title stringlengths 4 172 | link stringlengths 27 86 | article stringlengths 4 40.1k |
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Gambling In The Probability Space | https://hf.co/blog/TuringsSolutions/gambling-the-probability-space | 8. References |
Taxonomy Completion with Embedding Quantization and an LLM-based Pipeline: A Case Study in Computational Linguistics | https://hf.co/blog/dcarpintero/taxonomy-completion | Resources |
How to Optimize TTFT of 8B LLMs with 1M Tokens to 20s | https://hf.co/blog/iofu728/ttft-1m-20s | How to Optimize KV Cache in Decoding? |
Create a Diffusers-compatible Dataset for Stable Diffusion Fine-tuning | https://hf.co/blog/nroggendorff/create-diffusers-dataset | Step 4. 🎉 You Did It! 🎉 (<strong>finally</strong>) |
Bringing Open-Source Models to Spreadsheets 🚀 | https://hf.co/blog/fdaudens/hugging-face-on-sheets | What's Next? |
Introducing HelpingAI-Flash: Emotionally Intelligent Conversational AI for All Devices | https://hf.co/blog/Abhaykoul/helpingai-flash | Conclusion |
Introduction to State Space Models (SSM) | https://hf.co/blog/lbourdois/get-on-the-ssm-train | <span style="color: #FF0000"> <strong>References</strong> </span> |
Announcing Finance Commons and the Bad Data Toolbox: Pioneering Open Data and Advanced Document Processing | https://hf.co/blog/Pclanglais/finance-commons-bad-data-toolbox | Will we solve PDF parsing before AGI? |
Mixedbread 🤝 deepset: Announcing our New German/English Embedding Model | https://hf.co/blog/shadeMe/deepset-mixedbread-new-german-embedding-model | Use it with the Mixedbread Embedders |
Swarm Neural Networks (SNN) for Image Generation | https://hf.co/blog/TuringsSolutions/snndiffusion | References |
Querying Datasets with the Datasets Explorer Chrome Extension | https://hf.co/blog/cfahlgren1/querying-datasets-with-sql-in-the-browser | It's Open Source 🤗 |
Deploy hundreds of open source models on one GPU using LoRAX | https://hf.co/blog/macadeliccc/deploy-hundreds-of-models-on-one-gpu | Citations |
Structured Harm Reporting in AI: New Research Paper at AIES and DEFCON event! | https://hf.co/blog/evijit/coordinatedflaws-aies-defcon | Looking Ahead |
Unleash ML Power on iOS: Apple Silicon Optimization Secrets | https://hf.co/blog/fguzman82/coreml-async-batch-prediction | References |
How OpenGPT 4o works | https://hf.co/blog/KingNish/opengpt-4o-working | Conclusion |
Market Research using AI Evolutionary Algorithms and Multimodal Regression | https://hf.co/blog/tonyassi/market-research-ai | About Me |
Introducing Ghost 8B Beta: A Game-Changing Language Model | https://hf.co/blog/lamhieu/introducing-ghost-8b-beta-a-game-changing-language |
<p>
<strong>Ghost 8B Beta</strong>, a groundbreaking language model, is poised to revolutionize the field of natural language processing. Developed with a focus on exceptional multilingual capabilities, superior knowledge acquisition, and cost-effectiveness, this model promises to unlock a new era of AI-powered applic... |
The Rise of Agentic Data Generation | https://hf.co/blog/mlabonne/agentic-datagen | Conclusion |
Mixture of Agents Model (MAM): An AI-Driven Full-Stack Development Team | https://hf.co/blog/dnnsdunca/mam-model | References |
Is AI carbon footprint worrisome? | https://hf.co/blog/as-cle-bert/is-ai-carbon-footprint-worrisome | References |
Optimisation d'un système RAG pour la recherche sémantique | https://hf.co/blog/Woziii/rag-semantic-search-space-huggingface | 3. Intégration dans gradio. |
In-browser LLM app in pure Python: Gemini Nano + Gradio-Lite | https://hf.co/blog/whitphx/in-browser-llm-gemini-nano-gradio-lite | Further reading and references |
Introducing HelpingAI-15B: Emotionally Intelligent Conversational AI | https://hf.co/blog/Abhaykoul/introducing-helpingai-15b | Emotional Quotient (EQ) |
How to run Gemini Nano locally in your browser | https://hf.co/blog/Xenova/run-gemini-nano-in-your-browser | References: |
MMLU-Pro-NoMath | https://hf.co/blog/sam-paech/mmlu-pro-nomath | And also to the original MMLU which MMLU-Pro heavily draws from: |
RegMix: Data Mixture as Regression for Language Model Pre-training | https://hf.co/blog/SivilTaram/regmix | Try RegMix on your dataset |
MInference 1.0: 10x Faster Million Context Inference with a Single GPU | https://hf.co/blog/liyucheng/minference10 | View more information about MInference |
Enhancing Search Capabilities for Non-English Datasets in the Dataset Viewer | https://hf.co/blog/asoria/fts-dataset-viewer | Considerations |
Introducing the Polish ASR Leaderboard (PAL) and Benchmark Intended Grouping of Open Speech (BIGOS) Corpora | https://hf.co/blog/michaljunczyk/introducing-polish-asr-leaderboard | References |
Metric and Relative Monocular Depth Estimation: An Overview. Fine-Tuning Depth Anything V2 👐 📚 | https://hf.co/blog/Isayoften/monocular-depth-estimation-guide | References |
The Great LLM Showdown: Amy's Quest for the Perfect LLM | https://hf.co/blog/wolfram/the-great-llm-showdown | A Call for Improvement |
BM25 for Python: Achieving high performance while simplifying dependencies with *BM25S*⚡ | https://hf.co/blog/xhluca/bm25s | Does BM25S replace other libraries? |
arXiv实用技巧，如何让你的paper关注度变高？ | https://hf.co/blog/JessyTsu1/arxiv-trick | 4. arXiv卡点提交 |
Swarm Neural Networks: Revolutionizing Function and API Call Execution | https://hf.co/blog/TuringsSolutions/swarmneuralnetworks | References |
_Repetita iuvant_: how to improve AI code generation | https://hf.co/blog/as-cle-bert/repetita-iuvant-how-to-improve-ai-code-generation | References |
RAG chatbot using llama3 | https://hf.co/blog/not-lain/rag-chatbot-using-llama3 | Dedication |
GPM: Generative Password Manager | https://hf.co/blog/apehex/gpm | Improvements |
ColPali: Efficient Document Retrieval with Vision Language Models 👀 | https://hf.co/blog/manu/colpali | Acknowledgments |
Advanced RAG: Fine-Tune Embeddings from HuggingFace for RAG | https://hf.co/blog/lucifertrj/finetune-embeddings | Co-Author: Shivaya Pandey |
Image-based search engine | https://hf.co/blog/not-lain/image-retriever | Acknowledgement |
EU Training Data Transparency: A Proposal for a Sufficiently Detailed Summary 📑📚🖼️🇪🇺 | https://hf.co/blog/yjernite/eu-data-template | Additional Resources |
Transformers | https://hf.co/blog/Esmail-AGumaan/attention-is-all-you-need | Citation: |
Systems of Representation Are All You Need | https://hf.co/blog/TuringsSolutions/systemsofrepresentation | EuclAId 750 Google Pro 1.0 |
A Guide to Designing New Functional Proteins and Improving Protein Function, Stability, and Diversity with Generative AI | https://hf.co/blog/AmelieSchreiber/protein-optimization-and-design | Concluding Remarks |
Building a Neural Network Classifier from the Ground Up: A Step-by-Step Guide | https://hf.co/blog/dcarpintero/building-a-neural-network-for-image-classification | References |
How I train a LoRA: m3lt style training overview | https://hf.co/blog/alvdansen/training-lora-m3lt | Final Observations |
Financial Analysis with Langchain and CrewAI Agents | https://hf.co/blog/herooooooooo/financial-analysis-with-langchain-and-crewai | CrewAI |
Train custom AI models with the trainer API and adapt them to 🤗 | https://hf.co/blog/not-lain/trainer-api-and-mixin-classes | Outro |
Formatting Datasets for Chat Template Compatibility | https://hf.co/blog/nroggendorff/format-mayo | Usage |
Part 2: Enhancing the Motoku LLM Retrieval System with OpenAI Embeddings and Prompt-based Retrieval | https://hf.co/blog/theeseus-ai/motoku-retrieval-2 | Conclusion |
Finetuning clip can be done locally with decent results (even if you are GPU poor). | https://hf.co/blog/herooooooooo/clip-finetune |
<p>
this is the journal of me following clipfinetune I have already posted this on medium but trying to slowly migrate my stuff here</p>
<pre><code class="language-python"><span class="hljs-keyword">import</span> os
<span class="hljs-keyword">import</span> datasets
<span class="hljs-keyword">from</span> dataclasses <... |
Building a Motoku LLM Retrieval System Using Internet Computer Protocol, Motoko, and Node.js | https://hf.co/blog/theeseus-ai/icp-retrieval-system | Conclusion |
Building an AI-Powered Card Counter with TensorFlow | https://hf.co/blog/theeseus-ai/card-counting | Conclusion |
Tokenization Is A Dead Weight | https://hf.co/blog/apehex/tokenization-is-a-dead-weight | Resources |
Evaluate RAG pipeline using HuggingFace Open Source Models | https://hf.co/blog/lucifertrj/evaluate-rag | Try BeyondLLM |
Build Agentic Workflow using OpenAGI and HuggingFace models | https://hf.co/blog/lucifertrj/openagi-blog | Join the Community |
MotionLCM: The Fastest and Best Motion Generation Model | https://hf.co/blog/EvanTHU/motionlcm | 📜 Citation |
💃Introducing the first LLM-based Motion understanding model: MotionLLM | https://hf.co/blog/EvanTHU/motionllm | 📜 Citation |
🚨 ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming | https://hf.co/blog/sted97/alert | Further Resources |
𝗝𝘂𝗱𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗝𝘂𝗱𝗴𝗲𝘀: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗟𝗟𝗠𝘀-𝗮𝘀-𝗝𝘂𝗱𝗴𝗲𝘀 | https://hf.co/blog/singh96aman/judgingthejudges |
<p>
𝐂𝐚𝐧 𝐋𝐋𝐌𝐬 𝐬𝐞𝐫𝐯𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐣𝐮𝐝𝐠𝐞𝐬 ⚖️?</p>
<p>We aim to identify the right metrics for evaluating Judge LLMs and understand their sensitivities to prompt guidelines, engineering, and specificity. With this paper, we want to raise caution ⚠️ to blindly using LLMs as human proxy. </p>
<p>... |
Claude-3.5 Evaluation Results on Open VLM Leaderboard | https://hf.co/blog/KennyUTC/claude3-5 |
<p>
<a href="https://cdn-uploads.huggingface.co/production/uploads/63ee1379190ddd6214efd73a/-jf9u3KKGt0pYLD2wYFZs.png" rel="nofollow"><img alt="image/png" src="https://cdn-uploads.huggingface.co/production/uploads/63ee1379190ddd6214efd73a/-jf9u3KKGt0pYLD2wYFZs.png"/></a></p>
<p>Claude3.5-Sonnet is the latest large mul... |
seemore: Implement a Vision Language Model from Scratch | https://hf.co/blog/AviSoori1x/seemore-vision-language-model | Bringing everything together to implement Seemore: the simple Vision Language Model |
SeeMoE: Implementing a MoE Vision Language Model from Scratch | https://hf.co/blog/AviSoori1x/seemoe |
<p>
TL;DR: In this blog I implement a mixture of experts vision language model consisting of an image encoder, a multimodal projection module and a mixture of experts decoder language model in pure pytorch. Thus, the resulting implementation could be thought of as a scaled down version of Grok 1.5 Vision and GPT-4 V... |
Shape Rotation 101: An Intro to Einsum and Jax Transformers | https://hf.co/blog/dejavucoder/einsum | Attention block |
Open-source embeddings and LLMs outperform Gemini and OpenAI for Web Navigation while being faster and cheaper | https://hf.co/blog/dhuynh95/evaluating-open-source-and-closed-models | Conclusion |
Recommendation to Revisit the Diffuser Default LoRA Parameters | https://hf.co/blog/alvdansen/revisit-diffusers-default-params |
<p>
Over the last year I have trained hundreds of LoRA finetunes with SDXL, and in the short time that I've spent back in the consulting space, I have tested with over a dozen startup apps that offer finetuning services on their platforms. I have seen, very consistently, the same general quality results from these tr... |
Introducing Synthetic Data Workshop: Your Gateway to Easy Synthetic Dataset Creation | https://hf.co/blog/davanstrien/synthetic-data-workshop | Next steps |
Extracting Concepts from LLMs: Anthropic’s recent discoveries 📖 | https://hf.co/blog/m-ric/extracting-concepts-from-llms | 4+. Moving forward |
Enhancing Image Model Dreambooth Training Through Effective Captioning: Key Observations | https://hf.co/blog/alvdansen/enhancing-lora-training-through-effective-captions |
<p>
In the realm of Dreambooth and LoRA training, especially when fine-tuning models for SDXL, the nuances of how you approach captioning can significantly impact the model's performance. Here are five key observations based on my experiences that can guide you in optimizing your training data for more precise and de... |
Unveiling CIVICS: A New Dataset for Examining Cultural Values in Language Models | https://hf.co/blog/giadap/civics | Future Directions |
Introducing the Ultimate SEC LLM: Revolutionizing Financial Insights with Llama-3-70B | https://hf.co/blog/Crystalcareai/llama-3-sec | References |
Train a Terrible Tic-Tac-Toe AI | https://hf.co/blog/nroggendorff/ttt-ai | This is a dumb project, and it won't work |
Thoughts on LoRA Training Pt 2: Where to Train | https://hf.co/blog/alvdansen/thoughts-on-lora-training-pt-2-training-services |
<p>
This is a pretty quick follow up, but there were some immediate "where do I start" questions I want to answer.</p>
<p>First and foremost, if you have <strong>never</strong> trained a LoRA before, start somewhere that has presets - as in a notebook or platform that has preset trianing parameter values. I beg of yo... |
Thoughts on LoRA Training #1 | https://hf.co/blog/alvdansen/thoughts-on-lora-training-1 |
<p>
I talk to many people about training LoRAs, from a variety of backgrounds. Some are very new to it, while others are well-established with impressive model portfolios. I aim to make this a series of posts, and possibly an article, discussing my thoughts on LoRA training and my suggestions.</p>
<p>For my part, I w... |
MobileNet-V4 (now in timm) | https://hf.co/blog/rwightman/mobilenetv4 | PyTorch Implementation |
Against mixing environment setup with code | https://hf.co/blog/ucheog/separate-env-setup-from-code | Use python-dotenv [say what?!] |
SwanLab and Transformers: Power Up Your NLP Experiments | https://hf.co/blog/Andyrasika/swanlab-transformers | Conclusion |
CryptGPT: Privacy-Preserving Language Models Using Vigenere Cipher (Part 1) | https://hf.co/blog/diwank/cryptgpt-part1 | A Challenge for Cryptanalysts and LLM Researchers |
The CVPR Survival Guide: Discovering Research That's Interesting to YOU! | https://hf.co/blog/harpreetsahota/cvpr2024-survival-guide | 🔊 Now, let's check all this out in the app! Turn your audio on because I'll explain what I'm doing! |
Uncensor any LLM with abliteration | https://hf.co/blog/mlabonne/abliteration | References |
Low Latency CPU Based Educational Value Classifier With Generic Educational Value | https://hf.co/blog/kenhktsui/edu-value-classifier-cpu | Citation |
An Optimal Lossy Variant of Speculative Decoding | https://hf.co/blog/vivien/optimal-lossy-variant-of-speculative-decoding | Conclusion and further work |
Reports on the Hub: A First Look at Self-governance in Open Source AI Development | https://hf.co/blog/frimelle/self-governance-open-source-ai |
<p>
<img alt="" src="https://cdn-uploads.huggingface.co/production/uploads/6531310497d7f1b4a083de7b/mWAsYQK9yJPaQRY86ijLc.png" style="display: block; margin: auto;"/></p>
<p>Hugging Face has a unique position as the most widely used open-source platform for AI models. As in many open-source projects, one of the invalu... |
Building a Vision Mixture-of-Expert Model from several fine-tuned Phi-3-Vision Models | https://hf.co/blog/mjbuehler/phi-3-vision-cephalo-moe | Citation |
Running Large Multimodal Models on an AI PC's NPU | https://hf.co/blog/bconsolvo/llava-gemma-2b-aipc-npu | Conclusions and calls to action |
Saving Memory Using Padding-Free Transformer Layers during Finetuning | https://hf.co/blog/mayank-mishra/padding-free-transformer | References |
An Analysis of Chinese LLM Censorship and Bias with Qwen 2 Instruct | https://hf.co/blog/leonardlin/chinese-llm-censorship-analysis | Recommendations |
Aligning Large Language Models with BRAIn | https://hf.co/blog/gauravpandey1/brain | Experimental results <a name="experimental" rel="nofollow"></a> |
What CI/CD practitioners know that ML engineers don’t… yet | https://hf.co/blog/Manialgie/what-cicd-practitioners-know-that-ml-engineers-don | TL;DR |
BrAIn: next generation neurons? | https://hf.co/blog/as-cle-bert/brain-next-generation-neurons | References |
Training an Object Detection Model with AutoTrain | https://hf.co/blog/abhishek/object-detection-autotrain | Conclusion |
Orchestrating Small Language Models (SLM) using JavaScript and the Hugging Face Inference API | https://hf.co/blog/rrg92/orchestrating-small-llms-javascript-inference-api | Other Endpoints |
Orquestrando Small Language Models (SLM) usando JavaScript e a API de Inferência do Hugging Face | https://hf.co/blog/rrg92/orquestrando-small-llms-javascript-api-inferencia | Demais endpoints |
Announcing Occiglot-Fineweb | https://hf.co/blog/malteos/occiglot-fineweb | Insights and Next steps |
🦙⚗️ Using Llama3 and distilabel to build fine-tuning datasets | https://hf.co/blog/dvilasuero/synthetic-data-with-llama3-distilabel | Full pipeline code |
Fine-tune and deploy open LLMs as containers using AIKit - Part 1: Running on a local machine | https://hf.co/blog/sozercan/finetune-deploy-aikit-part1 | 📚 Additional Resources |
Virtual Try-On using IP-Adapter Inpainting | https://hf.co/blog/tonyassi/virtual-try-on-ip-adapter | About Me |
LLM数据工程3——数据收集魔法：获取顶级训练数据的方法 | https://hf.co/blog/JessyTsu1/data-collect-zh | 数据版本控制 |
LLM Data Engineering 3——Data Collection Magic: Acquiring Top Training Data | https://hf.co/blog/JessyTsu1/data-collect | Data Version Control |
I ran 580 model-dataset experiments to show that, even if you try very hard, it is almost impossible to know that a model is degrading just by looking at data drift results | https://hf.co/blog/santiviquez/data-drift-estimate-model-performance |
<p>
In my opinion, data drift detection methods are very useful when we want to understand what went wrong with a model, but they are not the right tools to know how my model's performance is doing.</p>
<p>Essentially, using data drift as a proxy for performance monitoring is not a great idea.</p>
<p>I wanted to prov... |
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