OpenELM: An Efficient Language Model Family with Open-source Training
  and Inference Framework (2404.14619)

OpenELM

An Efficient Language Model Family with Open-source Training and Inference Framework

https://huggingface.co/papers/2404.14619

The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks. To this end, we release OpenELM, a state-of-the-art open language model. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. For example, with a parameter budget of approximately one billion parameters, OpenELM exhibits a 2.36% improvement in accuracy compared to OLMo while requiring 2times fewer pre-training tokens. Diverging from prior practices that only provide model weights and inference code, and pre-train on private datasets, our release includes the complete framework for training and evaluation of the language model on publicly available datasets, including training logs, multiple checkpoints, and pre-training configurations. We also release code to convert models to MLX library for inference and fine-tuning on Apple devices. This comprehensive release aims to empower and strengthen the open research community, paving the way for future open research endeavors.


I have built a Space to compare different vision language model outputs, which model should I add next? 👀
Try them yourself here https://huggingface.co/spaces/merve/compare_VLMs


Fine tune Phi-3 using samatha themed dataset and Huggingface SFT trainer!

In this colab, we simply apply a supervised finetune to phi-3 using the sharegpt format.

```
def formatting_prompts_func(examples):
    convos = examples["conversations"]
    texts = []
    mapper = {"system": "system\n", "human": "\nuser\n", "gpt": "\nassistant\n"}
    end_mapper = {"system": "", "human": "", "gpt": ""}
    for convo in convos:
        text = "".join(f"{mapper[(turn := x['from'])]} {x['value']}\n{end_mapper[turn]}" for x in convo)
        texts.append(f"{text}{EOS_TOKEN}")
    return {"text": texts}

dataset = dataset.map(formatting_prompts_func, batched=True)
print(dataset['text'][8])

```
Opus Samantha consists of 1848 samples with the samantha personality. The dataset covers a wide variety of topics such as logical reasoning, mathematics, legal, and rp.

This notebook serves as a viable option to finetune Phi-3 until Unsloth supports phi-3, which should be very soon. When that happens check out AutoSloth for both SFT, DPO, and langfuse format RAG fine tuning on free tier colab hardware.

Resources:
Dataset: https://huggingface.co/datasets/macadeliccc/opus_samantha
Colab: https://colab.research.google.com/drive/1e8LILflDQ2Me52hwS7uIfuJ9DxE2oQzM?usp=sharing
AutoSloth: https://colab.research.google.com/drive/1Zo0sVEb2lqdsUm9dy2PTzGySxdF9CNkc#scrollTo=bpimlPXVz-CZ

victor KingNish Qwoook midesk pabloce lunarflu andrewatef EddyGiusepe

santyzenith clavel lunarflu Norod78 trtm


Complete Guide to SUPIR Enhancing and Upscaling Images Like in Sci-Fi Movies on Your PC : https://youtu.be/OYxVEvDf284

In this video, I explain how to 1 click install and use the most advanced image upscaler / enhancer in the world that is both commercially and open source available. The upscaler that I am going to introduce you is open source #SUPIR and the model is free to use. SUPIR upscaler is many times better than both paid Topaz AI and Magnific AI and you can use this upscaler on your computer for free forever. The difference of SUPIR vs #Topaz and #Magnific is like ages. So in this tutorial you are going to learn everything about how to install, update and use SUPIR upscaler on your personal computer. The video shows Windows but it works perfectly fine on Linux as well.

Scripts Download Link ⤵
https://www.patreon.com/posts/99176057

Samplers and Text CFG  (Text Guidance Scale) Comparison Link ⤵
https://imgsli.com/MjU2ODQz/2/1

How to install accurate Python, Git and FFmpeg on Windows Tutorial ⤵
https://youtu.be/-NjNy7afOQ0

Full DreamBooth / Fine-tuning Tutorial ⤵
https://youtu.be/0t5l6CP9eBg

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild : https://arxiv.org/abs/2401.13627

Authors introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. Authors collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential

MonsterMMORPG DmitryRyumin splendiferousConiferous sombaba DonRichards timothyupai catastropiyush vipulg louisbrulenaudet

MonsterMMORPG distantquant cocodark StatsGary dotdotgod kevinpics mtasic85


Phi-3 Technical Report: A Highly Capable Language Model Locally on Your
  Phone (2404.14219)

Phi-3 Technical Report

A Highly Capable Language Model Locally on Your Phone

https://huggingface.co/papers/2404.14219

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench).

himanshubeniwal wchai RahulSharma0 dotdotgod AtAndDev


Testing the Phi-3-mini 4k on HuggingChat. How well can it craft a tweet?

Not bad at all:
```
Excited to unveil phi-3-mini, a compact yet powerful 3.8B parameter model, outperforming giants like Mixtral & GPT-3.5 on benchmarks & safe for phones! *
#Al #Phi3 #LanguageModel #Techinnovation #Phi3Miniml
```
The models are here:
- Phi-3-Mini-4K-Instruct: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
-Phi-3-Mini-128K-Instruct: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct

Try it out in Hugging Chat: https://huggingface.co/chat/models/microsoft/Phi-3-mini-4k-instruct


Happy to announce https://imgsys.org -- a sister project to Chatbot Arena by lmsys -- for comparing different text guided image generation models models. Try it natively on HuggingFace: https://huggingface.co/spaces/fal-ai/imgsys

clefourrier clem julien-c victor radames multimodalart badayvedat DmitryRyumin seyf1elislam lysandre ArthurZ cansa KingNish


Thrilled to introduce our Hyper-SD, offering hyper-fast⚡ and hyper-quality✨ text-to-image generation.
Our model achieves SINGLE-STEP inference on both SD1.5 and SDXL architecture without evident losses of aesthetics, styles and structures.
Project page: https://hyper-sd.github.io
HuggingFace repo: https://huggingface.co/ByteDance/Hyper-SD

renyuxi multimodalart DmitryRyumin Tonic YaTharThShaRma999 radames victor alielfilali01


All You need To Know About Phi-3 (Technical Report Walkthrough)

Summary of Summaries:
Phi-3-mini
- Architecture specs: decoder-only transformer, ModelSize: 3.8 billion
  parameters,  LongRope [ 128K Context length ], Vocab Size [ 32064 ],
  trained on 3.3 trillion tokens. at bfloat16.
- Rivals performance to larger models like Mixtral 8x7B and GPT-3.5,
  capable of running locally on a smartphone.
- Utilizes high quality training dataset heavily filtered from web data and
   llm-generated synthetic data.
- Can be quantized to 4-bits, occupying ≈ 1.8GB of memory.
- Ran natively on iPhone 14 with A16 Bionic chip with inference speed of up
   to 12 tokens per second.

Phi-3-small
- Architecture specs: Also decoder-only, 7B parameters, Vocab size [ 100352 ], default context length [ 8k ], Context Length: 8K, Hidden Dimension: 4096, Number of Heads and Layers: Follows 7B class structure.
- Uses tiktoken tokenizer (for enhanced multilingual tokenization)

Phi-3-medium:
-  Architecture specs: Also decoder-only, Hidden Dimension: 5120, Number of Heads: 40, Number of Layers: 40, Tokenization: Consistent with other models, Training on 4.8 trillion tokens.

Training Methodology:
- Focuses on high-quality training data deviating from standard scaling laws.
- The models undergo two-phase pre-training using a mix of web sources and synthetic data for general knowledge and logical reasoning skills.

Performance:
- Phi-3-mini achieves competitive scores on standard benchmarks like MMLU and MT-Bench, indicating strong reasoning capabilities.
- Higher variants show even better performance, suggesting effective scaling with increased model size.

Limitations:
- phi-3-mini: limited by its smaller size in tasks requiring extensive factual knowledge, primarily  supports English.
- phi-3-small limited multilingual support.

Hosting LLMs locally is a big win for OSS - private, secured inferencing on the go😎

LHPKAI heydrnik Tonic xiyuzhai Akhil2507 mrfakename Adriato MissKinziRoze victor rashakol19 AtAndDev youknowwhatAImean EddyGiusepe cuongtaongoc


Learn2Talk: 3D Talking Face Learns from 2D Talking Face (2404.12888)

🚀🎭🔥 New Research Alert (Avatars Collection)! 🔥👄🚀
📄 Title: Learn2Talk: 3D Talking Face Learns from 2D Talking Face

📝 Description: Learn2Talk is a framework that leverages expertise from 2D talking face methods to improve 3D talking face synthesis, focusing on lip synchronization and speech perception.

👥 Authors: Yixiang Zhuang et al.

🔗 Paper: https://huggingface.co/papers/2404.12888

🌐 Github Page: https://lkjkjoiuiu.github.io/Learn2Talk/

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #Learn2Talk #3DTalkingFace #SpeechDrivenFacialAnimation #LipSync #SpeechPerception #ComputerVision #ImageProcessing #DeepLearning

DmitryRyumin KvrParaskevi AlekseiPravdin alicegaz


Trained another version of llama3-8b-instruct which beats the base model. This time without losing too many points on gsm8k benchmark. Again, using AutoTrain 💥 pip install autotrain-advanced
Trained model: https://huggingface.co/abhishek/autotrain-llama3-orpo-v2

abhishek clem GAurav19 not-lain louisbrulenaudet JayGhiya


Why does Meta invest millions in Llama 3 and then makes it available for free? Here is Zuckerberg's explanation to investors in the Q3 2023 earnings call:

"The second part of our playbook is open source software infrastructure. Our long-standing strategy has been to build and open source general infrastructure while keeping our specific product implementations proprietary.

[...] First, open source software is typically safer and more secure, as well as more compute efficient to operate due to all the ongoing feedback, scrutiny, and development from the community. This is a big deal because safety is one of the most important issues in AI. Efficiency improvements and lowering the compute costs also benefit everyone including us.

Second, open source software often becomes an industry standard, and when companies standardize on building with our stack, that then becomes easier to integrate new innovations into our products. That’s subtle, but the ability to learn and improve quickly is a huge advantage and being an industry standard enables that.

Third, open source is hugely popular with developers and researchers. We know that people want to work on open systems that will be widely adopted, so this helps us recruit the best people at Meta, which is a very big deal for leading in any new technology area.

And again, we typically have unique data and build unique product integrations anyway, so providing infrastructure like Llama as open source doesn't reduce our main advantages. This is why our long-standing strategy has been to open source general infrastructure and why I expect it to continue to be the right approach for us going forward."

Fully earnings call transcript: https://s21.q4cdn.com/399680738/files/doc_financials/2023/q4/META-Q4-2023-Earnings-Call-Transcript.pdf

dippatel2506 clem Vinzou boapps louisbrulenaudet m-ric ppsingh


How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study (2404.14047)

We release an empirical study to showcase "How Good Are Low-bit Quantized hashtag#LLaMA3 🦙 Models" with existing LLM quantization techniques!

In this study, the performance of the low-bit LLaMA3 models (especially LLaMA3-70B) is impressively notable. 🚀 However, the results also exposed significant performance degradation issues faced by existing quantization techniques when dealing with LLaMA3, especially under ultra-low bit-width.

We hope this study can serve as a reference for the LLM quantization community and promote the emergence of stronger LLM quantization methods in the context of LLaMA3's release. More work is on the way...

https://huggingface.co/papers/2404.14047

https://huggingface.co/collections/LLMQ/llama3-quantization-66251258525135aeda16513c


ByteDance released new distillation technique Hyper-SD (https://huggingface.co/ByteDance/Hyper-SD)  for efficient image generation.
Here a few demos:

Official:
Hyper-SDXL-1Step-T2I https://huggingface.co/spaces/ByteDance/Hyper-SDXL-1Step-T2I

Hyper-SD15-Scribble https://huggingface.co/spaces/ByteDance/Hyper-SD15-Scribble

Unofficial Demos: InstantStyle + Hyper SD1.5 (not great but super fast) https://huggingface.co/spaces/radames/InstantStyle-Hyper-SD

InstantStyle + Hyper SDXL https://huggingface.co/spaces/radames/InstantStyle-Hyper-SDXL


I'm super happy to co-organize the (Mechanistic) Interpretability social at #ICLR2024 with @nikhil07prakash ! 🔍

If you plan to attend, help us make this meetup awesome by filling the form below! 😄

📅 Wed, May 8, 12:45-2:15 PM
🔗 RSVP & share your ideas here: https://forms.gle/FWap4KW2ikdntjfb8


Introduction to HelpingAI-3B-v2.1 and HelpingAI-3B-v2.2

These models are designed to engage in dialogues that are not only contextually relevant but also deeply empathetic, tailoring their responses to the emotional state and context of the user. With advanced emotional understanding capabilities, they can comfort, celebrate, and address complex feelings with a level of emotional nuance that is unparalleled in the AI domain.

Emotional Intelligence Capabilities

The core of these models lies in their emotional intelligence capabilities. They exhibit several key traits that enable them to respond emotionally resonant:

- Emotion recognition and validation: The models can identify and validate the emotions expressed by the user, ensuring that their responses are grounded in understanding.
- Empathetic perspective-taking: They are capable of taking the user's perspective, understanding their feelings, and responding with empathy.
- Generating emotionally supportive language: The models can generate language that is supportive and comforting, tailored to the user's emotional state.
- Contextual emotional attunement: They can adapt their communication style to match the emotional context of the conversation, ensuring relevance and appropriateness.
- Using appropriate tone, word choice, and emotional expression**: The models are adept at using the right tone, words, and emotional expressions to convey their messages effectively.

Examples of Emotionally Intelligent Responses

Let's take a look at how these models can adapt their communication style with emotional nuance:

- Comforting someone grieving: The models can offer empathetic responses, acknowledging the user's feelings and suggesting practical ways to cope with grief.
- Celebrating positive news: They can generate responses that are joyful and supportive, encouraging the user to enjoy their positive experiences.
https://huggingface.co/OEvortex/HelpingAI-3B-v2.1
https://huggingface.co/OEvortex/HelpingAI-3B-v2.2


InstantStyle works with the 2-step SDXL-Lightning distilled model, reducing generation time from ~20s to ~9s!

In a big related update, as of today, Diffusers@main supports InstantStyle. I'm looking forward to playing with it!

https://github.com/huggingface/diffusers/pull/7668

https://huggingface.co/spaces/radames/InstantStyle-SDXL-Lightning
https://huggingface.co/ByteDance/SDXL-Lightning


Unlocking the Power of locally running Llama-3 8B Model Agents with Chat-UI! 🔥🚀✨

I'm thrilled to share my hackathon-style side project:
1. Finetuning Llama-8B for function calling using PEFT QLoRA as the instruct Llama-3 model doesn't support this. The colab notebook for it is here: https://lnkd.in/ggJMzqh2. 🛠
2. Finetuned model along with the 4-bit quants here: https://lnkd.in/gNpFKY6V ✨
3. Clone Hugging Face https://lnkd.in/gKBKuUBQ and make it compatible for function calling by building upon the PR https://lnkd.in/gnqFuAd4 for my model and local inferencing usecase using Ollama. This was a steep learning curve wherein I stayed awake the whole night to get it working. 💪🏽
4. Above, I used SerpAPI for web browsing and Mongo DB Atlas free tier for persistence of conversations and assistant configs. 🔎
5. More work is required to switch between using tools and responding directly wherein I see the model breaks. 🧐

How cool is this wherein we are approaching experience akin to ChatGPT while using local hosted agent model running on your laptop! 💻

zarazi Kukedlc Abhaykoul AlekseiPravdin Tonic victor RedSparkie jaocseverywhere johnbendi

DmitryRyumin Tonic Nayer-ali med4u alleniver kpi parsapico johnbendi


Great in-depth Llama-3 tests from @wolfram, of the models from Meta of course but also @MaziyarPanahi @emozilla @turboderp :  https://huggingface.co/blog/wolfram/llm-comparison-test-llama-3

Spotted by @jack-kumar

wolfram fdaudens raincandy-u MaziyarPanahi julien-c SaylorTwift


Enabling Natural Zero-Shot Prompting on Encoder Models via
  Statement-Tuning (2404.12897)

Prompting BERT!

Zero-shot learning ability is the hottest thing about causal LLMs. You don't need to finetune causal LLMs on each specific task. Instead, you can use prompting and get a decent performance on unseen tasks.

Unfortunately, autoencoding LLMs -  like our dear friend BERT 🙋♂- lack this ability and you need a task-specific head for different tasks. But what if you could prompt all the BERTs in the world?!

🥁 Introducing Statement-Tuning 🥁

Now hold your horses! don't go full-LLama on it yet. Using this finetuning approach, we can get zero-shot performance from encoders by turning a problem into a yes/no problem. Binary classification all the way down!
For example, a single entailment problem will be decomposed into 3 yes/no questions.

This is still not super useful. But I like works that try to make a little more space for encoders in the current autoregressive era!

Check the paper if interested: https://huggingface.co/papers/2404.12897

radames KingNish Unggi MarinaraSpaghetti


Idefics2 is trained mostly on OBELICS, our open interleaved image-text document dataset.

Training on interleaved data is crucial to reaching high performance on VQA tasks, taking an arbitrary number of images as input, and doing in-context learning.

Dataset: https://huggingface.co/datasets/HuggingFaceM4/OBELICS
Nomic visualization: https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f
Link to OBELICS thread: https://twitter.com/HugoLaurencon/status/1694005892839006301


Please... feed this Llama some Sauerkraut! 🍲

Said and done. Here it is. Our Sauerkraut Version of the strong Llama3-8b by Meta. Released from HANNOVER MESSE, just in front of meta booth.
https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct

According to benchmarks (LM-Evaluation-Harness 0.4.2), our #SauerkrautLM Dataset and fine-tuning pipeline improved the Model noticeably (AVG = 74,57), especially Reasoning and Common Sense capabilities.

Again we provide some more detail on the whole process:
✅ Original model: Llama-3-8b-Instruct
✅ Training Duration: 12 hours
✅ Training procedure: 2-staged DPO
✅ Trained data: 70k (first stage) and 20k (second stage)
✅ GPU: 4x RTX6000 ADA
✅ New model: Llama-3-SauerkrautLM-8b-Instruct
✅ Total training costs: 54,72 Dollar 💴 - RunPod FTW (excluding synthesizing data, curating data, benchmarks, error handling, testing)

See our model card on Hugging Face for more details: https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct

There will be more details on benchmarks during the next days.

Xorg2024 clem DaryoushV quyettv fynnkroeger jdjayakaran2410


Meta Llama 3 70B landed on the Leaderboard at the 11th position: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard


"Is this a bug? I'm able to post even though they didn't approve my request."


**Release Announcement: gliner_multi_pii-v1**

I am pleased to announce the release of gliner_multi_pii-v1, a model developed for recognizing a wide range of Personally Identifiable Information (PII). This model is the result of fine-tuning the urchade/gliner_multi-v2.1 on synthetic dataset (urchade/synthetic-pii-ner-mistral-v1).

**Model Features:**
- Capable of identifying multiple PII types including addresses, passport numbers, emails, social security numbers, and more.
- Designed to assist with data protection and compliance across various domains.
- Multilingual (English, French, Spanish, German, Italian, Portugese)

Link: https://huggingface.co/urchade/gliner_multi_pii-v1

```python
from gliner import GLiNER

model = GLiNER.from_pretrained("urchade/gliner_multi_pii-v1")

text = """
Harilala Rasoanaivo, un homme d'affaires local d'Antananarivo, a enregistré une nouvelle société nommée "Rasoanaivo Enterprises" au Lot II M 92 Antohomadinika. Son numéro est le +261 32 22 345 67, et son adresse électronique est harilala.rasoanaivo@telma.mg. Il a fourni son numéro de sécu 501-02-1234 pour l'enregistrement.
"""

labels = ["work", "booking number", "personally identifiable information", "driver licence", "person",  "address", "company",  "email", "passport number", "Social Security Number", "phone number"]
entities = model.predict_entities(text, labels)

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

```
Harilala Rasoanaivo => person
Rasoanaivo Enterprises => company
Lot II M 92 Antohomadinika => full address
+261 32 22 345 67 => phone number
harilala.rasoanaivo@telma.mg => email
501-02-1234 => Social Security Number
```

bfuzzy1 mrdbourke leslyarun Tatvajsh ksquarekumar manu xclyfe bitsTobyte timpearce


ChemLLM: A Chemical Large Language Model (2402.06852)

ChemLLM datasets is all open source now!
https://huggingface.co/papers/2402.06852
700K of SFT Dataset, ChemData700K For Chemistry of LLM!
https://huggingface.co/datasets/AI4Chem/ChemData700K
10K of DPO Dataset, ChemPref-10K, both English and Chinese!
https://huggingface.co/datasets/AI4Chem/ChemPref-DPO-for-Chemistry-data-en
https://huggingface.co/datasets/AI4Chem/ChemPref-DPO-for-Chemistry-data-cn
ChemBench-4K of 4100 high-quality single-choice benchmark for nine core Chemistry tasks!
https://huggingface.co/datasets/AI4Chem/ChemBench4K
C-MHChem, 600 real test questions written and checked manually, from 25 years of Chinese National Middle school chemistry Test!
https://huggingface.co/datasets/AI4Chem/C-MHChem-Benchmark-Chinese-Middle-high-school-Chemistry-Test
All hail to Open-source community!🤗

DmitryRyumin qq8933 ajibawa-2023 clem mmhamdy Tonic DRRRRDFFD tiandatong


🔥  **transformers**  4.40.0 is out 🔥

⚙ you can now push your custom pipelines easily to 🤗, allowing people to easily use your model in a more friendly, unified way.

🤓 I already updated my blog to match the new feature https://huggingface.co/blog/not-lain/custom-architectures-with-huggingface.

📃A list of some repos that have custom pipelines :
* https://huggingface.co/briaai/RMBG-1.4
* https://huggingface.co/p1atdev/siglip-tagger-test-3
* https://huggingface.co/sgugger/test-dynamic-pipeline

DmitryRyumin bfuzzy1 zolicsaki Tonic louisbrulenaudet


#TPU #PyTorch #Jax

When You're trying to use PyTorch or Jax on TPU,

for v2/v3/v4:
  use tpu-ubuntu2204-base

for v5p:
  use v2-alpha-tpuv5

for v5e:
  use v2-alpha-tpuv5-lite

You must use these base images for the system to 'boot'.

Previously used tpu-vm-v4-pt-1.13 images might seem to start the VM, but SSH connections do not work.

I thought it was a firewall issue and spent a lot of time on it before realizing it was a problem with the boot image 🥲

https://cloud.google.com/tpu/docs/runtimes#pytorch_and_jax

deepkyu maywell devngho byoussef Taekyoon


Meta's new LLama-3 (https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) is an extremely capable model out of the box for coding related tasks. It is the first model that we have seen that beats GPT-4 on Static-Analysis-Eval - https://huggingface.co/datasets/patched-codes/static-analysis-eval.

codelion ArchieX25 AayushShah victor clem g-ronimo Tonic


PersonaLLM: Investigating the Ability of Large Language Models to
  Express Personality Traits (2305.02547)

😀🤓😎 New Research Alert - NAACL 2024 (Big Five Personality Traits Collection)! 😎😉😤
📄 Title: PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits 💬

📝 Description: This research examines the ability of LLMs to express personality traits and finds that LLMs can generate content consistent with assigned personality profiles and that humans can recognize certain traits with up to 80% accuracy. However, accuracy drops significantly when annotators are aware that the content was generated by an AI.

👥 Authors: Hang Jiang et al.

📅 Conference: NAACL, June 16–21, 2024 | Mexico City, Mexico 🇲🇽

🔗 Paper: https://huggingface.co/papers/2305.02547

📁 Repository: https://github.com/hjian42/PersonaLLM

🚀 Added to the Big Five Personality Traits Collection: https://huggingface.co/collections/DmitryRyumin/big-five-personality-traits-661fb545292ab3d12a5a4890

🔥🔝 See also OCEAN-AI - https://huggingface.co/spaces/ElenaRyumina/OCEANAI (App, co-authored by @DmitryRyumin) 😉

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #PersonaLLM #OCEANAI #BigFive #PersonalityTraits #PersonalityAnalysis #Chatbots #LLMs #NAACL2024 #DeepLearning #Innovation


Open Models vs. Closed APIs for Software Engineers
-----------------------------------------------------------------------

If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.

But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:

1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.

2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.

3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)

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Already almost 1,000 llama3 model variations have been shared publicly on HF (many more in private use at companies): https://huggingface.co/models?p=5&sort=trending&search=llama3.

Everyone should fine-tune their own models for their use-cases, languages, industry, infra constraints,...

10,000 llama3 variants by the end of next week?

ijohn07 qnixsynapse Aeio DmitryRyumin Kathakali tomaarsen qubvel-hf rksiitd victor Jaward radames shiv2050 praveenpankaj Tonic rasonyang

yo DmitryRyumin alielfilali01 sepal tomaarsen fbjr raincandy-u radames Tonic louisbrulenaudet


HairFastGAN: Realistic and Robust Hair Transfer with a Fast
  Encoder-Based Approach (2404.01094)

🚀💇♂🔥 New Research Alert (Avatars Collection)! 🔥💇♀🚀
📄 Title: HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach

📝 Description: HairFastGAN is a fast, encoder-based approach to realistic and robust hair transfer that operates in the FS latent space of StyleGAN and includes enhanced in-painting and improved encoders for better alignment, color transfer, and post-processing.

👥 Authors: Maxim Nikolaev, Mikhail Kuznetsov, Dmitry Vetrov, and Aibek Alanov

🔗 Paper: https://huggingface.co/papers/2404.01094

📁 Repository: https://github.com/AIRI-Institute/HairFastGAN

🤗 Demo: https://huggingface.co/spaces/multimodalart/hairfastgan
🔥 Model 🤖: https://huggingface.co/AIRI-Institute/HairFastGAN

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords:  #HairFastGAN #StyleGAN #VirtualTryOn #HairTransfer #AIHairStyling #GenerativeModels #ComputerVision #ImageProcessing #DeepLearning

DmitryRyumin clem FantasiaFoundry multimodalart ameerazam08 tcarteratl victor AlekseiPravdin stinkyyy


# On Coding Your First Attention

While it’s not necessarily the case that you must code the attention block of a transformer from scratch to understand how it works, yet it sure is the closest you can get to having a first-principles understanding of why/how transformers behave the way they do.

@karpathy covered attention in detail in his nanoGPT video (strongly recommend watching). Now I would like to share some thoughts and experience in writing my first attention.

First let’s zoom out quickly and explain what attention is in transformers: Attention in transformers is a communication mechanism that allows the model to focus on different parts of the input sequence when making predictions.

It assigns weights to each input token based on its relevance to the current context, enabling the model to weigh information selectively. This mechanism helps transformers capture long-range dependencies and contextual information effectively.

The official AIAN paper introduced two commonly used forms of attentions: Scaled Dot-Product Attention (also known as Self-Attention) and a stack of self-attention blocks known as Multi-Head Attention.

# The Code

Now, attention as for most deep learning algorithms boils down to a math equation. So writing the code can get really trivial especially with a deep learning framework like PyTorch. Below is what's called a Single Head Attention

(image 2)

The code defines single-head attention in PyTorch - it transforms input vectors, computes attention scores and weights, and then calculates the weighted sum of values based on these weights (as per the attention equation)

When you have multiple of those stacked in parallel, you get what's called Multi-Head Attention. This gives a much simpler code if you are inheriting from the SingleHeadAttention class:

(image 3)

Full Article here: https://huggingface.co/blog/Jaward/coding-your-first-attention

DmitryRyumin Tensorkart Citaman alielfilali01 Theli LeroyDyer martineden


Shining Valiant 2 is here!
Finetuned on Llama 3 70b.

Super excited to build with Llama 3 - more models to come soon :)

https://huggingface.co/ValiantLabs/Llama3-70B-ShiningValiant2


Dynamic Typography: Bringing Words to Life (2404.11614)

Dynamic Typography

Bringing Words to Life

https://huggingface.co/papers/2404.11614

Text animation serves as an expressive medium, transforming static communication into dynamic experiences by infusing words with motion to evoke emotions, emphasize meanings, and construct compelling narratives. Crafting animations that are semantically aware poses significant challenges, demanding expertise in graphic design and animation. We present an automated text animation scheme, termed "Dynamic Typography", which combines two challenging tasks. It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. Our technique harnesses vector graphics representations and an end-to-end optimization-based framework. This framework employs neural displacement fields to convert letters into base shapes and applies per-frame motion, encouraging coherence with the intended textual concept. Shape preservation techniques and perceptual loss regularization are employed to maintain legibility and structural integrity throughout the animation process. We demonstrate the generalizability of our approach across various text-to-video models and highlight the superiority of our end-to-end methodology over baseline methods, which might comprise separate tasks. Through quantitative and qualitative evaluations, we demonstrate the effectiveness of our framework in generating coherent text animations that faithfully interpret user prompts while maintaining readability.

shellywhen math1761 alielfilali01 Csplk orionriker victor kgourgou

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💻 Smoothing the Transition from Service LLM to Local LLM

Imagine your go-to LLM service is down, or you need to use it offline – yikes! This project is all about having that "Plan B" ready to go. Here's LLaMA Duo I've been building with @sayakpaul :

✨ Fine-tune a smaller LLM: We used Hugging Face's alignment-handbook to teach a smaller-sized LLM to mimic my favorite large language model. Think of it as that super-smart AI assistant getting a capable understudy.

🤖 Batch Inference: Let's get that fine-tuned LLM working! My scripts generate lots of text like a champ, and we've made sure things run smoothly even with bigger workloads.

🧐 Evaluation: How well is my small LLM doing? We integrated with the Gemini API to use it as an expert judge – it compares my model's work to the original. Talk about a tough critic!

🪄 Synthetic Data Generation: Need to boost that model's performance? Using Gemini's feedback, we can create even more training data, custom-made to make the LLM better.

🧱 Building Blocks: This isn't just a one-time thing – it's a toolkit for all kinds of LLMOps work. Want to change your evaluation metrics? Bring in models trained differently? Absolutely, let's make it happen.

Why this project is awesome:

💪 Reliability: Keep things running no matter what happens to your main LLM source.
🔒 Privacy: Process sensitive information on your own terms.
🗺 Offline capable: No internet connection? No problem!
🕰 Version Control: Lock in your favorite LLM's behavior, even if the service model changes.

We'm excited to share the code on GitHub. Curious to see what you all think! 👉🏻 https://github.com/deep-diver/llamaduo

sayakpaul sudanenator Csplk Fetchniche ajibawa-2023 bjo163


Generalizable Face Landmarking Guided by Conditional Face Warping (2404.12322)

🚀👩🎤🌟 New Research Alert - CVPR 2024! 🌟👩🎤🚀
📄 Title: Generalizable Face Landmarking Guided by Conditional Face Warping

📝 Description: A new method is proposed to learn a generalizable face landmark that can handle different facial styles, using labeled real faces and unlabeled stylized faces.

👥 Authors: Jiayi Liang, Haotian Liu, Hongteng Xu, Dixin Luo

📅 Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸

🔗 Paper: https://huggingface.co/papers/2404.12322

🌐 Github Page: https://plustwo0.github.io/project-face-landmarker/
📁 Repository: https://github.com/plustwo0/generalized-face-landmarker

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #FaceLandmarking #DomainAdaptation #FaceWarpping #CVPR2024 #DeepLearning #Innovation


New multimodal dataset by @xai-org @liuhaotian 🤩❤ https://huggingface.co/datasets/xai-org/RealworldQA

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The Cauldron is a massive collection of 50 high-quality datasets, all converted to the user/assistant format, and ready to use to fine-tune any Vision Language Model.

The Cauldron covers a wide range of tasks, including general visual question answering, counting, captioning, text transcription, document understanding, chart/figure understanding, table understanding, visual reasoning, geometry, spotting differences between 2 images or converting a screenshot to a code.

https://huggingface.co/datasets/HuggingFaceM4/the_cauldron

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Can't wait to see multimodal LLama 3!

We released a resource that might come in handy: The Cauldron 🍯

The Cauldron is a massive manually-curated collection of 50 vision-language sets for instruction fine-tuning. 3.6M images, 30.3M query/answer pairs.

It covers a large variety of downstream uses: visual question answering on natural images, OCR, document/charts/figures/tables understanding, textbooks/academic question, reasoning, captioning, spotting differences between 2 images, and screenshot-to-code.

https://huggingface.co/datasets/HuggingFaceM4/the_cauldron

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Do I need to make it a tradition to post here every Friday? Well, here we are again!

This week, I'm happy to share that we have two official Mistral models on the Leaderboard! 🔥 You can check them out: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1 and https://huggingface.co/mistralai/Mixtral-8x22B-v0.1

The most exciting thing here? https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1 model got a first place among pretrained models with an impressive average score of 79.15!🥇 Not far behind is the Mixtral-8x22B-v0.1, achieving second place with an average score of 74.47! Well done, Mistral AI! 👏

Check out my screenshot here or explore it yourself at the https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard

The second news is that https://huggingface.co/CohereForAI/c4ai-command-r-plus model in 4-bit quantization got a great average score of 70.08. Cool stuff, Cohere! 😎 (and I also have the screenshot for this, don't miss it)

The last news, which might seem small but is still significant, the Leaderboard frontpage now supports Python 3.12.1. This means we're on our way to speed up the Leaderboard's performance! 🚀

If you have any comments or suggestions, feel free to also tag me on X (Twitter), I'll try to help – [at]ailozovskaya

Have a nice weekend! ✨

clem KonradSzafer Vanessasml clefourrier qubvel-hf SaylorTwift Nacholmo

clem KonradSzafer clefourrier SaylorTwift


Love this new Space built by @enzostvs + @Xenova for Transformers.js: Generate your own AI music (In-browser generation) with AI Jukebox

https://huggingface.co/spaces/enzostvs/ai-jukebox

radames Xenova enzostvs kramp victor qubvel-hf clem grv805 Azamat1k


IMO, the "grounded generation" feature from Cohere's CommandR+ has flown under the radar...

For RAG use cases, responses directly include inline citations, making source attribution an inherent part of generation rather than an afterthought 😎

Who's working on an open dataset with this for the HF community to fine-tune with??

🔗CommandR+ Docs: https://docs.cohere.com/docs/retrieval-augmented-generation-rag

🔗Model on the 🤗 Hub: https://huggingface.co/CohereForAI/c4ai-command-r-plus

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iAkashPaul tomaarsen abidlabs derek-thomas


Unsolvable Problem Detection: Evaluating Trustworthiness of Vision
  Language Models (2403.20331)

A great vision language benchmark: MM-UPD evaluates how model responds to unsolvable problems 🤓

As of now, most VLMs, including GPT-4V and LLaVA-Next-34B, struggle with refusing to answer
Dataset https://huggingface.co/datasets/MM-UPD/MM-UPD
Paper https://huggingface.co/papers/2403.20331

KingNish qubvel-hf AlekseiPravdin raincandy-u anldrms


🦫 We have just released https://huggingface.co/datasets/argilla/Capybara-Preferences in collaboration with Kaist AI (@JW17, @nlee-208) and Hugging Face (@lewtun)

A new synthetic preference dataset built using `distilabel` on top of the awesome https://huggingface.co/datasets/LDJnr/Capybara from @LDJnr

The current dataset combines the already generated alternative completions from https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized, while also adding the remaining ones using the same approach!

Here are some key features on how we built it:

- 🧹 Duplicate removal, keeping the conversation besides the last assistant response, and some slight pre-processing

- 🤖 Generation of alternative completions for the existing conversations (last turn only) with: https://huggingface.co/mlabonne/NeuralBeagle14-7B, https://huggingface.co/argilla/notus-7b-v1, and https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B

- 👨🏻🏫 Running UltraFeedback via GPT-4 to generate the critique i.e. ratings and rationales, for the last assistant responses

- 🎉 Finally, we selected the chosen and rejected responses based on their UltraFeedback score, and applied some slight post-processing!

Sounds simple right? Start building your own synthetic datasets with https://github.com/argilla-io/distilabel already!

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In the vector search setup, we normally combine a fast embedding model and an accurate but slow reranker model.

The newly released @jinaai rerankers are small in size and almost as accurate as our base reranker. This means given a time constraint, it can scoring more candidate documents from embedding models and have a better chance to feed LLM the correct context for RAG generation.

These models are available on Huggingface and has been integrated into the latest SentenceTransformers 2.7.0. Check it out!

https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
https://huggingface.co/jinaai/jina-reranker-v1-tiny-en

tomaarsen qubvel-hf clem sigridjineth dev7halo ceoofcapybaras


Turns out if you do a cute little hack, you can make https://hf.co/nateraw/musicgen-songstarter-v0.2 work on vocal inputs. 👀

Now, you can hum an idea for a song and get a music sample generated with AI 🔥🔥

Give it a try: ➡ https://hf.co/spaces/nateraw/singing-songstarter ⬅

It'll take your voice and try to autotune it (because let's be real, you're no michael jackson), then pass it along to the model to condition on the melody. It works surprisingly well!

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🥳 New license for datasets: Apache 2.0!

I have been struggling mentally for many months now with the OpenAI terms of use that indicate that their model outputs cannot be used to build "competing models". This leads to many questions:

- what is the definition of competing? Is it the same as "commercial"?
- since this is part of the terms of use between OpenAI and the API user, can a third party still use the generated dataset to build competing models?
- are such restrictions even legal in the first place?

Trying to "follow the rules" as much as possible despite wanting to be as open as possible, I kept releasing my datasets under non-commercial licenses (which are too restrictive anyhow - nothing should prevent you from using the data in non-LM commercial settings), just like models trained on these datasets. This has put me at a competitive disadvantage compared to creators who do not follow the same approach and release their data/models on apache 2.0 despite the OpenAI "restrictions". Moreover, I fear (https://twitter.com/BramVanroy/status/1780220420316164246) that my approach blocks adaptation of my data/models for (commercial) applications/integrations.

Thankfully @Rijgersberg  noted that these OpenAI terms of use are NOT explicit in the Azure OpenAI API (https://twitter.com/E_Rijgersberg/status/1780308971762450725). Since my latest datasets were created via Azure, this comes as a relief. As far as I can tell after digging through Azure docs, this allows me to change all recent GPT4-generated datasets to apache 2.0! 🥳

- https://huggingface.co/datasets/BramVanroy/ultrachat_200k_dutch
- https://huggingface.co/datasets/BramVanroy/orca_dpo_pairs_dutch
- https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch
- https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch_cleaned
- https://huggingface.co/datasets/BramVanroy/no_robots_dutch

I will have to mull over what I'll do for the older GPT3.5 datasets. What do you think that I should do?


With AutoTrain, you can already finetune the latest llama3 models without writing a single line of code. Here's an example finetune of llama3 8b model: https://huggingface.co/abhishek/autotrain-llama3-no-robots

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Open-source AI on your phone? The HuggingChat app is out for iOs, with the best models: Command R, Zephyr Orpo, Mixtral, Gemma... https://apps.apple.com/ca/app/huggingchat/id6476778843?l=fr-CA

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In a basic chatbots, errors are annoyances. In medical LLMs, errors can have life-threatening consequences 🩸

It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.

This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.

https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard

Congrats to  @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co/blog/leaderboard-medicalllm

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KonradSzafer BrigitteTousi lunarflu fdaudens MaziyarPanahi mmhamdy Ramikan-BR

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🚀 Sentence Transformers v2.7.0 is out! Featuring a new loss function, easier Matryoshka model inference & evaluation, CrossEncoder improvements & Intel Gaudi2 Accelerator support. Details:

1⃣ A new loss function: CachedGISTEmbedLoss
This loss function is a combination of CachedMultipleNegativesRankingLoss and the GISTEmbedLoss, both of which are already excellent. The caching mechanism allows for much higher batch sizes with constant memory usage, which boosts training performance. The GIST part introduces a guide model to guide the in-batch negative sample selection. This prevents false negatives, resulting in a stronger training signal.

2⃣ Automatic Matryoshka model truncation
Matryoshka models produce embeddings that are still useful after truncation. However, this truncation always had to be done manually, until now! We've added a `truncate_dim` option to the Sentence Transformer constructor. This also allows truncation when using `HuggingFaceEmbeddings` from LlamaIndex or LangChain.

3⃣ Additionally, you can now specify `truncate_dim` in evaluators to get the performance after truncation. (Hint: it's surprisingly good, even for models not trained with MatryoshkaLoss, and it can speed up e.g. clustering, retrieval, etc.)

4⃣ CrossEncoder improvements
The CrossEncoder now supports 'push_to_hub' to upload trained reranker models to Hugging Face. Additionally, CrossEncoders now support `trust_remote_code` to load models with custom modelling code.

5⃣ Inference on Intel Gaudi2
If you have an Intel Gaudi2 Accelerator, Sentence Transformers now uses it automatically for even faster inference. No changes are necessary to your code, the device is automatically detected!

Check out the release notes for all of the details: https://github.com/UKPLab/sentence-transformers/releases/tag/v2.7.0

I'm very excited for the upcoming releases: I'm making great progress with a notable v3 refactor that should heavily improve the training process for embedding models!

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🔥 Next Thursday 4/25 at 8am PT / 11am ET / 17h CET,  join our live Hugging Cast to learn how to deploy open models on Google Cloud.

Register ➡ https://streamyard.com/watch/xz2nxp85Pi6e

@philschmid , @tengomucho ,  @jeffboudier will show you brand new Hub integrations built with GCP
🔥 with HF Inference Endpoints
🌎 with Vertex and GKE
🚀 on TPU

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🚀 **InfiniTransformer, Gemma/Llama3 based Implementation!** 🌌

> Update @ 2024.04.19: It now supports Llama-3!

> Note: this implementation is unofficial

This implementation is designed to handle virtually infinite context lengths.

Here's the github repo: https://github.com/Beomi/InfiniTransformer

📄 **Read the original Paper:** https://arxiv.org/abs/2404.07143

## **Focus on Infini-Attention**

- **2 Types of Implementation available:** Attention-layer only implementation / Model & Train-wise implementation
- **Fixed(segment dependent) Memory Usage:** Enables training on larger models and longer sequences without the memory overhead typical of standard Transformer implementations.
- **Infinite Context Capability:** Train with unprecedented sequence lengths—imagine handling up to 1 million sequence lengths on standard hardware!
  - You could train Gemma-2B with 1M sequence length with 2K segmentation size with single H100 GPU.

## **Try InfiniTransformer**

1. **Clone the repository:**
   ```bash
   git clone https://github.com/Beomi/InfiniTransformer
   ```
2. **Install necessary tools:**
   ```bash
   pip install -r requirements.txt
   pip install -e git+https://github.com/huggingface/transformers.git@b109257f4f#egg=transformers
   ```
3. **Dive Deep into Custom Training:**
   - Train with extensive sequence lengths using scripts such as `./train.gemma.infini.noclm.1Mseq.sh`.

for more detailed info, please visit Repo: https://github.com/Beomi/InfiniTransformer

Look forward to see your feedbacks! 😊

ps. Training loss plot is here 😉

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Multilingual RAG optimized models and datasets available from the Language Technologies Unit @ the Barcelona Supercomputing Unit
We are releasing new RAG-optimized multilingual models and dataset, within the AINA project contributions:

https://huggingface.co/projecte-aina/FlorRAG , based on out Bloom Flor6.3b model, capable of RAG in Catalan, Spanish and English

https://huggingface.co/datasets/projecte-aina/RAG_Multilingual , a 56K+ instructional dataset with human-like answers created from kernel-of-truth of extractive datasets using a Mixtral8x7b model


Let's breakdown the technical details in Microsoft's mind blowing Lifelike audio-driven talking faces framework - VASA and model VASA-1:

Summary of Summaries
- The paper introduces VASA, a framework for generating lifelike talking faces with appealing visual affective skills (VAS) from a single image and speech audio.
- Core innovations include a diffusion-based model for holistic generation of facial dynamics and head movements in an expressive, disentangled face latent space developed using video data..
- VASA-1 Generates high-quality 512x512 videos at up to 40 FPS with low latency.
- Supports real-time generation of lifelike, emotive talking faces.

Summary of Overall Framework:
- VASA generates facial dynamics and head motion in latent space, conditioned on audio and other signals
- Instead of directly generating video frames, it generates holistic facial dynamics and head motion in a latent space, conditioned on audio and optional signals.
- To achieve this, the framework uses a face encoder-decoder to extract appearance and identity features and train a Diffusion Transformer model to generate motion latent codes.

Technical Method Details:
Expressive and Disentangled Face Latent Space Construction:
   - Based on 3D-AID face reenactment framework
   - Decomposes face into 3D appearance volume, identity code, head pose,
      and facial dynamics latents
   - Uses encoders to extract these latent factors from face images.
   - Applies additional losses to improve disentanglement:
       - Pairwise head pose and facial dynamics transfer loss
       - Face identity similarity loss for cross-identity pose/dynamics transfer

Holistic Facial Dynamics Generation with Diffusion Transformer:
- Represents all facial movements (lip, expression, gaze, etc.) as a single
latent sequence
 - Applies a Diffusion Transformer model to generate the facial dynamics sequence.
- Diffusion Transformer trained with simplified denoising score matching objective.

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We posted new SOTA SambaLingo 70B parameter models for Arabic, Thai and Hungarian!

Check out the models here https://huggingface.co/collections/sambanovasystems/sambalingo-65e25770f2037c85ad35ca77

and our paper
https://arxiv.org/abs/2404.05829

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lunarflu Axilex abidlabs franchb ChangranHuuu


Diaries of Open Source. Part 15 🤗

🕵♀Idefics 2 is out, a multimodal open-source model with very nice capabilities
Models, demo, and datasets: https://hf.co/collections/HuggingFaceM4/idefics2-661d1971b7c50831dd3ce0fe
Blog: https://hf.co/blog/idefics2

💾Snowflake released snowflake-arctic-embed, a family of powerful small embedding models
Model: https://huggingface.co/Snowflake/snowflake-arctic-embed-m
Blog: https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/

✨Pile-T5, EleutherAI's T5 model trained on 2T tokens
Blog: https://blog.eleuther.ai/pile-t5/
Models: https://hf.co/collections/EleutherAI/pile-t5-65a76a0d0022dd270b385a66
GitHub: https://github.com/EleutherAI/improved-t5

🤖CodeQwen1.5-7B base and chat models. Models trained on 3T tokens strong benchmark results for code generation, editing and SQL
Blog post: https://qwenlm.github.io/blog/codeqwen1.5/
Demo: https://hf.co/spaces/Qwen/CodeQwen1.5-7b-Chat-demo
Models: https://hf.co/Qwen/CodeQwen1.5-7B and https://hf.co/Qwen/CodeQwen1.5-7B-Chat

Misc
🦉 DocOwl1.5: Unified Stucture Learning for OCR-free Document Understanding https://hf.co/spaces/mPLUG/DocOwl
👀Cerule - a tiny Vision LM model https://hf.co/Tensoic/Cerule-v0.1
ChemLLM - a LLM for chemistry and molecule science ⚗https://hf.co/AI4Chem/ChemLLM-7B-Chat-1.5-DPO
Distil Whisper Large
📝New pdf/OCR datasets with 19 samples https://hf.co/collections/pixparse/pdf-document-ocr-datasets-660701430b0346f97c4bc628
🔥Gretel AI high quality text-to-sql synthetic dataset https://huggingface.co/datasets/gretelai/synthetic_text_to_sql

DmitryRyumin BrigitteTousi lunarflu not-lain fdaudens Dlbk clem ajibawa-2023 tranhoangnguyen03 dillfrescott clefourrier diwank Farabi224

lunarflu not-lain Dlbk clem pabloce dillfrescott diwank Ollyy

lunarflu not-lain Dlbk clem dillfrescott


PSYDIAL: Personality-based Synthetic Dialogue Generation using Large
  Language Models (2404.00930)

😀🤓😎 New Research Alert - LREC-COLING 2024 (Big Five Personality Traits Collection)! 😎😉😤
📄 Title: PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models

📝 Description: The PSYDIAL presents a novel pipeline for generating personality-based synthetic dialog data to elicit more human-like responses from language models, and presents a Korean dialog dataset focused on personality-based dialog.

👥 Authors: Ji-Eun Han et al.

📅 Conference: LREC-COLING, May 20-25, 2024 | Torino, Italia 🇮🇹

🔗 Paper: https://huggingface.co/papers/2404.00930

🚀 Added to the Big Five Personality Traits Collection: https://huggingface.co/collections/DmitryRyumin/big-five-personality-traits-661fb545292ab3d12a5a4890

🔥🔝 See also OCEAN-AI - https://huggingface.co/spaces/ElenaRyumina/OCEANAI (App, co-authored by @DmitryRyumin) 😉

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #PSYDIAL #PersonalityDialogues #SyntheticData #LanguageModels #ConversationalAI #KoreanDialogues #BigFivePersonality #ExtraveresionDialogues #OCEANAI #BigFive #PersonalityTraits #PersonalityAnalysis #LREC-COLING2024 #DeepLearning #Innovation

DmitryRyumin osanseviero BrigitteTousi lunarflu Dlbk DESUCLUB KvrParaskevi thinhlpg

DmitryRyumin BrigitteTousi lunarflu Dlbk clem


I see you all send your documents to close-source APIs, this is not ok 👎 it breaks my heart 💔
I have seen many open-source document models, and I am amazed by what IDEFICS2 has done with document understanding 🤯🤩 it's not something you've ever seen before! https://huggingface.co/spaces/HuggingFaceM4/idefics-8b

Please use it! Has Apache 2.0 license ❤

DmitryRyumin YaTharThShaRma999 victor Masa-Erland osanseviero nkasmanoff BrigitteTousi lunarflu fdaudens Ryukijano Dlbk samusenps clem matlok VictorSanh CookieMaster Tonic not-lain

lunarflu Ryukijano clem raghavprabhakar CookieMaster jbilcke Tonic not-lain


📣 I'm thrilled to announce "ALERT: A Comprehensive #Benchmark for Assessing #LLMs’ Safety through #RedTeaming" 🚨

📄 Paper: https://arxiv.org/pdf/2404.08676.pdf
🗃 Repo: https://github.com/Babelscape/ALERT
🤗 ALERT benchmark: https://huggingface.co/datasets/Babelscape/ALERT
🤗 ALERT DPO data: https://huggingface.co/datasets/Babelscape/ALERT_DPO

As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡

For collecting our prompts, we started from the popular
Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.

In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).

For more details, check out our preprint: https://arxiv.org/pdf/2404.08676.pdf 🤓

Huge thanks to @felfri, @PSaiml, Kristian Kersting, @navigli, @huu-ontocord and @BoLi-aisecure (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂

victor clefourrier osanseviero BrigitteTousi lunarflu huu-ontocord Dlbk tomaarsen AdinaY KvrParaskevi eliolio vicgalle kklyman taufiqdp Vlansu riccorl

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Contamination free code evaluations with LiveCodeBench! 🖥

LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅

This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀

Check it out!

Blog: https://huggingface.co/blog/leaderboard-livecodebench
Leaderboard: https://huggingface.co/spaces/livecodebench/leaderboard

Congrats to @StringChaos  @minimario  @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!

loubnabnl osanseviero BrigitteTousi lunarflu Dlbk clem mathiasn1 kingh0730 thomwolf dillfrescott

lunarflu Dlbk clem kingh0730 thomwolf dillfrescott


Is is time for the open-source AI robots revolution 🚀?

With @haixuantao and @Leyo we’ve been playing with a low-cost DJI robot controlled by three local open-source AI models (Whisper, Idefics2, Parler-TTS - all Apache2) and orchestrated by Dora-cs.

Links to find all the hardware/software we used in the demo:
- robot control framework – dora-rs: https://github.com/dora-rs/dora
- speech-to-text model – whisper: https://huggingface.co/openai/whisper-base
- vision-text model – Idefics2: https://huggingface.co/HuggingFaceM4/idefics2-8b-AWQ
-  text-to-speech model – ParlerTTS mini: https://huggingface.co/parler-tts/parler_tts_mini_v0.1
- robot: https://dji.com/robomaster-s1
- code gist: https://gist.github.com/haixuanTao/860e1740245dc2c8dd85b496150a9320
- Larger codebase: https://huggingface.co/datasets/dora-rs/dora-idefics2
- laptop/pc: any with a recent GPU card (our has a RTX 4090)

Enjoy!

SivilTaram KingNish Tonic clefourrier YaTharThShaRma999 victor osanseviero radames BrigitteTousi lunarflu Leyo giux78 clem Violette dodin InferenceIllusionist Abouharga Martins6 jme2791

Tonic clefourrier YaTharThShaRma999 osanseviero radames BrigitteTousi lunarflu Leyo giux78 clem Violette InferenceIllusionist Martins6 carloscardenas KingNish louisbrulenaudet

Tonic YaTharThShaRma999 osanseviero BrigitteTousi lunarflu giux78 clem Violette TommyZQ OmbelineM


🆕 Evaluate your RL agents - who's best at Atari?🏆

The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!

When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨

Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀

https://huggingface.co/spaces/open-rl-leaderboard/leaderboard

qgallouedec osanseviero BrigitteTousi lunarflu clem KvrParaskevi mlabonne


Excited to share that our paper: "AutoAgents: A Framework for Automatic Agent Generation" got accepted to this year's @ IJCAI  🎉🥳

As a young aspiring AI researcher, this one means a lot, as it is the first ever paper I was blessed to contribute in. Thanks to the incredibly brilliant minds I got to work with (Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Börje F. Karlsson, Jie Fu, Yemin Shi ) - you’re heroes of mine 🫡

@karpathy I hope this is worth a mutual follow/response haha, your lessons helped shaped my understanding of this field and they still are. Thank  you 🙏🏼

Demo: https://huggingface.co/spaces/LinkSoul/AutoAgents
Code: https://github.com/Link-AGI/AutoAgents
Paper: https://arxiv.org/abs/2309.17288


We noticed that all the open-source models and datasets from https://huggingface.co/WizardLM in their personal Hugging Face account & in the Microsoft Hugging Face organization (https://huggingface.co/microsoft) have been made private by the author, which will lead some demos to fail (these models were collectively downloaded over a hundred thousand times a month).

This is the explanation that @WizardLM communicated a few hours ago: https://huggingface.co/posts/WizardLM/329547800484476#661e0d17bca1a6038b60503e

We apologize for the inconvenience & are trying to get in touch with the author & Microsoft in order to try to find a good resolution for community members. Let us know if you have any questions!

osanseviero fdaudens ijohn07 pierrci BrigitteTousi lazarustda samusenps clefourrier lunarflu Diz187 guilherme-am m-ric acamilogg88

osanseviero pierrci Mehrdadgpt lunarflu fynnkroeger m-ric


Learn Your Reference Model for Real Good Alignment (2404.09656)

Learn Your Reference Model for Real Good Alignment

https://huggingface.co/papers/2404.09656

The complexity of the alignment problem stems from the fact that existing methods are unstable. Researchers continuously invent various tricks to address this shortcoming. For instance, in the fundamental Reinforcement Learning From Human Feedback (RLHF) technique of Language Model alignment, in addition to reward maximization, the Kullback-Leibler divergence between the trainable policy and the SFT policy is minimized. This addition prevents the model from being overfitted to the Reward Model (RM) and generating texts that are out-of-domain for the RM. The Direct Preference Optimization (DPO) method reformulates the optimization task of RLHF and eliminates the Reward Model while tacitly maintaining the requirement for the policy to be close to the SFT policy. In our paper, we argue that this implicit limitation in the DPO method leads to sub-optimal results. We propose a new method called Trust Region DPO (TR-DPO), which updates the reference policy during training. With such a straightforward update, we demonstrate the effectiveness of TR-DPO against DPO on the Anthropic HH and TLDR datasets. We show that TR-DPO outperforms DPO by up to 19%, measured by automatic evaluation with GPT-4. The new alignment approach that we propose allows us to improve the quality of models across several parameters at once, such as coherence, correctness, level of detail, helpfulness, and harmlessness.


LM Transparency Tool: Interactive Tool for Analyzing Transformer
  Language Models (2404.07004)

Information Flow Routes: Automatically Interpreting Language Models at
  Scale (2403.00824)

🔍 Today's pick in Interpretability & Analysis of LMs: https://huggingface.co/papers/2404.07004 by @igortufanov @mahnerak @javifer @lena-voita

The LLM transparency toolkit is an open source toolkit and visual interface to efficiently identify component circuits in LMs responsible for their predictions, using the Information Flow Routes approach (https://huggingface.co/papers/2403.00824).

The tool enables fine-grained customization, highlighting the importance of individual FFN neurons and attention heads. Moreover, vocabulary projections computed using the logit lens approach are provided to examine intermediate predictions of the residual stream, and tokens promoted by specific component updates.

💻 Code: https://github.com/facebookresearch/llm-transparency-tool

🚀 Demo: https://huggingface.co/spaces/facebook/llm-transparency-tool-demo

🔍 All daily picks: https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-ofc-lms-65ae3339949c5675d25de2f9


FiftyOne Datasets <> Hugging Face Hub Integration!

As of yesterday's release of FiftyOne `0.23.8`, the FiftyOne open source library for dataset curation and visualization is now integrated with the Hugging Face Hub!

You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:

```bash
pip install -U fiftyone
```

```py
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "mnist",
    format="ParquetFilesDataset",
    classification_fields="label",
)
session = fo.launch_app(dataset)
```

You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:

```py
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub("jamarks/VisDrone2019-DET", max_samples=1000)

# Launch the App
session = fo.launch_app(dataset)
```

And tying it all together, you can push your FiftyOne datasets directly to the hub:

```py
import fiftyone.zoo as foz
import fiftyone.utils.huggingface as fouh

dataset = foz.load_zoo_dataset("quickstart")
fouh.push_to_hub(dataset, "my-dataset")
```

Major thanks to @tomaarsen @davanstrien @severo @osanseviero and @julien-c for helping to make this happen!!!

Full documentation and details here: https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub

severo brianmoore davanstrien ehofesmann jguerrero harpreetsahota victor clem merve osanseviero lunarflu julien-c dgural not-lain jamarks shamy777 Maralk

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Demo for IDEFICS-8B demo is out! https://huggingface.co/spaces/HuggingFaceM4/idefics-8b

This checkpoint is not optimized to chat, but rather works very well for various tasks, incl visual question answering and document tasks 💬📑
Chatty one is coming soon!

radames kramp clem Redox3275 osanseviero CristianJD lunarflu VictorSanh


Introducing Super RAGs in Mistral 8x7B-v1 (2404.08940)

Super RAGs in Mistral 8x7B-v1 -
The recent paper on arXiv introduces Super Retrieval-Augmented Generation (Super RAGs), a groundbreaking approach to improve Large Language Models (LLMs) by integrating external knowledge sources. This integration into the Mistral 8x7B v1 LLM has shown notable improvements in accuracy, speed, and user satisfaction.

What are your thoughts on the potential of Super RAGs to transform the future of AI and LLMs?

https://huggingface.co/papers/2404.08940

victor clem merve lunarflu not-lain Jason-Lu

raghav2543 clem lunarflu not-lain Jason-Lu


Just had the former chief public health officer of the Netherlands🇳🇱 review a huggingface AI-doctor I made via a simple orpo-zephyr-8x22b-GPT  and they think it's really good.

https://hf.co/chat/assistant/661d77310e3aea9ae571e43c

pierrci pankaj-munde mbilalkhaan kramp jpodivin m-ric clem mdadnan lunarflu Violette BrigitteTousi jbilcke-hf dillfrescott kellykellz akyarborough andrewgleave surajp bbooth dillen-hub


We release Idefics2-8B, a foundation vision language model with SOTA results for its size on many benchmarks.

For Idefics2, we adopted a simple architecture:
-Images are fed to a vision encoder, then to a modality projection to match the input dimension of the LLM, and finally to a perceiver resampler for efficient pooling.
-Interleaved image-text data are then passed to the LLM.

During the pre-training:
-The modality projection and perceiver resampler weights are newly initialized.
-We start with pre-trained models for the vision encoder and the LLM, and continue the training with LoRA.
-In total, we see 1.5T images!

We pre-train on 3 types of data, all publicly available:
-Interleaved image-text documents: our dataset OBELICS https://huggingface.co/datasets/HuggingFaceM4/OBELICS
-Image caption pairs: only synthetic captions!
-PDF documents: IDL and PDFA

We kept the aspect ratio of the images with the Patch n' Pack strategy, with a resolution of up to 980x980.
At inference, it's also more efficient for lower-resolution images.

For the SFT, we build The Cauldron, a collection of 50 high-quality datasets in the user/assistant format.
It is a ready-to-use dataset for the fine-tuning of any VLM.
https://huggingface.co/datasets/HuggingFaceM4/the_cauldron

Most current models, like LLaVA-NeXT, encode images with an excessive number of tokens, like 2880.
Instead, we put a focus on being efficient at inference by training on a mix of images encoded with 64 tokens, and 320 tokens.
The result is that we perform favorably compared to the best models in our size class, while being efficient at inference.

VictorSanh victor danaaubakirova sbarman25 m-ric clem lunarflu Violette fdaudens sbrandeis

m-ric clem samusenps Sylvestre Molbap NePe lunarflu fynnkroeger Violette

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New open multimodal model in town: Idefics2!

💪 Strong 8B-parameters model: often on par with open 30B counterparts.
🔓Open license: Apache 2.0.
🚀 Strong improvement over Idefics1: +12 points on VQAv2, +30 points on TextVQA while having 10x fewer parameters.
📚 Better data: boosting OCR capabilities with 6TB of documents to transcribe, and improving QA capabilities on charts/figures/diagrams.
🕵♀ Transparent training data: inspect and build upon all the data (10s of TB of data) we trained on.
🔲 More natural image processing: Incorporating strategies to treat images in their native resolution and native aspect ratio.
📸 High-resolution images: image resolutions up to 980 x 980 and integrating strategies that allow to trade computational efficiency for performance.
😎 2 checkpoints: Releasing both base checkpoint and instruction fine-tuned checkpoint. Chat version to come.

Ressources: https://huggingface.co/collections/HuggingFaceM4/idefics2-661d1971b7c50831dd3ce0fe
Blogpost: https://huggingface.co/blog/idefics2

VictorSanh rwightman monsoon-nlp oceansweep pierrci ajibawa-2023 osanseviero Ayushnangia andrewrreed m-ric clem Molbap lunarflu fynnkroeger marcsun13 raincandy-u jlzhou

VictorSanh clem oceansweep pierrci samusenps osanseviero andrewrreed m-ric Molbap lunarflu

VictorSanh clem samusenps andrewrreed m-ric lunarflu


🔥🔥🔥 Introducing WizardLM-2!

📙Release Blog: https://wizardlm.github.io/WizardLM2
✅Model Weights: https://huggingface.co/collections/microsoft/wizardlm-661d403f71e6c8257dbd598a
🐦Twitter: https://twitter.com/WizardLM_AI/status/1779899325868589372

We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.

WizardLM-2 8x22B is our most advanced model, and the best opensource LLM in our internal evaluation on highly complex tasks. WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.

🤗 WizardLM 2 Capacities:

1. MT-Bench (Figure-1)
The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary works such as GPT-4-Trubo and Glaude-3. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.

2. Human Preferences Evaluation (Figure 2)
Through this human preferences evaluation, WizardLM-2's capabilities are very close to the cutting-edge proprietary models such as GPT-4-1106-preview, and significantly ahead of all the other open source models.

🔍Method Overview:
As the natural world's human-generated data becomes increasingly exhausted through LLM training, we believe that: the data carefully created by AI and the model step-by-step supervised by AI will be the sole path towards more powerful AI.

In the past one year, we built a fully AI powered synthetic training system. (As shown in the Figure 3).

WizardLM tangmen victor vaclavkosar s0me-0ne oceansweep Bruno xianbao haipeng1 syslot Lin-Rexter Chaklam JosMax veeragoni CocoSun ChuckMcSneed osanseviero julien-c Ramikan-BR niclaslindqvist radames taher30 jinkazamapro07 m-ric jayjay5230 Jaward latent-variable jameshwade Revs64 JSCPPProgrammer cppowboy ShaneTian jlzhou alesthehuman bardusco-tau mahirzukic2 noobmaster29 Emigmo KvrParaskevi Aressions

haipeng1 Lin-Rexter osanseviero julien-c radames kramp latent-variable djester alesthehuman bardusco-tau mahirzukic2 noobmaster29 c4wrd wucl usholanb bs82 mitra-mir

sparkycollier haipeng1 Lin-Rexter YueJie osanseviero julien-c ragibhasan radames latent-variable sugatoray alesthehuman mahirzukic2 noobmaster29

KyleLyu djester alesthehuman pharmaia noobmaster29 tobitege StudentWen VDBLOI2024

Tenbo fysp latent-variable alesthehuman noobmaster29


Just tested Argilla's new data annotation feature - it's a game changer for AI project quality.

Upload CSVs, work with published datasets, or improve existing ones directly on HuggingFace Hub. Setup took < 2 minutes, no code needed (see example below where I selected a dataset to classify tweets in categories).

Real world impact: Missing in Chicago won a Pulitzer using a similar approach - 200 volunteers labeled police misconduct files to train their model. That's the power of good data annotation.

Three immediate use cases I see:
- Build collaborative training sets with your community (surprisingly underused in AI journalism)
- Turn your website chatbot logs into high-quality fine-tuning data
- Compare generated vs published content (great for SEO headlines)

Works for solo projects or teams up to 100 people. All integrated with HuggingFace Hub for immediate model training.

Interesting to see tools like this making data quality more accessible. Data quality is the hidden driver of AI success that we don't talk about enough.

- Check out the blogpost: https://huggingface.co/blog/argilla-ui-hub
- And the quickstart guide: https://docs.argilla.io/latest/getting_started/quickstart/


Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated
  Parameters by Tencent (2411.02265)

𝗛𝘂𝗻𝘆𝘂𝗮𝗻-𝗟𝗮𝗿𝗴𝗲 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗯𝘆 𝗧𝗲𝗻𝗰𝗲𝗻𝘁: 𝗟𝗮𝗿𝗴𝗲𝘀𝘁 𝗲𝘃𝗲𝗿 𝗼𝗽𝗲𝗻 𝗠𝗼𝗘 𝗟𝗟𝗠, 𝗼𝗻𝗹𝘆 𝟱𝟮𝗕 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀 𝗯𝘂𝘁 𝗯𝗲𝗮𝘁𝘀 𝗟𝗟𝗮𝗠𝗔 𝟯.𝟭-𝟰𝟬𝟱𝗕 𝗼𝗻 𝗺𝗼𝘀𝘁 𝗮𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 🚀

⚡ Mixture of Experts (MoE) architecture: 389 B parameters in total, but only 52B are activated for any input

🧪 Trained on 7T tokens, including 1.5T tokens of synthetic data

🏗 Architecture : Novel "recycle routing" prevents token dropping when experts are overrloaded

📊 Great benchmark results: Surpasses Llama-3-405B-Instruct in most benchmarks although it has 8x fewer active parameters
‣ Impressive perf on MATH: 77.4

🐋 Large context length: up to 256K tokens

🔒 License:
‣ Commercial use allowed, except if your products have >100M monthly active users
‣ No access in the EU

🤗 Model weights available on HF!

Read the full paper here 👉 https://huggingface.co/papers/2411.02265


🎓 Introducing Kompy.info Uzbek Educational Dataset - https://huggingface.co/datasets/nyuuzyou/kompy

Dataset highlights:
- 584,648 pages of educational content extracted from kompy.info, a comprehensive educational resource website
- Content exclusively in Uzbek language, focusing on technical and scientific topics
- Each entry contains: URL, page title, and extracted main text content
- Data extracted using trafilatura HTML extraction tool
- Covers a wide range of academic and educational materials
- Released to the public domain under Creative Commons Zero (CC0) license

The dataset presents a valuable resource for natural language processing tasks in the Uzbek language, particularly in educational and technical domains. It can be used for text classification, topic modeling, and content analysis of educational materials. The large-scale collection of Uzbek-language academic content makes it especially useful for developing educational technology applications and studying pedagogical approaches in Uzbek-language instruction. The dataset's monolingual nature provides a focused corpus for understanding technical and scientific terminology in Uzbek educational contexts.


OS-ATLAS: A Foundation Action Model for Generalist GUI Agents (2410.23218)

🚀 Excited to introduce a new member of the OS-Copilot family: OS-Atlas - an open-sourced foundational action model for GUI agents

📘 Paper:  https://huggingface.co/papers/2410.23218
🔗 Website: https://osatlas.github.io

😇 TL;DR: OS-Atlas offers:
1. State-of-the-Art GUI Grounding: Helps GUI agents accurately locate GUI elements.
2. Strong OOD Performance and Cross-platform Compatibility: Excels in out-of-domain agentic tasks across MacOS, Windows, Linux, Android, and Web.
3. Complete Infrastructure for GUI Data Synthesis:
You can easily build your own OS agent upon it!


🚀 New feature of the Comparator of the 🤗 Open LLM Leaderboard: now compare models with their base versions & derivatives (finetunes, adapters, etc.). Perfect for tracking how adjustments affect performance & seeing innovations in action. Dive deeper into the leaderboard!

🛠 Here's how to use it:
1. Select your model from the leaderboard.
2. Load its model tree.
3. Choose any base & derived models (adapters, finetunes, merges, quantizations) for comparison.
4. Press Load.
See side-by-side performance metrics instantly!

Ready to dive in? 🏆 Try the 🤗 Open LLM Leaderboard Comparator now! See how models stack up against their base versions and derivatives to understand fine-tuning and other adjustments. Easier model analysis for better insights! Check it out here: https://huggingface.co/spaces/open-llm-leaderboard/comparator 🌐


https://huggingface.co/Alfitaria/Q25-1.5B-VeoLu
Q2.5-1.5-VeoLu is a 1.5 billion parameter General Purpose Creative model trained on Qwen2.5-1.5B-Instruct. Intended mostly as an educational process for myself, Veo Lu nevertheless manages to be usable most of the time, while also being light enough to potentially run on a smartphone.

John6666 bannerlord djuna AuriAetherwiing EloyOn Rsln


Hi HugginfgFacers!🤗

If you're into biomedical sciences, you will know the pain that, sometimes, searching PubMed can be🙇♀

For these purposes, I built a bot that scrapes PubMed for you, starting from the exact title of a publication or key word search - all beautifully rendered through Gradio✅

Find it here: https://huggingface.co/spaces/as-cle-bert/BioMedicalPapersBot

And here's the GitHub repository🐱: https://github.com/AstraBert/BioMedicalPapersBot

It's also available as a Docker image!🐳

```
docker pull ghcr.io/astrabert/biomedicalpapersbot:main
```

Best of luck with your research!

PS: in the very near future some AI summarization features will be included!


Exciting Research Alert: Revolutionizing Dense Passage Retrieval with Entailment Tuning!

The good folks at HKUST have developed a novel approach that significantly improves information retrieval by leveraging natural language inference.

The entailment tuning approach consists of several key steps to enhance dense passage retrieval performance.

Data Preparation
- Convert questions into existence claims using rule-based transformations.
- Combine retrieval data with NLI data from SNLI and MNLI datasets.
- Unify the format of both data types using a consistent prompting framework.

Entailment Tuning Process
- Initialize the model using pre-trained language models like BERT or RoBERTa.
- Apply aggressive masking (β=0.8) specifically to the hypothesis components while preserving premise information.
- Train the model to predict the masked hypothesis tokens from the premise content.
- Run the training for 10 epochs using 8 GPUs, taking approximately 1.5-3.5 hours.

Training Arguments for Entailment Tuning (Yes! They Shared Them)
- Use a learning rate of 2e-5 with 100 warmup steps.
- Set batch size to 128.
- Apply weight decay of 0.01.
- Utilize the Adam optimizer with beta values (0.9, 0.999).
- Maintain maximum gradient norm at 1.0.

Deployment
- Index passages using FAISS for efficient retrieval.
- Shard vector store across multiple GPUs.
- Enable sub-millisecond retrieval of the top-100 passages per query.

Integration with Existing Systems
- Insert entailment tuning between pre-training and fine-tuning stages.
- Maintain compatibility with current dense retrieval methods.
- Preserve existing contrastive learning approaches during fine-tuning.

Simple, intuitive, and effective!

This advancement significantly improves the quality of retrieved passages for question-answering systems and retrieval-augmented generation tasks.

John6666 Kaba louisbrulenaudet tiendung KarthikaRajagopal AtAndDev Bruno


Smol TTS models are here! OuteTTS-0.1-350M - Zero shot voice cloning, built on LLaMa architecture, CC-BY license! 🔥

> Pure language modeling approach to TTS
> Zero-shot voice cloning
> LLaMa architecture w/ Audio tokens (WavTokenizer)
> BONUS: Works on-device w/ llama.cpp ⚡

Three-step approach to TTS:

> Audio tokenization using WavTokenizer (75 tok per second)
> CTC forced alignment for  word-to-audio token mapping
> Structured prompt creation w/ transcription, duration, audio tokens

The model is extremely impressive for 350M parameters! Kudos to the
OuteAI team on such a brilliant feat - I'd love to see this be applied on larger data and smarter backbones like SmolLM 🤗

Check out the models here: https://huggingface.co/collections/OuteAI/outetts-6728aa71a53a076e4ba4817c


Build datasets for AI on the Hugging Face Hub—10x easier than ever!

Today, I'm excited to share our biggest feature since we joined Hugging Face.

Here’s how it works:

1. Pick a dataset—upload your own or choose from 240K open datasets.
2. Paste the Hub dataset ID into Argilla and set up your labeling interface.
3. Share the URL with your team or the whole community!

And the best part? It’s:
- No code – no Python needed
- Integrated – all within the Hub
- Scalable – from solo labeling to 100s of contributors

I am incredibly proud of the team for shipping this after weeks of work and many quick iterations.

Let's make this sentence obsolete: "Everyone wants to do the model work, not the data work."

Read, share, and like the HF blog post:
https://huggingface.co/blog/argilla-ui-hub


😀🤓😎 New Space - OCEAN-AI (App, co-authored by @DmitryRyumin) 😎😉😤
🚀 Title: OCEAN-AI is an open-source app for Big Five personality traits assessment and HR-processes automatization.

🤗 Demo: https://huggingface.co/spaces/ElenaRyumina/OCEANAI

👥 Authors: @ElenaRyumina, @DmitryRyumin, and Alexey Karpov

📝 Description: OCEAN-AI consists of a set of modules for intellectual analysis of human behavior based on multimodal data for automatic personality traits (PT) assessment. The app evaluates five PT: Openness to experience, Conscientiousness, Extraversion, Agreeableness, Non-Neuroticism.

The App solves practical tasks:
- Ranking of potential candidates by professional responsibilities.
- Forming effective work teams.
- Predicting consumer preferences for industrial goods.

🔍 Keywords: #OCEANAI #BigFive #PersonalityTraits #PersonalityAnalysis #MultimodalData #Transformers #FirstImpressionsV2 #DeepLearning #Innovation #BehaviorAnalysis #AffectiveRecognition #TeamFormation #ConsumerPreferences #CandidateRanking

DmitryRyumin ElenaRyumina Mihaj nilacadenas arturoxdev MiSTe-R Ramikan-BR kramp

DmitryRyumin ElenaRyumina Mihaj MiSTe-R beno-rohman


𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 💡

𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 💡

𝐏𝐚𝐩𝐞𝐫 𝐑𝐞𝐯𝐢𝐞𝐰: 𝐑𝐡𝐨-𝟏 - 𝐃𝐨 𝐧𝐨𝐭 𝐮𝐬𝐞 𝐚𝐥𝐥 𝐭𝐨𝐤𝐞𝐧𝐬 𝐞𝐪𝐮𝐚𝐥𝐥𝐲 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠! ⚖⛔

A new paper topping Daily papers questions a hidden assumption in LLM training:

🤔 𝙎𝙝𝙤𝙪𝙡𝙙 𝙬𝙚 𝙧𝙚𝙖𝙡𝙡𝙮 𝙪𝙨𝙚 𝙖𝙡𝙡 𝙩𝙤𝙠𝙚𝙣𝙨 𝙚𝙦𝙪𝙖𝙡𝙡𝙮 𝙞𝙣 𝙤𝙪𝙧 𝙇𝙇𝙈'𝙨 𝙩𝙧𝙖𝙞𝙣𝙞𝙣𝙜 ?

Some tokens are more relevant than others, and some are mostly noise (just look up the history of 𝘚𝘰𝘭𝘪𝘥𝘎𝘰𝘭𝘥𝘔𝘢𝘨𝘪𝘬𝘢𝘳𝘱).

So this paper introduces 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴, which is actually really simple:
➡ A specific metric measures the relevance of each token. Then during training, only the top k% tokens for this relevance metric count in the loss calculation.

Authors test this method by training models on the difficult MATH dataset (only competition mathematics problems).

➡ Their technique seems like a new must-do in LLM training: Training is much faster and reaches an impressive performance!

𝐑𝐞𝐬𝐮𝐥𝐭𝐬:
◆ ⏱ Training is x5 to x10 faster to reach equivalent performance compared to standard language modeling.
◆ 💪 Their 1B model achieves close to GPT4 Chain-of-Thought performance on MATH!
◆ 🚀 Their 7B model match performance of the state-of-the-art DeepSeek for the same size, while trained on only 3% of tokens

𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 💡
◆ Datasets used for pre-training, even after pre-filtering, still contain a large proportion of noisy tokens 😖
◆ Authors show that when you reduce loss on noisy tokens, you actually reduce accuracy (Figure 7). So Selective Language Modeling seems fundamental! ✅

Find great reads in @akhaliq 's Daily Papers 👉 https://huggingface.co/papers
Paper added to my collection 👉 https://huggingface.co/collections/m-ric/spinning-up-in-llms-659e698f9dd5a71bd3f579a7


We just released a new MoE model (meraGPT/mera-mix-4x7B) that is half as large as Mixtral-8x7B while still been competitive with it across different benchmarks. mera-mix-4x7B achieves 76.37 on the open LLM eval.

You can check mera-mix-4x7B out on HF here - https://huggingface.co/meraGPT/mera-mix-4x7B


ORPO: Monolithic Preference Optimization without Reference Model (2403.07691)

Could more DPO-style preference data be crucial for enhancing open LLMs across different languages?

Leveraging a 7k preference dataset Argilla (@alvarobartt), Hugging Face (@lewtun) and Kaist AI (@JW17 & @nlee-208)
utilized Kaist AI's recently introduced ORPO technique https://huggingface.co/papers/2403.07691 with the latest MistralAI MOE model to create a very high-performing open LLM: https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1

Since ORPO doesn't require a separate SFT stage, all that is needed is a strong base model + high-quality DPO-style datasets.

Currently, there is a significant lack of non-English DPO datasets. Filling this gap could significantly improve open LLMs in various languages.

You can get an overview of the current state of DPO datasets across different languages here: https://huggingface.co/spaces/DIBT/preference_data_by_language

JW17 alvarobartt osanseviero nlee-208 NMontanaBrown Violette Heng666 raincandy-u DataSoul


On Agentic AI: Autonomy Is All You Need!

There is this remarkable beauty in witnessing an AI system autonomously completes complex tasks with a level of brilliance that supersedes our reasoning capabilities/expectations - It is the holy grail of creation.

Giving your AI agents autonomy is analogous to us having "free will" and everything else thereafter is a cascade of possibilities and potentials waiting to unfold.

As brilliantly said by @AndrewNg  "It’s a beautiful thing when you see an agent autonomously decide to do things in ways that you had not anticipated, and succeed as a result!"

Autonomy in Agentic AI
 - augments agents' decision-making capabilities.
 - enables adaptation to diverse environments.
 - facilitates real-time learning and improvement.
 - fosters dynamic multi-agent collaboration.
 - promotes efficient and independent task execution.
 - drives innovation in dynamic and unpredictable
    scenarios.

This what the AutoAgents paper conveyed - A fully autonomous agentic framework that basically gives agents the free will for authentic/compelling creativity.

With just three dynamically predefined agents [ Planner, Agent Observer and Plan Observer ] acting collaboratively they are able to generically create autonomous agents:

```
Agent (A) =  {
     Prompt (P) - defines agent's identity fully ,
     Description (D) - adds specific role identity,
     Toolset (T) - equips the agent with tools,
     Suggestion (S) - offers task execution tips
 }
```

Demo: https://huggingface.co/spaces/LinkSoul/AutoAgents
Code: https://github.com/Link-AGI/AutoAgents
Paper: https://arxiv.org/abs/2309.17288


Fun dataset added last week by  @esind from https://huggingface.co/Anthropic to compare persuasiveness between AI and human outputs:
https://huggingface.co/datasets/Anthropic/persuasion

Jaward kgourgou osanseviero not-lain KvrParaskevi InferenceIllusionist nogully


We're introducing experimental support for `device_map` in Diffusers 🤗

If you have multiple GPUs you want to use to distribute the pipeline models, you can do so. Additionally, this becomes more useful when you have multiple low-VRAM GPUs.

Documentation:
https://huggingface.co/docs/diffusers/main/en/training/distributed_inference#device-placement

🚨 Currently, only "balanced" device mapping strategy is supported.

ajibawa-2023 clem osanseviero Bruno Paolo16 alvarobartt ingTikna


Watch the full tutorial here : https://youtu.be/0t5l6CP9eBg

The tutorial is over 2 hours literally with manually fixed captions and perfect video chapters.

Most Awaited Full Fine Tuning (with DreamBooth effect) Tutorial Generated Images - Full Workflow Shared In The Comments - NO Paywall This Time - Explained OneTrainer - Cumulative Experience of 16 Months Stable Diffusion

In this tutorial, I am going to show you how to install OneTrainer from scratch on your computer and do a Stable Diffusion SDXL (Full Fine-Tuning 10.3 GB VRAM) and SD 1.5 (Full Fine-Tuning 7GB VRAM) based models training on your computer and also do the same training on a very cheap cloud machine from MassedCompute if you don't have such computer.

Tutorial Readme File ⤵
https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Tutorials/OneTrainer-Master-SD-1_5-SDXL-Windows-Cloud-Tutorial.md

Register Massed Compute From Below Link (could be necessary to use our Special Coupon for A6000 GPU for 31 cents per hour) ⤵
https://bit.ly/Furkan-Gözükara

Coupon Code for A6000 GPU is : SECourses

0:00 Introduction to Zero-to-Hero Stable Diffusion (SD) Fine-Tuning with OneTrainer (OT) tutorial
3:54 Intro to instructions GitHub readme
4:32 How to register Massed Compute (MC) and start virtual machine (VM)
5:48 Which template to choose on MC
6:36 How to apply MC coupon
8:41 How to install OT on your computer to train
9:15 How to verify your Python, Git, FFmpeg and Git installation
12:00 How to install ThinLinc and start using your MC VM
12:26 How to setup folder synchronization and file sharing between your computer and MC VM
13:56 End existing session in ThinClient
14:06 How to turn off MC VM
14:24 How to connect and start using VM
14:41 When use end existing session
16:38 How to download very best OT preset training configuration for SD 1.5 & SDXL models
18:00 How to load configuration preset
18:38 Full explanation of OT configuration and best hyper parameters for SDXL
.
.
.

rafalwiatrowski mehdicode123 samusenps kadirnar HeavyLvy clem Aakashk Matix200 AlexSonar levulinh


Here's a utility component for integrating your Gradio app with Hugging Face. This custom component enables you to search for models, spaces, datasets, and users.

`pip install gradio_huggingfacehub_search`

You can see it in action here. https://huggingface.co/spaces/arcee-ai/mergekit-config-generator

And learn how to use it here https://huggingface.co/spaces/radames/gradio_huggingfacehub_search

samusenps osanseviero srisree clem Gevennou multimodalart


"How expensive is it actually to teach a #LanguageModel German through #finetuning 💰💰💰? We get asked this quite often.

There is no one-size-fits-all answer to this question, as among other factors:
⏹ each fine-tuning is different,
⏹ the hardware used can be a major cost driver,
⏹ the amount and type of training data can extend the process,
⏹ and the skills to be trained can increase the difficulty of fine-tuning.

However, we have broken down the costs incurred for our latest fine-tune ( https://huggingface.co/VAGOsolutions/SauerkrautLM-Qwen-32b)

Base model: https://huggingface.co/Qwen/Qwen1.5-32B
Fine-Tuning Goal: Train German language
Training dataset size: 160,000 SFT data / 110,000 DPO data
Training duration: 72.5 hours (2 epochs SFT / 1 epoch DPO)
GPU: 2x A100 SXM
New model: https://huggingface.co/VAGOsolutions/SauerkrautLM-Qwen-32b

Total cost: 312 euros 💶

These are quite reasonable training costs considering the model now speaks passable German (previously very broken). Depending on the use case and process requirements, this can even be a real alternative to the costly continuous pre-training of foreign language models.

jed-tiotuico osanseviero clem victor fpreiss philipp-zettl joennlae Kearm


Yesterday, xAI announced Grok-1.5 Vision - https://x.ai/blog/grok-1.5v. But more importantly, they also released a new VLM benchmark dataset - RealWorldQA. The only problem was that they released it as a ZIP archive. I fixed that! Now you can use it in your evaluations as a regular HF dataset: https://huggingface.co/datasets/visheratin/realworldqa

musfiqdehan fynnkroeger osanseviero Bruno ajibawa-2023 VictorSanh clem not-lain ShihHong alielfilali01 dinhanhx jyoung105


Introducing MusicGen Web: AI-powered music generation directly in your browser, built with 🤗 Transformers.js! 🎵

Everything runs 100% locally, meaning there are no calls to an API! 🤯 Since it's served as a static HF space, it costs $0 to host and run! 🔥

We also added the ability to share your generated music to the discussion tab, so give it a try! 👇
https://huggingface.co/spaces/Xenova/musicgen-web

DmitryRyumin adamelliotfields samusenps andrewmilesmurphy1992 Lyte rocca Sword666 ajibawa-2023 ashwinlimaye Retr0REG cdosoftei Cksampa osanseviero clem AlekseiPravdin victor ylacombe alt2023 DataSoul b4s36t4 Noomam nekomeowww

luoweiqiang9527 cdosoftei raincandy-u Cksampa osanseviero clem victor Kce niwram69


Spent the morning designing a super important product: SMALL LANGUAGE MODEL BABY ONESIE https://www.shop.hackernoon.com/all-merch/p/small-language-model-baby-onesie

samusenps visheratin travisking Violette


Hi everyone! I lead a team of students to create a game board community of LLMs that interact to help one of the LLMs create a presentation on an idea.
https://github.com/GooseCube/WWPD.ai
This was based on Standfords unusable initial version:

"A group of researchers at Stanford University and Google have created a miniature RPG-style virtual world similar to The Sims, where 25 characters, controlled by ChatGPT and custom code, live out their lives independently with a high degree of realistic behavior. They wrote about their experiment in a preprint academic paper released on Friday."

We've come so far on this open source project, however the class that developed it is now finished. The UI needs a work over, but the giant success is that this is a LIVE version of the Stanfords version. We're super close to finishing something that is super cool.

Check it out at: https://wwpd-ai.vercel.app/

I'm hoping that we can get some help from the community to help bring it to a place where the LLM gives a ted talk on whatever a topic that the user chooses.

Username and pass:
hug@huggingface.com
12345678

raincandy-u DRRRRDFFD osanseviero oceansweep clem abidlabs


Hello everyone, looking to share and learn more most of my experience is security so I will be doing more learning in order to secure AI

adaboost19 raincandy-u nachoyawn sbrandeis