title stringlengths 4 172 | link stringlengths 27 86 | article stringlengths 4 40.1k |
|---|---|---|
FiftyOne Computer Vision Datasets Come to the Hugging Face Hub | https://hf.co/blog/jamarks/fiftyone-datasets-come-to-hf-hub | 📚Resources |
⚗️ 🔥 Building High-Quality Datasets with distilabel and Prometheus 2 | https://hf.co/blog/burtenshaw/distilabel-prometheus-2 | Resource |
Expert-Level Tutorials on Stable Diffusion & SDXL: Master Advanced Techniques and Strategies | https://hf.co/blog/MonsterMMORPG/expert-level-tutorials-on-stable-diffusion-gen-ai | Tutorial Videos |
Wikipedia's Treasure Trove: Advancing Machine Learning with Diverse Data | https://hf.co/blog/frimelle/wikipedias-treasure-trove-ml-data | More Wikimedia data on Hugging Face - How? |
Introducing Tenzin 1.0: | https://hf.co/blog/Tar9897/my-first-model |
<p>
Tenzin: A Technical Exploration into Achieving Artificial General Intelligence
Artificial General Intelligence (AGI) represents the zenith of artificial intelligence research—a machine capable of understanding, learning, and applying knowledge across a wide array of tasks at a level comparable to human intelligen... |
Mergoo: Efficiently Build Your Own MoE LLM | https://hf.co/blog/alirezamsh/mergoo | Learn More |
Fine-tuning LLMs with Singular Value Decomposition | https://hf.co/blog/fractalego/svd-training | References |
Introducing UNA-ThePitbull Series | https://hf.co/blog/fblgit/una-thepitbull | Bonus |
Indexify: Bringing HuggingFace Models to Real-Time Pipelines for Production Applications | https://hf.co/blog/rishiraj/announcing-indexify | Start Using Indexify |
HelpingAI 9B: Cutting Edge Emotionally Intelligent AI | https://hf.co/blog/KingNish/helpingai-9b | Conclusion: |
How to directly access 150k+ Hugging Face Datasets with DuckDB and query using GPT-4o | https://hf.co/blog/chilijung/access-150k-hugging-face-datasets-with-duckdb | Start asking questions |
FaceChain-FACT: Open-source 10-second portrait generation, reusing massive LoRa styles, a base-model-friendly portrait application. | https://hf.co/blog/haoyufirst/facechain-fact | Expansion & Co-construction |
Revolutionizing Human-Computer Interaction: The Emotional Intelligence and Ethical Impact of HelpingAI-9B | https://hf.co/blog/Abhaykoul/helpingai | 7.3. Closing Remarks |
So WTF is an Audio Embedding Model? | https://hf.co/blog/cappuch/audio-embedding-wtf | What Can Audio Embedding Model Be Used For |
Orchestration of Experts: The First-Principle Multi-Model System | https://hf.co/blog/alirezamsh/leeroo-multi-model-system | Citation |
How to Fine-Tune Custom Embedding Models Using AutoTrain | https://hf.co/blog/abhishek/finetune-custom-embeddings-autotrain | Summary |
GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing | https://hf.co/blog/NicoNico/green-bit-llm | Links: |
Not Legal Advice on AI Training Data in Japan | https://hf.co/blog/leonardlin/ai-training-data-in-japan | Terms of Service and Synthetic Data |
Sales Forecasting with Image Regression | https://hf.co/blog/tonyassi/image-regression | About Me |
AI has a problem with objectifying women | https://hf.co/blog/sasha/objectifying-women-in-ai |
<p>
May 24, 2024</p>
<p>Last week, OpenAI did a much-publicized <a href="https://www.wired.com/story/openai-gpt-4o-model-gives-chatgpt-a-snappy-flirty-upgrade/" rel="nofollow">demo</a> of their new chatbot, ChatGPT 4.0, now endowed with a speech interface. One of the voices used during their demo, nickname Sky, insta... |
Training MoE on AWS Trainium | https://hf.co/blog/ynakashima/training-moe-on-aws-trainium | Conclusion |
Let's talk about LLM evaluation | https://hf.co/blog/clefourrier/llm-evaluation | Acknowledgements |
Synthetic dataset generation techniques: generating custom sentence similarity data | https://hf.co/blog/davanstrien/synthetic-similarity-datasets | Conclusion |
Journey With Me Into The Mind of Large Language Models: Interesting Findings in AnthropicAI's Scaling Monosemanticity paper. | https://hf.co/blog/Jaward/journey-with-me-into-the-mind-of-llms |
<p>
One of the many unknowns with LLMs is the why behind the responses they give - it's unclear why certain responses are chosen over others. Which shows how little we know of what's happening inside these models. </p>
<p>To have a deeper sense of this, they tried Sparse Dictionary Learning on a larger model (Claude ... |
Enjoy the Power of Phi-3 with ONNX Runtime on your device | https://hf.co/blog/Emma-N/enjoy-the-power-of-phi-3-with-onnx-runtime | Enjoy Phi-3 on your device |
What is going on with AlphaFold3? | https://hf.co/blog/as-cle-bert/what-is-going-on-with-alphafold3 | References |
Decoding GPT-4'o': In-Depth Exploration of Its Mechanisms and Creating Similar AI. | https://hf.co/blog/KingNish/decoding-gpt-4o | Making of Similar AI |
Sora | https://hf.co/blog/Kvikontent/sora | Ready to try it out? |
Explaining the SDXL latent space | https://hf.co/blog/TimothyAlexisVass/explaining-the-sdxl-latent-space | Back to top |
Diffusion Models | https://hf.co/blog/Esmail-AGumaan/diffusion-models | Citation: |
Evaling llm-jp-eval (evals are hard) | https://hf.co/blog/leonardlin/llm-jp-eval-eval |
<p>
With training of <a href="https://wandb.ai/augmxnt/shisa-v2/" rel="nofollow">shisa-v2</a> starting in earnest, I've been digging a bit more into <a href="https://github.com/llm-jp/llm-jp-eval" rel="nofollow">llm-jp-eval</a>, which I used as a quick and simple benchmark to help to track shisa-v1 (especially the ba... |
2024-04-22 - Hub Incident Post Mortem | https://hf.co/blog/mcpotato/hub-incident-post-mortem-20240422 | Timeline |
Hugging Face + Google Visual Blocks | https://hf.co/blog/radames/hugging-face-google-visual-blocks | Acknowledgements |
Multimodal Augmentation for Documents: Recovering “Comprehension” in “Reading and Comprehension” task | https://hf.co/blog/danaaubakirova/doc-augmentation | References |
Synthetic dataset generation techniques: Self-Instruct | https://hf.co/blog/davanstrien/self-instruct | Using Self Instruct |
Glaze and the Effectiveness of Anti-AI Methods for Diffusion Models | https://hf.co/blog/parsee-mizuhashi/glaze-and-anti-ai-methods | Conclusion |
RFDiffusion Potentials | https://hf.co/blog/AmelieSchreiber/rfdiffusion-potentials | Example 3: Combining <code>substrate_contacts</code>, <code>monomer_ROG</code>, and <code>monomer_contacts</code> for motif scaffolding |
Exploration of Job Application Automation with Data Scraping | https://hf.co/blog/herooooooooo/automation-job-applications-with-python-and-ollama | Conclusion |
Everything About Long Context Fine-tuning | https://hf.co/blog/wenbopan/long-context-fine-tuning | What's Next |
Advancing Open-source Large Language Models in the Medical & Healthcare Domain | https://hf.co/blog/aaditya/openbiollm | Detailed Medical Subjectwise accuracy |
Energy Star Ratings for AI Models | https://hf.co/blog/sasha/energy-star-ai-proposal | Future Work |
Train Custom Models on Hugging Face Spaces with AutoTrain SpaceRunner | https://hf.co/blog/abhishek/autotrain-spacerunner |
<p>
Did you know you could train your custom models on Hugging Face Spaces!!!? Yes, its possible and super-easy to do with AutoTrain SpaceRunner 💥 All you need is a Hugging Face account (which you probably have already) and a payment method attached to your account (in case you want to use GPUs, CPU training is free... |
makeMoE: Implement a Sparse Mixture of Experts Language Model from Scratch | https://hf.co/blog/AviSoori1x/makemoe-from-scratch | Putting it all together |
Can we create pedagogically valuable multi-turn synthetic datasets from Cosmopedia? | https://hf.co/blog/davanstrien/cosmochat | TODO |
Evalverse: Revolutionizing Large Language Model Evaluation with a Unified, User-Friendly Framework | https://hf.co/blog/Yescia/evalverse-llm-evaluation-opensource | Citation |
🧑⚖️ "Replacing Judges with Juries" using distilabel | https://hf.co/blog/alvarobartt/replacing-judges-with-juries-distilabel | References |
Fish Speech V1 - New Multilingual Open Source TTS Model | https://hf.co/blog/lengyue233/fish-speech-1 | Next Steps |
Google Search with LLM | https://hf.co/blog/nand-tmp/google-search-with-llm | How to use RAG method to access the entire internet with ML |
Token Merging for fast LLM inference : Background and first trials with Mistral | https://hf.co/blog/samchain/token-merging-fast-inference | Links |
⚗️ 🧑🏼🌾 Let's grow some Domain Specific Datasets together | https://hf.co/blog/burtenshaw/domain-specific-datasets | <strong>5. Review and share the dataset</strong> |
Expanding Model Context and Creating Chat Models with a Single Click | https://hf.co/blog/maywell/llm-feature-transfer | Links |
Estimating Memory Consumption of LLMs for Inference and Fine-Tuning for Cohere Command-R+ | https://hf.co/blog/Andyrasika/memory-consumption-estimation | Conclusion |
Post-OCR-Correction: 1 billion words dataset of automated OCR correction by LLM | https://hf.co/blog/Pclanglais/post-ocr-correction | Current results and use cases |
Can We Train Chat Models with Raw Data? | https://hf.co/blog/maywell/layer-aware-1 | This simple experiment was designed and conducted based on empirical intuition rather than theoretical grounds. |
RealWorldQA, What's New? | https://hf.co/blog/KennyUTC/realworldqa | Takeaway |
How to Finetune phi-3 on MacBook Pro | https://hf.co/blog/abhishek/phi3-finetune-macbook |
<p>
In this blog, i'll show you how you can train/finetune the latest phi-3 model from Microsoft on your macbook pro! You'll need an M1 or M2 mac to do this. We will be using AutoTrain Advanced!</p>
<p>To install AutoTrain Advanced, you can do:</p>
<pre><code class="language-bash">$ pip install autotrain-advanced
</c... |
Fine Tuning a LLM Using Kubernetes with Intel® Xeon® Scalable Processors | https://hf.co/blog/dmsuehir/llama2-fine-tuning-k8s | Citations |
LLM Comparison/Test: Llama 3 Instruct 70B + 8B HF/GGUF/EXL2 (20 versions tested and compared!) | https://hf.co/blog/wolfram/llm-comparison-test-llama-3 | TL;DR: Observations & Conclusions |
Outpainting III - Inpaint Model | https://hf.co/blog/OzzyGT/outpainting-inpaint-model | 4.- Final touch-ups |
Outpainting II - Differential Diffusion | https://hf.co/blog/OzzyGT/outpainting-differential-diffusion |
<p>
This is the third guide about outpainting, if you want to read about the other methods here they are:</p>
<ul>
<li><a href="https://huggingface.co/blog/OzzyGT/outpainting-controlnet">Outpainting I - Controlnet version</a></li>
<li><a href="https://huggingface.co/blog/OzzyGT/outpainting-inpaint-model">Outpainting ... |
Outpainting I - Controlnet version | https://hf.co/blog/OzzyGT/outpainting-controlnet | 6.- Outpaint tip with IP Adapter |
Exploring Emotionally Intelligent AI with HelpingAI | https://hf.co/blog/Abhaykoul/emotionally-intelligent-ai | 8.1. Embracing the Era of Emotionally Intelligent AI |
Fine-tune Llama 3 with ORPO | https://hf.co/blog/mlabonne/orpo-llama-3 | References |
Starting Tiny with Protein LLaMA | https://hf.co/blog/monsoon-nlp/greenbeing-and-protein-models | Limitations and Safety Notes |
Mixture of Depth is Vibe | https://hf.co/blog/joey00072/mixture-of-depth-is-vibe | Few Gotchas |
Custom architectures with HuggingFace 🤗 | https://hf.co/blog/not-lain/custom-architectures-with-huggingface | push to hub 🤗 |
Run the strongest open-source LLM model: Llama3 70B with just a single 4GB GPU! | https://hf.co/blog/lyogavin/llama3-airllm | Does Llama3’s success herald the rise of open-source models?? |
On Coding Your First Attention | https://hf.co/blog/Jaward/coding-your-first-attention |
<p>
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.</p>
<p><a href="https://cdn-uploads.huggingface.co/... |
SVGDreamer: Text Guided Vector Graphics Generation with Diffusion Model | https://hf.co/blog/xingxm/svgdreamer | References |
Releasing Youtube-Commons: a massive open corpus for conversational and multimodal data | https://hf.co/blog/Pclanglais/youtube-commons |
<p>
We announce today the release of <a href="https://huggingface.co/datasets/PleIAs/YouTube-Commons">Youtube-Commons</a> on HuggingFace:</p>
<ul>
<li>Youtube-Commons is the largest corpus of video to date under an entirely free license.</li>
<li>Youtube-Commons comprises 2 million videos in CC-By with documented pro... |
Design choices for Vision Language Models in 2024 | https://hf.co/blog/gigant/vlm-design | Where are Vision-Language Models headed? |
It's raining diffusion personalization techniques☔️🎭🖼️ | https://hf.co/blog/linoyts/zero-shot-personalization |
<p>
Recently, generating high quality portraits from refrence photos was made possible with as little as a single reference image & without any optimization⚡️</p>
<p><a href="https://cdn-uploads.huggingface.co/production/uploads/638f308fc4444c6ca870b60a/U4Jxr5L5htNaxv02CRiNR.png" rel="nofollow"><img alt="image/pn... |
History of State Space Models (SSM) in 2022 | https://hf.co/blog/lbourdois/ssm-2022 | <strong>Citation</strong> |
What Historical AI Breakthroughs Have Been Unappreciated by The Mainsteam Media? | https://hf.co/blog/Smooke/ai-breakthroughs-unappreciated-by-mainstream-media |
<p>
<i>Recently had the chance to interview <a href="https://twitter.com/jbrowder1" rel="nofollow">Joshua Browder</a>, the Founder/CEO of <a href="https://donotpay.com" rel="nofollow">DoNotPay</a>. <a href="https://hackernoon.com/multimodal-is-the-most-unappreciated-ai-breakthrough-says-donotpayceo-joshua-browder" rel... |
Analysis on evaluating 7 bilions italian LLMs | https://hf.co/blog/giux78/analysis-on-ita-llm | Conclusion |
DS-MoE: Making MoE Models More Efficient and Less Memory-Intensive | https://hf.co/blog/bpan/ds-moe |
<p>
<em>Estimated reading time: 4 minutes</em> </p>
<p>Mixture-of-Experts (MoE) language models are known for their ability to reduce computing needs by 2 to 4 times compared to traditional dense models, without sacrificing performance. This makes them especially useful in situations where computing resources are limi... |
RAG Empowerment: Cohere C4AI Command-R and Transformers Unveiled | https://hf.co/blog/Andyrasika/command-r-transformer | Conclusion |
🐦 The IBIS Challenge | https://hf.co/blog/nikgr/the-ibis-challenge |
<p>
Join <strong>the IBIS Challenge</strong>: an open competition in <strong>I</strong>nferring and predicting transcription factor <strong>Bi</strong>nding <strong>S</strong>pecificities.</p>
<p>Deciphering human gene regulation is a cornerstone of <em>modern molecular biology and biomedicine</em>. On the regulatory... |
The LASER technique: Evaluating SVD compression | https://hf.co/blog/fractalego/mistral-laser-svd | Citation |
Open Source All About Data Processing, Dataverse | https://hf.co/blog/EujeongChoi/dataverse-opensource-for-data-processing | 4. Future Work and Contribution Points |
Many-shot jailbreaking | https://hf.co/blog/vladbogo/many-shot-jailbreaking | Conclusion |
Aurora-M: The First Open Source Biden-Harris Executive Order Red teamed Multilingual Language Model | https://hf.co/blog/mayank-mishra/aurora | Conclusion |
Gecko: Versatile Text Embeddings Distilled from Large Language Models | https://hf.co/blog/vladbogo/gecko | Conclusion |
Finetune Mixtral 8x7B with AutoTrain | https://hf.co/blog/abhishek/autotrain-mixtral-dgx-cloud-local |
<p>
In this blog, I'll show you how you can fine-tune Mixtral 8x7B on your own dataset using <a href="https://github.com/huggingface/autotrain-advanced" rel="nofollow">AutoTrain</a>. The amount of coding used in this blog post will be quite small. We will be writing <em>zero</em> lines of code! </p>
<p>Since Mixtral ... |
How do Textual Inversion tokens destroy prompts? | https://hf.co/blog/Isamu136/textual-inversion-prompt-destruction | Conclusion and Future Direction |
Experiments with Bitnet 1.5 (~ngmi~) | https://hf.co/blog/joey00072/experiments-with-bitnet-1-5 | Training code |
Create Mixtures of Experts with MergeKit | https://hf.co/blog/mlabonne/frankenmoe | References |
Elevate Responses: RAG with LlamaIndex & MongoDB | https://hf.co/blog/Andyrasika/mongodb-llamaindex-rag | Conclusion |
Samantha Mistral Instruct 7b - Comprehensive Bulleted Notes | https://hf.co/blog/cognitivetech/samantha-mistral-instruct-7b-bulleted-notes | Thanks |
Policy Questions Blog 1: AI Data Transparency Remarks for NAIAC Panel 📚🔍⚖️ | https://hf.co/blog/yjernite/naiac-data-transparency | A Minimum Standard for Meaningful Data Disclosure |
Protein similarity and Matryoshka embeddings | https://hf.co/blog/monsoon-nlp/proteins-matryoshka-embeddings | 🦠🧬🤖🪆 Future Thoughts |
A brief analysis of automerger data, feat. SLERP and DARE-TIES LLM merging | https://hf.co/blog/kgourgou/a-first-look-at-automerger-data | To sum up |
Data exploration and filtering with Nomic Atlas | https://hf.co/blog/visheratin/nomic-data-cleaning | Conclusion |
Giskard Bot: Identifying robustness, performance and ethical vulnerabilities in the Top 10 Most Popular Hugging Face Models | https://hf.co/blog/JMJM/vulnerabilities-top-10-hf-models | Conclusion |
Releasing Common Corpus: the largest public domain dataset for training LLMs | https://hf.co/blog/Pclanglais/common-corpus |
<p>
We announce today the release of <a href="https://huggingface.co/collections/PleIAs/common-corpus-65d46e3ea3980fdcd66a5613">Common Corpus</a> on HuggingFace:</p>
<ul>
<li>Common Corpus is the largest public domain dataset released for training LLMs.</li>
<li>Common Corpus includes 500 billion words from a wide di... |
What's Automatic Differentiation? | https://hf.co/blog/andmholm/what-is-automatic-differentiation | Personal |
Dive Deeper into Yi-9B | https://hf.co/blog/lorinma/yi-9b-divedeep | 📌 Related Resources |
Sparse Mixture of Experts Language Model from Scratch: Extending makeMoE with Expert Capacity | https://hf.co/blog/AviSoori1x/makemoe2 | Why is Expert Capacity even important? |
VideoMamba: State Space Model for Efficient Video Understanding | https://hf.co/blog/vladbogo/video-mamba | Conclusion |
Better RAG 3: The text is your friend | https://hf.co/blog/hrishioa/retrieval-augmented-generation-3-structure | Conclusion |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.