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... | Want to read curated list of papers by @akhaliq in your mail box?
Thanks to the API provided by Hugging Face, I made a simple GitHub Action based newsletter bot to send out 🤗 Daily Papers. Check out the attached video clip to get a sense of what it is!
Internally, it leverages Gemini API to assign tags for each paper, and all papers are archived by tags and batches. Of course, you can directly go to the papers' pages from your mail box to check out the full paper!
Since everything is automated, GitHub Action and Gemini API are free, and the subscription management is free via Google Groups, this newsletter bot is entirely free. Furthermore, if you wish, you could fork the project for your own newsletter service.
subscription: https://groups.google.com/g/hf-daily-paper-newsletter
project repo: https://github.com/deep-diver/hf-daily-paper-newsletter
In the next step, I will experimentally add auto translation (to Korean) feature for every papers. | {
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"raw": "🙋🏻♂️Hey there folks ,",
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"href": null... | 🙋🏻♂️Hey there folks ,
i wanted to share with you a really cool new organisation called
https://huggingface.co/lowres
In just one week it has gathered almost 150 members !
Check them out if you love anime , SDLX, LORAs and cool datasets.
can we make this one reach 200 members? 🚀 | {
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... | 2024-01-14T22:51:02.000Z | 2024-07-13T06:01:19.766Z | [
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"value": "🤦🏻♂️well, day before yesterday i was so happy about **gpuzero** that i made a bunch of demos : ",
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"code... | 🤦🏻♂️well, day before yesterday i was so happy about **gpuzero** that i made a bunch of demos : https://huggingface.co/posts/Tonic/802671427380916
- one for YI-200K , but it actually doesnt quite fit on a GPUZero... U_U
- one for SDXL style align, but omg i didnt even realize at the time it wasnt my demo of it (lol)
- one for texify (which works great btw, keep an eye on texify, it's about to blow up... in a couple of months!)
so yeah, i ran back and tried to get my demos working at least for sdxl which i love , but i simply couldnt get the CPU stuff working, or the refactored code working. no wonder i was thinking "wow this is so easy" on @osanseviero 's demo : yeah , it's not my code that's why it works 😅🙏🏻
anyway spent the day unsuccessfully experimenting, but starting tomorrow i'll try to serve some cool and overlooked models so 🤗huggingface appreciators can try them out 🚀 | {
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"v... | Shocking: 2/3 of LLMs fail at 2K context length
code_your_own_ai makes a great vlog about mostly LLM related AI content.
As I watched the video below, I wondered about current best practices on LLM evaluation. We have benchmarks, we have sota LLMs evaluating LLMs, we have tools evaluating based on human comparison.
Often, I hear, just play with the LLM for 15 mins to form an opinion.
While I think for a specific use case and clear expectations, this could yield signal carrying experiences, I also see that one prompt is used to judge models.
While benchmarks have their weaknesses, and are by themselves not enough to judge model quality, I still think systematic methods that try to reduce various scientifically known errs should be the way forward, even for qualitative estimates.
What do you think? How can we make a public tool for judging models like lmsys/chatbot-arena-leaderboard help to leverage standards known in social science?
https://www.youtube.com/watch?v=mWrivekFZMM | {
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885363857514207 | [
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... | Here is my selection of papers for today (12 Jan)
https://huggingface.co/papers
PALP: Prompt Aligned Personalization of Text-to-Image Models
Object-Centric Diffusion for Efficient Video Editing
TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Rendering
Diffusion Priors for Dynamic View Synthesis from Monocular Videos
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
TOFU: A Task of Fictitious Unlearning for LLMs
Patchscope: A Unifying Framework for Inspecting Hidden Representations of Language Models
Secrets of RLHF in Large Language Models Part II: Reward Modeling
LEGO:Language Enhanced Multi-modal Grounding Model
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages
A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
Towards Conversational Diagnostic AI
Transformers are Multi-State RNNs
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
Distilling Vision-Language Models on Millions of Videos
Efficient LLM inference solution on Intel GPU
TrustLLM: Trustworthiness in Large Language Models | {
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... | 2024-01-12T14:44:25.000Z | 2024-01-12T14:44:25.072Z | [] | /posts/akhaliq/885363857514207 | 14 | 0 |
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"value": "Let's play a little game, how would you build the Rabbit R1 with open source tech? Here is my stack:",
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"... | Let's play a little game, how would you build the Rabbit R1 with open source tech? Here is my stack:
- https://huggingface.co/openai/whisper-small for awesome Speech-to-Text with low latency
- https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1 for an awesome super powerful LLM Brain
- https://huggingface.co/coqui/XTTS-v2 for a nice and clean voice
Which stack will you personally choose? | {
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... | /posts/victor/688106937901639 | 131 | 19 |
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"value": "Sharing a super-fast segmentation model today 💨 ",
"raw": "Sharing a super-fast segmentation model today 💨 ",
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... | Sharing a super-fast segmentation model today 💨
SlimSAM is pruned-distilled version of SAM model, it's (up-to 8.6x) faster and smaller yet very powerful! ⚡️
It has the same architecture as SAM, meaning you can use the 🤗 transformers code for SAM on SlimSAM models ⬇️ (yes only 3 lines of code!)
```python
from transformers import pipeline
generator = pipeline(model="nielsr/slimsam-50-uniform", task="mask-generation")
outputs = generator(image)
```
Lastly, I have built an app for you to compare SlimSAM and SAM outputs
https://huggingface.co/spaces/merve/slimsam | {
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... | As a GPU poor, I found a nice open source project.
dstackai is the perfect open source project for GPU poor. Simply specify resource requirements (GPU RAM, spot, ...), then will suggest the cheapest options among the popular GPU cloud providers (AWS, GCP, Azure, Lambda Labs, TensorDock, and vast.ai)
Provision VM instances in 3 different use cases. These are the essential for any ML projects.
- Dev: connect provisioned VM instance to your fav IDE (including Jupyter)
- Task: run experiments (training, fine-tuning, ...) via SSH
- Service: run your model in production via HTTPS
dstack is 100% open source, but you need to have your own accounts for each GPU cloud provider, enough GPU quota, configure credentials, etc., all by yourself. Luckily, dstack will announce dstack cloud which let you not worried about all the hassles. The price is almost as same as you directly connect to each cloud with your account.
The attached code snippet shows you how to provision a Mistral-7B model in Text Generation Inference(TGI) on the cheapest VM instance (of having 24GB of VRAM). Then you get the HTTPS connection to it, and play with it as usual with TGI client library as attached in the second code snippet.
If you want to learn more about dstack, check out the official website. Without GPU sponsors, as an individual open source contributor in ML, this kind of project is pretty important.
: https://dstack.ai/
If you are looking for an alternative, there is SkyPilot project as well
: https://github.com/skypilot-org/skypilot | {
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RE-Introducing, some of the best SFT model, he legend: DOLPHIN. This model is very special, a LASER-UNA model: UNA-dolphin-2.6-mistral-7b-dpo-laser
@fblgit in collaboration with @fernandofernandes and @ehartford | {
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... | Here is my selection of papers for today (11 Jan)
https://huggingface.co/papers
ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video https://huggingface.co/papers/2401.05314
Score Distillation Sampling with Learned Manifold Corrective https://huggingface.co/papers/2401.05293
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes https://huggingface.co/papers/2401.05335
PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models https://huggingface.co/papers/2401.05252
URHand: Universal Relightable Hands https://huggingface.co/papers/2401.05334
The Impact of Reasoning Step Length on Large Language Models https://huggingface.co/papers/2401.04925
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk https://huggingface.co/papers/2401.05033 | {
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"raw": " 🙋🏻♂️hey there folks , 🌟Tonic here",
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"... | 🙋🏻♂️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 | {
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"labe... | 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 | {
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"value": "Last month was great for faster/smaller segmentation models, and I wanted to dedicate my first post to compile the recently released SAM variants! 🤗",
"raw": "Last month was great for faster/smaller segmentation models, and I wanted to dedicate my first post to compile the r... | 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! | {
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... | 2024-01-10T19:58:41.000Z | 2024-01-10T19:59:42.650Z | [] | /posts/merve/533880363228237 | 29 | 0 |
462914041098598 | [
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"val... | 🔥 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?
| {
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... | 2024-01-10T18:43:17.000Z | 2024-01-21T19:26:53.267Z | [
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"... | Finally found my go-to hat for 2024 😎
Thanks to https://huggingface.co/fal-ai | {
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... | 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 | {
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... | 2024-01-10T15:11:03.000Z | 2024-01-10T18:02:54.693Z | [
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379937660970830 | [
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"raw": "hello world! ",
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"resource": null... | 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. | {
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... | 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 | {
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204586934082310 | [
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... | 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
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... | 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 | {
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248307299871659 | [
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... | 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 | {
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321536384660058 | [
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"value": "👋 Hi there!",
"raw": "👋 Hi there!",
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... | 👋 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! | {
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"raw": "Train ANYTHING on Hugging Face Spaces hardware using AutoTrain SpaceRunner: ",
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"label": null... | Train ANYTHING on Hugging Face Spaces hardware using AutoTrain SpaceRunner: https://hf.co/blog/stefan-it/autotrain-flair-mobie 💥 | {
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... | 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
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"type"... | 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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... | 2024-01-04T15:54:11.000Z | 2024-01-04T16:02:27.001Z | [] | /posts/fffiloni/915335447857525 | 202 | 0 |
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... | 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 | {
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"value": "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 wi... | 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! | {
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... | 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
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... | 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 | {
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... | 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 | {
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}... | ✅ 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! 🚀 | {
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... | 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 | {
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"raw": "💬 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 kn... | 💬 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! | {
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"... | 2023-12-28T16:45:36.000Z | 2023-12-28T16:45:36.328Z | [] | /posts/alvarobartt/421657801791921 | 437 | 0 |
656973498012745 | [
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"value": "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:",
"raw": "Intr... | 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. | {
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"raw": "Do you use Python to visualize data? Wouldn't it be nice if an AI chatbot can help you write Python code and impro... | 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
| {
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"raw": "some mandatory hello world as first post 🤗",
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```
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
tokenizer.encode("Hello world~ Hello 2024").tokens
``` | {
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570492751808024 | [
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"value": "Here is my selection of papers for today (27 Dec) on Hugging Face daily papers newsletter",
"raw": "Here is my selection of papers for today (27 Dec) on Hugging Face daily papers newsletter",
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"user... | 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 | {
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... | 2023-12-27T17:04:19.000Z | 2023-12-27T21:50:29.606Z | [] | /posts/akhaliq/570492751808024 | 11 | 0 |
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... | 💨 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 | {
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206565732690667 | [
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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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"value": "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 hal... | 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 | {
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"value": "Papers/Models"... | 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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@echo off
echo hello world
pause
```
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"raw": "boomers still pick zenodo.org instead of hu... | 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. | {
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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. | {
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"type"... | 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 :) | {
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... | 💾🧠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! | {
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"value": "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 settin... | 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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"type"... | 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
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"re... | 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. | {
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"href": ... | 🙋🏻♂️ 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 ! | {
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https://youtu.be/B9lMONNngGM
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"co... | 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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Parents now: Teach the kids to fix the code when it starts walking around 🤖✨ | {
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🔥 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 | {
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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
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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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... | 2024-10-24T20:45:22.000Z | 2024-11-03T04:10:51.322Z | [
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"raw": "If you have ~300+ GB of V-RAM, you can run Mochi from ",
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"type": "men... | 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"... | 2024-10-24T19:13:20.000Z | 2024-10-25T20:33:12.062Z | [
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657096750247570 | [
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"label"... | 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. | {
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"value": null,... | 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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"value": "🌟🌎 Cohere releases Aya 8B & 32B: SOTA multilingual models for 23 languages !",
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"label": ... | 🌟🌎 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 | {
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... | 2024-10-24T14:11:52.000Z | 2024-10-24T14:11:52.486Z | [] | /posts/m-ric/149699867340480 | 1,942 | 0 |
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"value": "🚨 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: ",
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"resource"... | 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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"value": "Are you a Professional Python Developer? Here is why Logging is important for debugging, tracking and monitoring the code",
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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 | {
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547097184415362 | [
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"value": "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... | 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
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"value": "Since 2022 I have been trying to understand how to support advancement of the two best python patterns for AI development which are:",
"raw": "Since 2022 I have been trying to understand how to support advancement of the two best python patterns for AI development which are:"... | 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 | {
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"value": "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",
"raw": "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 y... | 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 | {
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219908014902053 | [
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"raw": "🔥🔥🔥Introducing Oryx-1.5!",
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"href"... | 🔥🔥🔥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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478366490704768 | [
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"value": "# PyTorch == 2.5.0 Breaks Transformers' SDPAttention!",
"raw": "# PyTorch == 2.5.0 Breaks Transformers' SDPAttention!",
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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 | {
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... | 2024-10-23T09:20:20.000Z | 2024-10-23T09:20:20.749Z | [] | /posts/beomi/478366490704768 | 3,713 | 0 |
808673436695273 | [
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"value": "The Mystery Bot 🕵️♂️ saga I posted about from earlier this week has been solved...🤗",
"raw": "The Mystery Bot 🕵️♂️ saga I posted about from earlier this week has been solved...🤗",
"href": null,
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"user": nul... | 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 | {
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"resource"... | 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! | {
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"raw": "LoRA with code 🚀 using PEFT (parameter efficient fine-tuning)",
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... | 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. | {
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"la... | Just released a dataset with 7000+ hours of synthetically generated lo-fi music. https://huggingface.co/datasets/vikhyatk/lofi | {
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"value": "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 o... | 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 | {
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634777850443475 | [
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"value": "Finding the Best SmolLM for Your Project ",
"raw": "Finding the Best SmolLM for Your Project ",
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"value": null,... | 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! 👇 | {
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364264215572346 | [
{
"type": "text",
"value": "🌍 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!",
"raw": "🌍 I’ve always had a dream of making AI accessible to everyone, regardless of locat... | 🌍 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 | {
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444566857659250 | [
{
"type": "text",
"value": "🇫🇷 Lancement officiel de l'OpenLLM French Leaderboard : initiative open-source pour référencer l’évaluation des LLMs francophones",
"raw": "🇫🇷 Lancement officiel de l'OpenLLM French Leaderboard : initiative open-source pour référencer l’évaluation des LLMs francophones",
... | 🇫🇷 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. | {
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"value": "observers 🔭 - automatically log all OpenAI compatible requests to a dataset💽",
"raw": "observers 🔭 - automatically log all OpenAI compatible requests to a dataset💽",
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"label": ... | 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 | {
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"value": "🎉 Reached HuggingFace Trending Top 100 in Just One Day! Introducing Mouse-I",
"raw": "🎉 Reached HuggingFace Trending Top 100 in Just One Day! Introducing Mouse-I",
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"label": null... | 🎉 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
```
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"lan... | Did a quick conversion from flux1-fill-dev to diffusers. Release here:
https://huggingface.co/xiaozaa/flux1-fill-dev-diffusers
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"raw": "Wanted to move eyes with Flux.1 schnell, prompts failed.Made a guide image, surprisingly useful on its own. in... | 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
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"raw": "Vision finetuning is in 🦥Unsloth! You can now finetune Llama 3.2, Qwen2 VL,... | 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 | {
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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. | {
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... | 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!! | {
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"... | 2024-11-21T16:38:57.000Z | 2024-11-21T16:38:57.310Z | [] | /posts/victor/448624842722506 | 1,639 | 0 |
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🥳Flux LoRA Dlc : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
Thankyou! | {
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... | 2024-10-22T11:28:11.000Z | 2024-10-22T11:29:19.633Z | [] | /posts/prithivMLmods/371681139214764 | 1,919 | 0 |
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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! 🤩
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"... | https://huggingface.co/papers/2410.15735 | {
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... | 2024-10-22T05:37:09.000Z | 2024-10-22T05:37:09.221Z | [] | /posts/abhishek/971438953844337 | 4,318 | 0 |
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"type": "new_l... | 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 | {
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"value": " of Allura has put out Meadowlark, an RP-focused Mistral Small... | @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 | {
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"resource"... | 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 🚀🚀 | {
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