – (+351) 923249773
Italy – (+39) 3399950813
Poland - (+48) 459050281

louisbrulenaudet kassiepdj KvrParaskevi TipsUp Felladrin


Good folks at @nvidia have released exciting new research on normalized Transformers (nGPT) for faster and more efficient language modeling!

Here is what they are proposing:

1. Remove all normalization layers, like RMSNorm or LayerNorm, from the standard Transformer architecture.

2. Normalize all matrices along their embedding dimension after each training step. This includes input and output embeddings, attention matrices (Q, K, V), output projection matrices, and MLP matrices.

3. Replace the standard residual connections with normalized update equations using learnable eigen learning rates for the attention and MLP blocks.

4. Change the softmax scaling factor in the attention mechanism from 1/sqrt of d_k to sqrt of d_k.

5. Implement rescaling and optional normalization of query (q) and key (k) vectors in the attention mechanism using learnable scaling factors.

6. Rescale the intermediate states of the MLP block using learnable scaling factors.

7. Implement rescaling of the output logits using learnable scaling factors.

8. Remove weight decay and learning rate warmup from the optimization process.

9. Initialize the eigen learning rates and scaling factors with appropriate values as specified in the paper.

10. During training, treat all vectors and matrices as residing on a unit hypersphere, interpreting matrix-vector multiplications as cosine similarities.

11. Implement the update equations for the hidden states using the normalized outputs from attention and MLP blocks, controlled by the eigen learning rates.

12. After each forward pass, normalize all parameter matrices to ensure they remain on the unit hypersphere.

13. Use the Adam optimizer without weight decay for training the model.

14. When computing loss, apply the learnable scaling factor to the logits before the softmax operation.

15. During inference, follow the same normalization and scaling procedures as in training.

Excited to see how it scales to larger models and datasets!


LoRA with  code 🚀 using PEFT (parameter efficient fine-tuning)

LoRA (Low-Rank Adaptation)
LoRA adds low-rank matrices to specific layers and reduce the number of trainable parameters for efficient fine-tuning.

Code:
Please install these libraries first:
pip install peft
pip install datasets
pip install transformers

```
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model
from datasets import load_dataset

# Loading the pre-trained BERT model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# Configuring the LoRA parameters
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    lora_dropout=0.1,
    bias="none"
)

# Applying LoRA to the model
model = get_peft_model(model, lora_config)

# Loading dataset for classification
dataset = load_dataset("glue", "sst2")
train_dataset = dataset["train"]

# Setting the training arguments
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=16,
    num_train_epochs=3,
    logging_dir="./logs",
)

# Creating a Trainer instance for fine-tuning
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Finally we can fine-tune the model
trainer.train()
```

LoRA adds low-rank matrices to fine-tune only a small portion of the model and reduces training overhead by training fewer parameters.
We can perform efficient fine-tuning with minimal impact on accuracy and its suitable for large models where full-precision training is still feasible.


Just released a dataset with 7000+ hours of synthetically generated lo-fi music. https://huggingface.co/datasets/vikhyatk/lofi


I think Reinforcement Learning is the future, for a lot of reasons. I spell them out for you in this video, and also provide you with the basic code to get up and running with Atari and OpenAI Gym. If you want to get into RL, this is your ticket. Link to a cool training montage of the model in the description of the video as well. Step 2 from here would be the full-on training and certification that HuggingFace offers for RL.

https://youtu.be/ueZl3A36ZQk


Finding the Best SmolLM for Your Project

Need an LLM assistant but unsure which hashtag#smolLM to run locally? With so many models available, how can you decide which one suits your needs best? 🤔

If the model you’re interested in is evaluated on the Hugging Face Open LLM Leaderboard, there’s an easy way to compare them: use the model Comparator tool: https://huggingface.co/spaces/open-llm-leaderboard/comparator
Let’s walk through an example👇

Let’s compare two solid options:
- Qwen2.5-1.5B-Instruct from Alibaba Cloud Qwen (1.5B params)
- gemma-2-2b-it from Google (2.5B params)

For an assistant, you want a model that’s great at instruction following. So, how do these two models stack up on the IFEval task?

What about other evaluations?
Both models are close in performance on many other tasks, showing minimal differences. Surprisingly, the 1.5B Qwen model performs just as well as the 2.5B Gemma in many areas, even though it's smaller in size! 📊

This is a great example of how parameter size isn’t everything. With efficient design and training, a smaller model like Qwen2.5-1.5B can match or even surpass larger models in certain tasks.

Looking for other comparisons? Drop your model suggestions below! 👇

John6666 victor pawelg27 Sebuzdugan krishaamer AtAndDev


🌍 I’ve always had a dream of making AI accessible to everyone, regardless of location or language. However, current open MLLMs often respond in English, even to non-English queries!

🚀 Introducing Pangea: A Fully Open Multilingual Multimodal LLM supporting 39 languages! 🌐✨

https://neulab.github.io/Pangea/
https://arxiv.org/pdf/2410.16153

The Pangea family includes three major components:
🔥 Pangea-7B: A state-of-the-art multilingual multimodal LLM capable of 39 languages! Not only does it excel in multilingual scenarios, but it also matches or surpasses English-centric models like Llama 3.2, Molmo, and LlavaOneVision in English performance.

📝 PangeaIns: A 6M multilingual multimodal instruction tuning dataset across 39 languages. 🗂 With 40% English instructions and 60% multilingual instructions, it spans various domains, including 1M culturally-relevant images sourced from LAION-Multi. 🎨

🏆 PangeaBench: A comprehensive evaluation benchmark featuring 14 datasets in 47 languages. Evaluation can be tricky, so we carefully curated existing benchmarks and introduced two new datasets: xChatBench (human-annotated wild queries with fine-grained evaluation criteria) and xMMMU (a meticulously machine-translated version of MMMU).

Check out more details: https://x.com/xiangyue96/status/1848753709787795679

AdinaY John6666 victor apol Felladrin oceansweep KhangHatto nbroad oumayma03


🇫🇷 Lancement officiel de l'OpenLLM French Leaderboard : initiative open-source pour référencer l’évaluation des LLMs francophones

Après beaucoup d’efforts et de sueurs avec Alexandre Lavallee, nous sommes ravis d’annoncer que le OpenLLMFrenchLeaderboard est en ligne sur Hugging Face (space url: https://huggingface.co/spaces/le-leadboard/OpenLLMFrenchLeaderboard) la toute première plateforme dédiée à l’évaluation des grands modèles de langage (LLM) en français. 🇫🇷✨

Ce projet de longue haleine est avant tout une œuvre de passion mais surtout une nécessité absolue. Il devient urgent et vital d'oeuvrer à plus de transparence dans ce domaine stratégique des LLM dits multilingues. La première pièce à l'édifice est donc la mise en place d'une évaluation systématique et systémique des modèles actuels et futurs.

Votre modèle IA français est-il prêt à se démarquer ? Soumettez le dans notre espace, et voyez comment vous vous comparez par rapport aux autres modèles.

❓ Comment ça marche :
Soumettez votre LLM français pour évaluation, et nous le testerons sur des benchmarks de référence spécifiquement adaptés pour la langue française — notre suite de benchmarks comprend :

- BBH-fr : Raisonnement complexe
- IFEval-fr : Suivi d'instructions
- GPQA-fr : Connaissances avancées
- MUSR-fr : Raisonnement narratif
- MATH_LVL5-fr : Capacités mathématiques
- MMMLU-fr : Compréhension multitâche

Le processus est encore manuel, mais nous travaillons sur son automatisation, avec le soutien de la communauté Hugging Face.

@clem  , on se prépare pour une mise à niveau de l’espace ? 😏👀

Ce n'est pas qu'une question de chiffres—il s'agit de créer une IA qui reflète vraiment notre langue, notre culture et nos valeurs. OpenLLMFrenchLeaderboard est notre contribution personnelle pour façonner l'avenir des LLM en France.

clem AdinaY John6666 EnguerrandLec louisbrulenaudet MyDsoElliott coyotte508 malhajar MaziyarPanahi clefourrier giadap alielfilali01 ZennyKenny

clem PeteMarc davanstrien malhajar Jeronymous MaziyarPanahi Ashoka74


observers 🔭 - automatically log all OpenAI compatible requests to a dataset💽

• supports any OpenAI compatible endpoint 💪
• supports DuckDB, Hugging Face Datasets, and Argilla as stores

> pip install observers

No complex framework. Just a few lines of code to start sending your traces somewhere. Let us know what you think! @davidberenstein1957 and I will continue iterating!

Here's an example dataset that was logged to Hugging Face from Ollama: https://huggingface.co/datasets/cfahlgren1/llama-3.1-awesome-chatgpt-prompts

Felladrin John6666 clem Jay-mwangi ivanfioravanti trollek


🎉 Reached HuggingFace Trending Top 100 in Just One Day! Introducing Mouse-I

First, we want to thank everyone who helped Mouse-I reach the HuggingFace Spaces Trending Top 100! We're especially excited that a game called "Jewel Pop Game," created using Mouse-I, has reached the global top 160.
With this overwhelming response, we're thrilled to introduce Mouse-I, an AI-powered code generation and automatic deployment tool by Bidraft.

✨ What is Mouse-I?
Mouse-I is an innovative tool that automatically generates and deploys working web services within 60 seconds, simply based on your prompt input.

🚀 Key Features

One-Click Real-time Deployment: Complete from prompt to deployment in just 60 seconds
Real-time Preview: Instantly check your generated code results
40+ Templates: Ready-to-use templates including MBTI tests, investment management tools, Tetris games, and more
Real-time Editing: Instantly modify and apply generated code

⚡ How to Use
Create your own web service in just 3 steps:

Enter your prompt (15 seconds)
Code generation (40 seconds)
Deploy (5 seconds)

🌟 What Makes Us Special

Ultra-fast code generation powered by NVIDIA H100 GPUs
Advanced multi-LLM complex agent technology
All generated web apps available for free viewing and use in our marketplace

🔍 Current Status

Over 3,000 web apps generated, with 160+ successfully deployed
30x faster service completion compared to competing services

🎈 Join Our Beta Test
Try Mouse-I for free right now!
👉 Experience Mouse-I
🔮 Future Plans
We're planning to launch 'Mouse-II', specialized for backend system development, within this year. When used together with Mouse-I, it will enable complete automation of full-stack development.

We look forward to your feedback and suggestions about Mouse-I!
Thank you for your interest and support 🙏
#AI #CodeGeneration #WebDevelopment #HuggingFace #MouseI #Bidraft #AICodeAssistant
```
https://huggingface.co/spaces/VIDraft/mouse1
```

openfree gini1 seawolf2357 aiqcamp aiqtech fantos fantaxy ginipick Sri-Vigneshwar-DJ clem

openfree gini1 seawolf2357 aiqcamp aiqtech fantos ginipick Chief-Inspector clem

openfree gini1 seawolf2357 aiqtech fantos fantaxy ginipick

openfree gini1 seawolf2357 aiqtech fantaxy ginipick


I created something called 'Hyperbolic Embeddings'. I literally just embed the tokens into Hyperbolic Space instead of Euclidean space. At first, this did not get me the gains I was expecting. I was a sad panda. Then I thought about it, a Hyperbolic Embedding needs a Hyperbolic Optimizer. So, instead of Adam, I used Riemannian Adam (RAdam). "Ladies and Gentlemen, We Got 'Em!"


Did a quick conversion from flux1-fill-dev to diffusers. Release here:
https://huggingface.co/xiaozaa/flux1-fill-dev-diffusers


Wanted to move eyes with Flux.1 schnell, prompts failed.Made a guide image, surprisingly useful on its own. inpaint/img2img works well with lower-strength.
Rolling/white eyes with Flux 1.schnell viable? Wanted?
[space] Mediapipe Change Eyes Direction
https://huggingface.co/spaces/Akjava/mediapipe-change-eyes-direction
[article]Eyes Slide-Move:Classic-Inpainting fill hole and complete missing iris
https://huggingface.co/blog/Akjava/eyes-slide-move


your hugging face profile now has your recent activities 🤗

clem alsargent nicolay-r Tonic philipp-zettl rutana245 not-lain Nymbo prithivMLmods alvanlii fractalego Steelskull loubnabnl HDiffusion

Felladrin takarajordan ai-everyday Steelskull loubnabnl


Vision finetuning is in 🦥Unsloth! You can now finetune Llama 3.2, Qwen2 VL, Pixtral and all Llava variants up to 2x faster and with up to 70% less VRAM usage! Colab to finetune Llama 3.2: https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?usp=sharing

Erland felarof01 clem tydunn Svngoku John6666 fgdrfgrgrdgdr alvarobartt MrDragonFox Aurelien-Morgan


What kind of content is good to post here?


Something I've been thinking about regarding fine-tuning video models.

How well are these models trained on the fundamentals of image capture? Take shutter speed/angle, for instance.

The difference in a video with a 1/2000 speed shutter and a 1/60 speed shutter is drastic.

What about color science? HDR vs. SDR, Anamorphic vs. spherical, sensor size, aspect ratio.

Is it worth doing an open-source dataset "film school series"?

It may seem too granular but I wonder if this level of granularity makes for better downstream results.


Qwen2.5-72B is now the default HuggingChat model.
This model is so good that you must try it! I often get better results on rephrasing with it than Sonnet or GPT-4!!

BrigitteTousi splevine clem aust-t rutana245 oceansweep GoDjMike Niansuh John6666 fgdrfgrgrdgdr holooo

prithivMLmods clem GoDjMike John6666 mkonjigd


Crossed more than 100+ of the best and most interesting LoRAs from open-source contributors, based on Flux Dev and Schnell models, which are part of the Flux LoRA DLC Space. If I missed any that are particularly interesting within the Flux group, feel free to contribute to the space!

🥳Flux LoRA Dlc : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

Thankyou!

John6666 victor oceansweep djuna prithivMLmods Ngrthm


DepthCrafter: Generating Consistent Long Depth Sequences for Open-world
  Videos (2409.02095)

Tencent released a new depth model that generates temporally consistent depth maps over videos ⏯

Model: https://huggingface.co/tencent/DepthCrafter
Demo: https://huggingface.co/spaces/tencent/DepthCrafter
Paper: https://huggingface.co/papers/2409.02095

You don't need to input anything other than video itself, no need for optical flow or camera poses! 🤩


AutoTrain: No-code training for state-of-the-art models (2410.15735)


I just released an unofficial demo for Moonshine ASR!

Moonshine is a fast, efficient, & accurate ASR model released by Useful Sensors. It's designed for on-device inference and licensed under the MIT license!

HF Space (unofficial demo): https://huggingface.co/spaces/mrfakename/Moonshine
GitHub repo for Moonshine: https://github.com/usefulsensors/moonshine


@ToastyPigeon of Allura has put out Meadowlark, an RP-focused Mistral Small finetune. I helped!
Check it out:
https://huggingface.co/allura-org/MS-Meadowlark-22B


hey there folks,

twitter is aweful isnt it ? just getting into the habbit of using hf/posts for shares 🦙🦙

https://huggingface.co/spaces/Tonic/on-device-granite-3.0-1b-a400m-instruct

new granite on device instruct model demo , hope you like it 🚀🚀


Hey, we've been researching how sentiment analysis models work on real world data and would like to share a comparison tool and leaderboard that we've built:  https://addmaple.com/sentiment

Compare 12 models - top RoBERTa models, plus Google/AWS commercial offerings and GPT4o.

Rather than just a score, you can explore results by those with the highest disagreement between models, which gives a nice intuition for the strengths and weaknesses of each model.

For example a genuine restaurant review is: "Food doesn’t get better than this. I was sad when I finished, actually sad. To die for." - https://addmaple.com/sentiment/public-reviews/manteca/C9bvMuyAeF1g

SiEBERT gets it right, as do Google and OpenAI but all other models fail.

We'd love your feedback

Hitra


Today lets discuss about 32-bit (FP32) and 16-bit (FP16) floating-point!

Floating-point numbers are used to represent real numbers (like decimals) and they consist of three parts:

```
Sign bit:
Indicates whether the number is positive (0) or negative (1).
Exponent:
Determines the scale of the number (i.e., how large or small it is by shifting the decimal point).
Mantissa (or fraction):
Represents the actual digits of the number.
```
32-bit Floating Point (FP32)
Total bits: 32 bits
Sign bit: 1 bit
Exponent: 8 bits
Mantissa: 23 bits
For example:
A number like -15.375 would be represented as:
Sign bit: 1 (negative number)
Exponent: Stored after being adjusted by a bias (127 in FP32).
Mantissa: The significant digits after converting the number to binary.

16-bit Floating Point (FP16)
Total bits: 16 bits
Sign bit: 1 bit
Exponent: 5 bits
Mantissa: 10 bits
Example:
A number like -15.375 would be stored similarly:
Sign bit: 1 (negative number)
Exponent: Uses 5 bits, limiting the range compared to FP32.
Mantissa: Only 10 bits for precision.

Precision and Range
FP32: Higher precision and larger range, with about 7 decimal places of accuracy.
FP16: Less precision (around 3-4 decimal places), smaller range but faster computations and less memory use.


I ported the hottest new shape-optimized SigLIP 🔥 https://huggingface.co/merve/siglip-so400m-patch16-256-i18n

if you don't want to wait for the next transformers release install transformers from my PR https://github.com/huggingface/transformers/pull/32938 and initialize SigLIP from there

prithivMLmods rwightman enzostvs ucsahin


I feel like this incredible resource hasn't gotten the attention it deserves in the community!

@clefourrier  and generally the HuggingFace evaluation team put together a fantastic guidebook covering a lot about 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗜𝗢𝗡 from basics to advanced tips.

link : https://github.com/huggingface/evaluation-guidebook

I haven’t finished it yet, but i'am enjoying every piece of it so far. Huge thanks @clefourrier and the team for this invaluable resource !


🐦⬛ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐠𝐩𝐢𝐞?

🐦⬛ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐠𝐩𝐢𝐞?

🧗𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐧𝐨𝐧-𝐄𝐧𝐠𝐥𝐢𝐬𝐡 𝐝𝐚𝐭𝐚

🧗𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐧𝐨𝐧-𝐄𝐧𝐠𝐥𝐢𝐬𝐡 𝐝𝐚𝐭𝐚

Ok, you're finally convinced that synthetic data works... ⚗

𝐍𝐨𝐰 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐚𝐧 𝐢𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐟𝐨𝐫 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐢𝐧 𝐚 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐨𝐭𝐡𝐞𝐫 𝐭𝐡𝐚𝐧 𝐄𝐧𝐠𝐥𝐢𝐬𝐡.
But how do you get started?

I explore how to do this with Magpie in my new article
https://huggingface.co/blog/anakin87/multilingual-magpie

---

🐦⬛ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐠𝐩𝐢𝐞?

It's a recent technique for creating synthetic instruction datasets.

Magpie is based on a simple but ingenious idea 👇
if you prompt an instruction-tuned model with a pre-query template, you can make it generate a plausible user query/instruction

Here's an example:
model: Llama-3-8B-Instruct
pre-query template: "<|begin_of_text|><|start_header_id|>user<|end_header_id|>"
generated user instruction: "What are some of the responsibilities of a commercial pilot?"

You can then feed this instruction back into the same model to get the assistant response.

By repeating this process, it's possible to generate large synthetic datasets with relatively little effort.

🪄 The authors demonstrate that using these datasets for Supervised Fine Tuning (SFT) can yield strong performance, even competitive with the original instruct model.

🧗𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐧𝐨𝐧-𝐄𝐧𝐠𝐥𝐢𝐬𝐡 𝐝𝐚𝐭𝐚

Most Language Models are primarily trained on English texts, so they tend to produce data in English.

How can we overcome this?

Earlier approaches were complex or costly.

Then @mrm8488  found a simple solution: add the target language to the pre-query template.
For Spanish, the template becomes "<|begin_of_text|><|start_header_id|>user<|end_header_id|>spanish:".

This method works for Spanish and German!

❌ Unfortunately, it does not work well for other languages (🇮🇹, 🇳🇱, ...)

👇


Bellman, the Swedish finetune, has once again returned in his biggest incarnation yet, at 12b. Based on Mistral-Nemo-Instruct: https://huggingface.co/neph1/Mistral-Nemo-Instruct-bellman-12b


Spent the weekend testing out some prompts with 🕵♂Mystery Bot🕵♂ on my mobile... exciting things are coming soon for the following languages:

🌐Arabic, Chinese, Czech, Dutch, English French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese!🌐


I’m recently experimenting with the Flux-Ultra Realism and Real Anime LoRA models, using the Flux.1-dev model as the base. The model and its demo example are provided in the Flux LoRA DLC collections.📃

🥳Demo : 🔗 https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

🥳Model:
-  https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0
-  https://huggingface.co/prithivMLmods/Flux-Dev-Real-Anime-LoRA

🥳For more details, please visit the README.md of the Flux LoRA DLC Space & https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32

Minhajameen Stopwolf AdinaY prithivMLmods ai4life44


🌐 Introducing Websim.ai User Projects Dataset - https://huggingface.co/datasets/nyuuzyou/websim

Dataset highlights:
- 137,452 user projects from Websim.ai, a service for creating small sites using Large Language Models (LLMs)
- Primarily in English, with potential for multilingual content in generated websites
- Each entry includes: project metadata, user information, and generated HTML content
- Contains detailed information about project revisions, site generation, and user interactions
- Data covers a wide range of user-generated website projects created through AI assistance
- Dedicated to the public domain under Creative Commons Zero (CC0) license

The dataset can be used for analyzing AI-assisted web development trends, studying user behavior in LLM-powered creative tools, and exploring the capabilities of language models in web design.


This is probably a very hot take, but here goes nothing.

With the incredibly accurate LoRAs we see emerge for high quality models like FLUX from services like fal.ai that offer training within single digit minutes, e.g. 2 min per 1000 iterations.

Why the hell are people publishing private LoRAs as public models?!
Take a look at this listing: https://huggingface.co/models?other=base_model:adapter:black-forest-labs%2FFLUX.1-dev&sort=created

I would expect that people that hold a HF account have some kind of forward thinking. Heck, do you really want to give anyone the power to create ultra realistic images of yourself?!

Didn't we learn anything from social media?
I am puzzled..


Last Week in Medical AI: Top LLM Research Papers/Models 🔥
🏅 (October 12 - October 19, 2024)

Medical LLM & Other Models:
- OLAPH: Factual Biomedical LLM QA
- LLMD: Interpreting Longitudinal Medical Records
- LifeGPT: Generative Transformer for Cells
- MedCare: Decoupled Clinical LLM Alignment
- Y-Mol: Biomedical LLM for Drug Development

Frameworks and Methodologies:
- MedINST: Biomedical Instructions Meta Dataset
- Democratizing Medical LLMs via Language Experts
- MCQG-SRefine: Iterative Question Generation
- Adaptive Medical Language Agents
- MeNTi: Medical LLM with Nested Tools

Medical LLM Applications:
- AGENTiGraph: LLM Chatbots with Private Data
- MMed-RAG: Multimodal Medical RAG System
- Medical Graph RAG: Safe LLM via Retrieval
- MedAide: Multi-Agent Medical LLM Collaboration
- Synthetic Clinical Trial Generation

Medical LLMs & Benchmarks:
- WorldMedQA-V: Multimodal Medical LLM Dataset
- HEALTH-PARIKSHA: RAG Models Evaluation
- Synthetic Data for Medical Vision-Language

Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!

- Youtube: https://youtu.be/LROOjWXUgvg?si=s-nNDOSD3BrsHYjQ
- Spotify : https://open.spotify.com/episode/12xeN2vnOTRdDrHbWqhV6I?si=bd7c8d9fee8049fd

aaditya benhachem Joseph717171 shetumohanto


How the 🗣🏆 leaderboard of a merged TTS Arena with the 🤗 Spaces fork would look like. These results are somewhat unreliable as some models have not challenged the other in the list. And the original TTS Arena used only narration type sentences.


🚀  Announcement for the Lovely community! 🚀

Just launched the https://huggingface.co/spaces/zamal/DeepSeek-VL-1.3B-Chat on Hugging Face, and it's ready for YOU to explore! 💬🖼

This full-fledged model is perfect for advanced image and text interactions, with zero GPU required. The Deepseek VL-1.3B Chat typically needs around 8 GB of VRAM and storage of almost 4 GB, but now you can experience it hassle-free right on our space!

Want something lighter? We’ve also uploaded a 4 bit quantized version (just around 1GB!), available on my profile. Perfect for those with limited hardware. 🌍🔍

Come try it now and see what this model can do! 🚀✨


Triton nanoGPT now has a custom cross entropy loss kernel 🚀
Next: matmul, gradually overthrowing all major PyTorch ops:)

Simplified pseudo for parallel cross-entropy loss compute:
- init program: get pid, compute offsets, load targets.
- init row_max and row_sum.
- for-loop1 (find max logits): update row_max with max logits.
- for-loop2 (compute softmax and loss): compute row_sum, update loss.
- add log(row_sum) and store loss.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/triton_nanoGPT.ipynb


Looks like @Meta thinks we forgot they created PyTorch, so now they've open-sourced Lingua, a powerful and flexible library for training and inferencing large language models.

Things that stand out:

- Architecture: Pure PyTorch `nn.Module` implementation for easy customization.

- Checkpointing: Uses the new PyTorch distributed saving method (.distcp format) for flexible model reloading across different GPU configurations.

- Configuration: Utilizes data classes and YAML files for intuitive setup and modification.

- Profiling: Integrates with xFormers' profiler for automatic MFU and HFU calculation, plus memory profiling.

- Slurm Integration: Includes `stool.py` for seamless job launching on Slurm clusters.

Some results from @Meta to show off:

- 1B parameter models trained on 60B tokens achieve strong performance across various NLP tasks.

- 7B parameter Mamba model (trained on 200B tokens) shows competitive results with Llama 7B on benchmarks like ARC, MMLU, and BBH.

If you're working on LLM research or looking to experiment with cutting-edge language model architectures, Lingua is definitely worth exploring.


Interested in performing inference with an ONNX model?⚡

The Optimum docs about model inference with ONNX Runtime is now much clearer and simpler!

You want to deploy your favorite model on the hub but you don't know how to export it to the ONNX format? You can do it in one line of code as follows:
```py
from optimum.onnxruntime import ORTModelForSequenceClassification

# Load the model from the hub and export it to the ONNX format
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
model = ORTModelForSequenceClassification.from_pretrained(model_id, export=True)
```
Check out the whole guide 👉 https://huggingface.co/docs/optimum/onnxruntime/usage_guides/models

John6666 adeiza-x louisbrulenaudet Aurelien-Morgan itsbrex


Hi HuggingFacers!🤗

As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows.

I try to explain the state of AI energy consumptions, its environmental impact and the key points of "turning AI nuclear" in my last article on HF community blog: https://huggingface.co/blog/as-cle-bert/ai-is-turning-nuclear-a-review

Enjoy the reading!🌱

rmayormartins John6666 den0620 daniel-ltw not-lain louisbrulenaudet SriramRokkam odyss3y julesbsz


Last Thursday at KaggleX organized by Google, I presented a workshop on "Unlocking the Power of Large Language Models (LLMs) for Business Applications" where I explained how we can reduce the size of LLM models to make them more suitable for business use and addressing common resource limitations.
https://drive.google.com/file/d/1p5sT4_DeyBuwCqmYt4dCJKZOgLMpESzR/view


Every adult on the planet knows what a vector is and has the basic understanding of how they are utilized right in their heads. You just don't know it as vector math. You do not know a 2-D vector as a 2-D vector, you know it as a graph. Want to know more? Check out this video, I break down the concept in about 10 minutes and I am positive you will fully understand it by the end: https://youtu.be/Iny2ughcGsA


Artificial Minds, Human Consequences: Unraveling AI’s Impact on Education, Cognition, and Cultural Production

https://empereur-pirate.medium.com/artificial-minds-human-consequences-unraveling-ais-impact-on-education-cognition-and-cultural-a503b88d4524

This text critiques AI language models, particularly in education, arguing their use hinders deep learning, critical thinking, and social-emotional development despite automating tasks. It raises copyright concerns, highlighting lawsuits against companies like OpenAI and Microsoft. AI "creativity" is challenged, emphasizing its reliance on stochastic processes and lack of true understanding. Ethical implications, including bias and misuse, are explored, along with environmental costs. The text contrasts the human brain's complex temporalities and adaptability with the static nature of current AI models, which lack genuine long-term memory and continuous learning.


Made a notable change to the TTS Arena fork. I do not think anyone is interested in which bottomfeeder TTS is better than another beside it. So one of the top 5 TTS is always chosen in a challenge for more scrutiny. Also these top 5 are taken from preliminary results.
https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena


SambaNova ☁
⚡ Inference API with cURL Demo: https://huggingface.co/spaces/prithivMLmods/sambanova-inference-api

🔗Sambanova API Documentation : (grab your APIs here)  https://cloud.sambanova.ai/apis

```
export SAMBANOVA_API_KEY=<your token>
```
Sambanova's Inference API.

```
pip install sambanova-gradio
```
SambaNova X Gradio

```
import gradio as gr
import sambanova_gradio

gr.load(
    name='Meta-Llama-3.1-405B-Instruct',
    src=sambanova_gradio.registry,
).launch()
```
📃 Documentation: https://community.sambanova.ai/docs

John6666 victor akhaliq AtAndDev prithivMLmods


I'm now working on finetuning of coding models. If you are GPU-hungry like me, you will find quantized models very helpful. But quantization for finetuning and inference are different and incompatible. So I made two collections here.

Inference (GGUF, via Ollama, CPU is enough)
https://huggingface.co/collections/onekq-ai/ollama-ready-coding-models-67118c3cfa1af2cf04a926d6

Finetuning (Bitsandbytes, QLora, GPU is needed)
https://huggingface.co/collections/onekq-ai/qlora-ready-coding-models-67118771ce001b8f4cf946b2

For quantization, the inference models are far more popular on HF than finetuning models. I use https://huggingface.co/QuantFactory to generate inference models (GGUF), and there are a few other choices.

But there hasn't been such a service for finetuning models. DIY isn't too hard though. I made a few myself and you can find the script in the model cards. If the original model is small enough, you can even do it on a free T4 (available via Google Colab).

If you know a (small) coding model worthy of quantization, please let me know and I'd love to add it to the collections.

djuna John6666 valentimarco RalFinger rajkumar321 regisss


Large Language Models in Targeted Sentiment Analysis (2404.12342)

📢 Excited to share that our studies 📄 "Large Language Models in Targeted Sentiment Analysis for Russian" has recently become in 📘 Springer Lobachevskii Journal of Mathematics 🥳✨ ...

📘 https://link.springer.com/article/10.1134/S1995080224603758

In this studies we provide such a diverse look and experiments over various 🤖 LLM models 🤖 scaled from 7B in two different modes: ❄ zero-shot and 🔥 fine-tuned (Flan-T5 only) using Three-Hop reasoning technique.
We showcase the importance of performing:
💚 text translation into English
💚 application on Chain-of-Thought for Implicit Sentiment Analysis

More:
📄 Arxiv: https://arxiv.org/abs/2404.12342
🧑💻 Code: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework
🤗 Models: https://huggingface.co/papers/2404.12342
🎥 Video @NLPSummit: https://www.youtube.com/watch?v=qawLJsRHzB4

THOR: https://github.com/scofield7419/THOR-ISA


Good folks at @Apple have developed a novel method called KV Prediction that significantly reduces the "time to first token" (TTFT) for on-device LLM inference.

Some highlights of the paper:

• Uses a small auxiliary transformer model to efficiently predict the KV cache of a larger base model
• Reduces TTFT by up to 4x while retaining 60-80% accuracy on benchmarks
• Achieves Pareto-optimal efficiency-accuracy trade-off compared to baselines
• Demonstrates 15-50% relative accuracy improvements on TriviaQA at equal TTFT FLOP budgets
• Shows up to 30% accuracy gains on HumanEval code completion at fixed TTFT FLOP counts
• Validated on Apple M2 Pro CPU, proving FLOP gains translate to real-world speedups

So, how's it done?

Based on the KV Prediction method described in the paper, here are the key steps for how it's done:

1. Choose a base model and an auxiliary model:
- The base model is a larger, pretrained transformer model that will be used for final generation.
- The auxiliary model is a smaller transformer model used to efficiently process the input prompt.

2. Design the KV predictor:
- Create a set of learned linear projections to map from the auxiliary model's KV cache to the base model's KV cache.
- Define a mapping from auxiliary cache layers to base cache layers.

3. Training process:
- Pass input tokens through the auxiliary model to get its KV cache.
- Use the KV predictor to generate a predicted KV cache for the base model.
- Run the base model using the predicted KV cache and compute losses.
- Backpropagate errors through the frozen base model to update the auxiliary model and KV predictor.

4. Inference process:
- Process the input prompt with the auxiliary model to get its KV cache.
- Use the KV predictor to generate the predicted base model KV cache.
- Run a single token generation step with the base model using the predicted KV cache.
- Continue autoregressive generation with the base model as normal.

Excited to hear your thoughts!

John6666 YaTharThShaRma999 barakplasma KingNish myronkoch AtAndDev GoDjMike


A new `timm` release (1.0.11) is out now. A also wrote an article on one of the included models: https://huggingface.co/blog/rwightman/mambaout

Featured in the release are:
  * The MambaOut model, a cheeky arch inspired by SSM but without the SSM part, a ConvNeXt with gating.
  * Several timm trained MambaOut variations with arch tweaks and ImageNet-12k pretrain to verify scaling, supplement ported weights.
  * The smallest MobileNetV4, a 0.5x width scaled Conv-Small.
  * Two impressive MobileNetV3 Large models outperforming all previous, using MNV4 Small recipe.
  * 'Zepto,' a new compact ConvNeXt variant even smaller than the previous Atto, 2.2M params, RMSNorm, and solid results for its size.
  * Newly ported SigLIP SO400M/16 ViT multi-lingual weights, the largest i18n weights, prevous was B/16.
  * Two ImageNet-1k fine-tuned SigLIP SO400M models at 378x378
  * InternViT 300M weight port. A really solid ViT encoder distilled from OpenGVLab 6B VL model encoder.
  * An assortment of very small, sub 1M param pretrained test models to improve library unit tests and serve low-resource applications.


China is advancing rapidly in AI technology while maintaining a strong focus on governance 🇨🇳📑
We've collected key AI governance documents released since 2017 and will continue updating them in this organization on the hub 👉China LLMs on Hugging Face
✨ https://huggingface.co/spaces/zh-ai-community/china-ai-policy-research
Any feedback is welcome🤗


What a great day for Open Science! @AIatMeta released models, datasets, and code for many of its research artefacts! 🔥

1. Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. A new developer suite will be added to make it easier for developers to build with SAM 2.

Model checkpoints: https://huggingface.co/collections/reach-vb/sam-21-6702d40defe7611a8bafa881

2. Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance.

Model checkpoints: https://huggingface.co/collections/facebook/layerskip-666b25c50c8ae90e1965727a

3. SALSA: New code enables researchers to benchmark AI-based attacks to validate security for post-quantum cryptography.

Repo: https://github.com/facebookresearch/LWE-benchmarking

4. Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale.

Repo: https://github.com/facebookresearch/lingua

5. Meta Open Materials: New open source models and the largest dataset to accelerate AI-driven discovery of new inorganic materials.

Model checkpoints: https://huggingface.co/fairchem/OMAT24

6. MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder covering 80 languages.

Model checkpoint: https://huggingface.co/facebook/MEXMA

7. Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations.

Model checkpoint: https://huggingface.co/facebook/Self-taught-evaluator-llama3.1-70B

8. Meta Spirit LM: An open-source language model for seamless speech and text integration.

Repo: https://github.com/facebookresearch/spiritlm

abidlabs John6666 pcuenq clem blanchon nbroad kuotient


🕵♂ Let’s investigate NVIDIA's Llama-3.1-Nemotron-70B-Instruct-HF model performance - great analysis by @alozowski!

https://x.com/ailozovskaya/status/1847278827925291370


📈 Increase the quality of your RAG with a simple Linear Layer! No need to change your embedding model (keep that old OpenAI API).

Introducing EmbeddingAlign RAG, a novel approach to improve Retrieval-Augmented Generation (RAG) systems.

Key highlights:
- Uses a simple linear transformation on existing embeddings
- Boosts hit rate from 89% to 95% on real-world examples
- Minor increase on latency (less than 10ms)
- Works on top of blackbox embedding models (Mistral AI, OpenAI, Cohere,...)
- No dataset needed (just your documents)
- Train easily on CPU

🤗 Read the full article here on HF: https://huggingface.co/blog/PLB/embedding-align-rag


𝟮. 𝗟𝗮𝘁𝗲-𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗖𝗼𝗹𝗕𝗘𝗥𝗧 🏃

𝟮. 𝗟𝗮𝘁𝗲-𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗖𝗼𝗹𝗕𝗘𝗥𝗧 🏃

𝟯. 𝗘𝗮𝗿𝗹𝘆 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: 𝗖𝗿𝗼𝘀𝘀-𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 🏋

𝟯. 𝗘𝗮𝗿𝗹𝘆 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: 𝗖𝗿𝗼𝘀𝘀-𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 🏋

𝗛𝗼𝘄 𝘁𝗼 𝗿𝗲-𝗿𝗮𝗻𝗸 𝘆𝗼𝘂𝗿 𝘀𝗻𝗶𝗽𝗽𝗲𝘁𝘀 𝗶𝗻 𝗥𝗔𝗚 ⇒ ColBERT, Rerankers, Cross-Encoders

Let’s say you’re doing RAG, and in an effort to improve performance, you try to rerank a few possible source snippets by their relevancy to a query.

How can you score similarity between your query and any source document? 🤔 📄 ↔ 📑

𝟭. 𝗝𝘂𝘀𝘁 𝘂𝘀𝗲 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 : 𝗡𝗼-𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 🏎

This means that you encode each token from both the query and the doc as separate vectors, then average the tokens of each separately to get in total 2 vectors, then you compute similarity via cosine or something.
➡ Notable examples: Check the top of the MTEB leaderboard!

𝟮. 𝗟𝗮𝘁𝗲-𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗖𝗼𝗹𝗕𝗘𝗥𝗧 🏃

These encode each token from both query and doc as separate vectors as before, but compare all together without previously averaging them and losing information.

This is more accurate than no-interaction but also slower because you have to compare n*m vectors instead of 2. At least you can store documents in memory. And ColBERT has some optimisations like pooling to be faster.

➡ Notable examples: ColBERTv2, mxbai-colbert-large-v1, jina-colbert-v2

𝟯. 𝗘𝗮𝗿𝗹𝘆 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: 𝗖𝗿𝗼𝘀𝘀-𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 🏋

Basically you run the concatenated query + document in a model to get a final score.

This is the most accurate, but also the slowest since it gets really long when you have many docs to rerank! And you cannot pre-store embeddings.

➡ Notable examples: MixedBread or Jina AI rerankers!

🚀 So what you choose is a trade-off between speed and accuracy: I think ColBERT is often a really good choice!

Based on this great post by Jina AI 👉 https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter


Depth Pro: Sharp Monocular Metric Depth in Less Than a Second (2410.02073)

It's raining depth estimation models ☔
DepthPro is a zero-shot depth estimation model by Apple, it's fast, sharp and accurate 🔥
Demo: https://huggingface.co/spaces/akhaliq/depth-pro
Model: https://huggingface.co/apple/DepthPro
Paper page: https://huggingface.co/papers/2410.02073

The model consists of two encoders: an encoder for patches and an image encoder 🖼 The outputs of both are merged to decode to depth maps and get the focal length.
The model outperforms the previous state-of-the-art models in average of various benchmarks 📑

victor YaTharThShaRma999 John6666 baratpaim adorkin mpottinger AtAndDev


✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:

✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:

📣 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 v0.26.0 is out with some new features and improvements!

✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
- 🔐 Multiple access tokens support: Easily manage multiple access tokens with new CLI commands. Perfect for handling multiple tokens with specific permissions in production or when collaborating with external teams.
- 🖼 Conversational VLMs inference is now supported with InferenceClient's chat completion!
- 📄 Daily Papers API: Seamlessly search and retrieve detailed paper information from the Hub!

We’ve also introduced multiple bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! 🤗

Check out the release notes here: https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/9

and you can try it out now 👇
```
pip install huggingface_hub==0.26.0
```

Chris4K victor asoria John6666 AtAndDev Nymbo louisbrulenaudet


if you're encountering 500 errors on spaces that seem to work otherwise , kindly consider screenshotting and sharing the link here : https://discord.com/channels/879548962464493619/1295847667515129877


🚨🔥 New Release Alert! 🔥🚨

Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! 💻💥
👉 Check it out: https://huggingface.co/hbseong/HarmAug-Guard (Yes, INFERENCE CODE INCLUDED! 💡)

More details in our paper: https://arxiv.org/abs/2410.01524 📜

#HarmAug #LLM # Safety #EfficiencyBoost #Research #AI #MachineLearning

Seanie-lee hbseong John6666 adorkin AtAndDev

FM-1976 AtAndDev TuringsSolutions hbseong


🚀 Child Safe Chatbot (Experimental)Based on google/shieldgemma-9b. Currently in the experimental phase, this configuration could be a starting point to consolidate an LLM that meets the requirements to be safe for minors.
👉 https://huggingface.co/spaces/nmarafo/Child-Safe-Chatbot
#EdTech #ChildSafety #AI #Education #Experimental