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] | New adapt.s for dev 🍔
Hosted -> https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
✨Teen Outfit: https://huggingface.co/prithivMLmods/Teen-Outfit
✨Dark Pink: https://huggingface.co/prithivMLmods/Dark-Thing-Flux-LoRA
✨Shadow Projection: https://huggingface.co/prithivMLmods/Shadow-Projection-Flux-LoRA
✨Abstract Cartoon: https://huggingface.co/prithivMLmods/Abstract-Cartoon-Flux-LoRA
✨Street Bokeh: https://huggingface.co/prithivMLmods/Street-Bokeh-Flux-LoRA
✨Fine Detailed: https://huggingface.co/prithivMLmods/Flux-Realism-FineDetailed
✨Bold Shadows: https://huggingface.co/prithivMLmods/Bold-Shadows-Flux-LoRA
✨Yellow Laser: https://huggingface.co/prithivMLmods/Yellow-Laser-Flux-LoRA
------------
🎉LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
🎉LoRA Spaces: https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32
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[ViTPose Playground](https://huggingface.co/spaces/Dref360/vit_pose_playground)
This model will be available in `transformers` once [#30530](https://github.com/huggingface/transformers/pull/30530) is merged. Huge shoutout to @nielsr and @danelcsb for bringing this to HF!
Here's the result on my Ken Halloween costume.
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🔗 Try it yourself: https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts
This is democratization of coding in real-time. Excited to see AI tools becoming more capable and accessible.
What would you build with this? Share your ideas below! 👇
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] | 🎵 Introducing Suno Music Generation Dataset - https://huggingface.co/datasets/nyuuzyou/suno
Dataset highlights:
- 659,788 AI-generated music samples with comprehensive metadata from suno.com
- Multilingual content with English as primary language, including Japanese and other languages
- Each entry contains rich metadata including:
- Unique song ID, audio/video URLs, and thumbnail images
- AI model version and generation parameters
- Song metadata (tags, prompts, duration)
- Creator information and engagement metrics
- Released to the public domain under Creative Commons Zero (CC0) license
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- Music generation model training
- Cross-modal analysis (text-to-audio relationships)
- User engagement studies
- Audio classification tasks
- Music style and genre analysis | {
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] | 𝗤𝘄𝗲𝗻𝟮.𝟱-𝗖𝗼𝗱𝗲𝗿-𝟯𝟮𝗕: 𝗻𝗲𝘄 𝗯𝗲𝘀𝘁-𝗶𝗻-𝗰𝗹𝗮𝘀𝘀 𝗼𝗽𝗲𝗻 𝗰𝗼𝗱𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹, 𝗯𝗲𝗮𝘁𝘀 𝗚𝗣𝗧-𝟰𝗼 𝗼𝗻 𝗺𝗼𝘀𝘁 𝗰𝗼𝗱𝗶𝗻𝗴 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀!💥
💪 It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities!
✨ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B
📚 Support long context up to 128K (for the 14B and 32B models)
✅ Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts!
🤗 Models available right now on the Hub, under Apache 2.0 license!
They have setup a crazy Artifacts demo, you should go have a look!
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"value": "📊 What are scaling laws? These are empiric laws that say \"Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick\". Of course, they apply only if you train your model with the right methods.",
"raw": "📊 What are scaling laws? These are empiric laws that say \"Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick\". Of course, they apply only if you train your model with the right methods.",
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"value": "The image below illustrates it: they're from a paper by Google, \"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation\", and they show how quality and instruction following of models improve when you scale the model up (which is equivalent to scaling up the compute spent in training).",
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"value": "➡️ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their \"Startgate\" mega training cluster, due to start running in 2028.",
"raw": "➡️ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their \"Startgate\" mega training cluster, due to start running in 2028.",
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"value": "🤔 So, what about these reports of scaling laws slowing down?",
"raw": "🤔 So, what about these reports of scaling laws slowing down?",
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"value": "If they are true, they would mean a gigantic paradigm shift, as the hundreds of billions poured by AI companies into scaling could be a dead-end. ⛔️",
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] | 𝗔𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗼𝘃𝗲𝗿? 𝗔 𝗿𝗲𝗽𝗼𝗿𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱 𝘁𝗵𝗮𝘁 𝗢𝗽𝗲𝗻𝗔𝗜 𝗶𝘀 𝘀𝗲𝗲𝗶𝗻𝗴 𝗱𝗶𝗺𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴 𝗿𝗲𝘁𝘂𝗿𝗻𝘀 𝗳𝗿𝗼𝗺 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘂𝗽 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗚𝗣𝗧 𝗺𝗼𝗱𝗲𝗹𝘀.
📊 What are scaling laws? These are empiric laws that say "Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick". Of course, they apply only if you train your model with the right methods.
The image below illustrates it: they're from a paper by Google, "Scaling Autoregressive Models for Content-Rich Text-to-Image Generation", and they show how quality and instruction following of models improve when you scale the model up (which is equivalent to scaling up the compute spent in training).
➡️ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their "Startgate" mega training cluster, due to start running in 2028.
🤔 So, what about these reports of scaling laws slowing down?
If they are true, they would mean a gigantic paradigm shift, as the hundreds of billions poured by AI companies into scaling could be a dead-end. ⛔️
But I doubt it: until the most recent publications, scaling laws showed no signs of weakness, and the researchers at the higher end of the scale-up seems to imply the scaling up continues.
Wait and see! | {
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"value": "After outpuiting the list, accoring to the list optmiced Composer edit block to fix the ones severe that make sense to adjust accoirng to gradio limitations and current usage target )dont suppose we need unecessary funcitons)",
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You are a frustrated user who has tested this application extensively. Your job is to list EVERY possible way this app could completely break or become unusable.
For each potential failure:
1. What would make you say "This app is totally broken!"?
2. What exact steps did you take when it broke?
3. What did you see on your screen when it broke?
4. How angry would this make a typical user (1-10)?
5. What would you expect the app to do instead?
Think about:
- What happens if you click buttons really fast?
- What if your internet is slow/disconnected?
- What if you upload weird files/images?
- What if you try to break the app on purpose?
- What if multiple people use it at once?
- What if you use it on mobile/tablet?
- What if you refresh/navigate while it's working?
- What if you paste invalid inputs?
- What if you upload HUGE files?
- What if you leave it running overnight?
Don't worry about being technical - just describe what you saw break as a user.
Format each issue like:
ISSUE #1: [Brief angry user description]
- STEPS TO BREAK IT: [Exactly what you did]
- WHAT HAPPENED: [What you saw]
- ANGER LEVEL: [1-10]
- EXPECTED: [What should happen]
Keep going until you've found every possible way to break this app from a user's perspective!
After outpuiting the list, accoring to the list optmiced Composer edit block to fix the ones severe that make sense to adjust accoirng to gradio limitations and current usage target )dont suppose we need unecessary funcitons) | {
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] | 2024-11-11T17:27:35.000Z | 2024-11-11T17:27:35.284Z | [] | /posts/luigi12345/444428540739993 | 2,092 | 0 |
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] | NEW RELEASE! Shining Valiant 2 for Llama 3.1 70b is here!
- Trained on high quality science-instruct, complex queries, and general chat data!
- Uses our newest datasets, ALL open-sourced for everyone to use!
GET SV2 70B: https://huggingface.co/ValiantLabs/Llama3.1-70B-ShiningValiant2
- Find the SV datasets here, including the expanded version of our science-instruct dataset:
- https://huggingface.co/datasets/sequelbox/Celestia
- https://huggingface.co/datasets/sequelbox/Spurline
- https://huggingface.co/datasets/sequelbox/Supernova
- SV2 8b and 3b will be updated with the new datasets soon!
Enjoy! :) | {
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] | Cybertron is back:
We released today a newest version of Cybertron: V4 based on Qwen2.5 7B and trained on MagPie. Scoring #1 LLM on 7B & 8B class.
The model hasn't go thru DPO, so the weights are in good shape to welcome further training sessions and optimizations.
Enjoy it in the hub as usual:
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] | FLUX De-Distilled and Anti-Bleeding Fine-Tuning / DreamBooth & LoRA Training Experiments
Also Testing CFG Impact for Stylized Images on base FLUX DEV model
Full size grids and full details shared here : https://www.patreon.com/posts/114969137
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] | ⚡️ LLMs do a good job at NER, but don't you want to do learn how to do more with less?
Go from 🐢 -> 🐇
If you want a small model to perform well on your problem, you need to fine-tune it.
Bootstrap with a teacher model.
Correct potential mistakes to get high-quality data.
Fine-tune your student model
Go more accurate and more efficient.
Free signup: https://lu.ma/zx2t7irs | {
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] | 🚀 Exploring Topic Modeling with BERTopic 🤖
When you come across an interesting dataset, you often wonder:
Which topics frequently appear in these documents? 🤔
What is this data really about? 📊
Topic modeling helps answer these questions by identifying recurring themes within a collection of documents. This process enables quick and efficient exploratory data analysis.
I’ve been working on an app that leverages BERTopic, a flexible framework designed for topic modeling. Its modularity makes BERTopic powerful, allowing you to switch components with your preferred algorithms. It also supports handling large datasets efficiently by merging models using the BERTopic.merge_models approach. 🔗
🔍 How do we make this work?
Here’s the stack we’re using:
📂 Data Source ➡️ Hugging Face datasets with DuckDB for retrieval
🧠 Text Embeddings ➡️ Sentence Transformers (all-MiniLM-L6-v2)
⚡ Dimensionality Reduction ➡️ RAPIDS cuML UMAP for GPU-accelerated performance
🔍 Clustering ➡️ RAPIDS cuML HDBSCAN for fast clustering
✂️ Tokenization ➡️ CountVectorizer
🔧 Representation Tuning ➡️ KeyBERTInspired + Hugging Face Inference Client with Meta-Llama-3-8B-Instruct
🌍 Visualization ➡️ Datamapplot library
Check out the space and see how you can quickly generate topics from your dataset: https://huggingface.co/spaces/datasets-topics/topics-generator
Powered by @MaartenGr - BERTopic | {
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AI spotted patterns, humans verified facts. Every AI-flagged quote was manually verified against source recordings. Really appreciate that they published their full methodology - transparency matters when using AI in journalism.
A perfect blend of tech & journalism.
The future of journalism isn't robots replacing reporters - it's AI helping humans process massive datasets more efficiently. Sometimes the most powerful tech solutions are the least flashy ones.
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https://huggingface.co/spaces/open-llm-leaderboard/comparator
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] | New Dataset: Software-Architecture
Link: https://huggingface.co/datasets/ajibawa-2023/Software-Architecture
I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl.
I have included following topics:
Architectural Frameworks
Architectural Patterns for Reliability
Architectural Patterns for Scalability
Architectural Patterns
Architectural Quality Attributes
Architectural Testing
Architectural Views
Architectural Decision-Making
Advanced Research
Cloud-Based Architectures
Component-Based Architecture
Data Architecture
Emerging Trends
Event-Driven Architecture
Evolvability and Maintainability
Microservices and Monolithic
Microservices Architecture
Security Architecture
Service-Oriented Architecture
Software Design Principles
and Many More!
This dataset is useful in LLM development. Also those who are working on developing Software development related LLMs then this dataset can be useful.
This dataset is very useful to Researchers as well.
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] | Check out The AI Writing Contest with @BrightData! https://www.contests.hackernoon.com/ai-writing-contest Cash prizes for innovative approaches to AI and LLM training. We publish blog posts, research papers, stories of side hustles, you name it.
Any story tagged #AI enters to win. Most recent stories: https://hackernoon.com/tagged/ai and RSS feed https://hackernoon.com/tagged/ai/feed
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Why Salesforce and Microsoft Are Battling for the Future of AI Agents https://hackernoon.com/why-salesforce-and-microsoft-are-battling-for-the-future-of-ai-agents
Decentralized AI Summit at MIT Votes OriginTrail As The Best Decentralized AI Project https://hackernoon.com/decentralized-ai-summit-at-mit-votes-origintrail-as-the-best-decentralized-ai-project
Studying is Overrated https://hackernoon.com/studying-is-overrated
Why Can’t AI Count Letters??? https://hackernoon.com/why-cant-ai-count-letters
The Paradox of AI: If It Can't Replace us, Is It Making Us Dumber? https://hackernoon.com/the-paradox-of-ai-if-it-cant-replace-us-is-it-making-us-dumber
How Does Human Memory Work? https://hackernoon.com/how-does-human-memory-work
Is AI Actually Writing Production-Ready Code? https://hackernoon.com/is-ai-actually-writing-production-ready-code
Our AI Coding Tool Went Viral, Then Everything Broke. This is What We Learned. https://hackernoon.com/our-ai-coding-tool-went-viral-then-everything-broke-this-is-what-we-learned
Startups of The Year: Meet the AI Industry https://hackernoon.com/startups-of-the-year-meet-the-ai-industry
Nobel Prize Winner Geoffrey Hinton Explores Two Paths to Intelligence in AI Lecture https://hackernoon.com/nobel-prize-winner-geoffrey-hinton-explores-two-paths-to-intelligence-in-ai-lecture
Comparing AI vs. Blockchain Hype https://hackernoon.com/comparing-ai-vs-blockchain-hype
The SaaS Apocalypse and How aI Will Give Birth to One-person Tech Giants https://hackernoon.com/the-saas-apocalypse-and-how-ai-wi
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📄 Title: Expressive Gaussian Human Avatars from Monocular RGB Video 🔝
📝 Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video.
👥 Authors: Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang Wang
📄 Paper: https://huggingface.co/papers/2407.03204
🌐 Github Page: https://evahuman.github.io/
📁 Repository: https://github.com/evahuman/EVA
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📚 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
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] | New dataset filtering feature just dropped! 🤗🚀
Find exactly what you need with filters for:
- Modalities (text, image, audio, etc.)
- Dataset size
- File format
Try it now: https://huggingface.co/datasets
What other filters would you find useful? Drop your ideas! | {
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I'd be super happy to give you a GPU grant to host it on a Space, it would allow more people to discover and use it! | {
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- 🚀 Fast inference using llama.cpp
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] | @Omartificial-Intelligence-Space has trained and released 6 Arabic embedding models for semantic similarity. 4 of them outperform all previous models on the STS17 Arabic-Arabic task!
📚 Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet
6️⃣ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.
🪆 Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.
📈 Outperforms all commonly used multilingual models like https://huggingface.co/intfloat/multilingual-e5-large, https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2, and https://huggingface.co/sentence-transformers/LaBSE.
Check them out here:
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
Or the collection with all: https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e
My personal favourite is likely https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka: a very efficient 135M parameters & scores #1 on https://huggingface.co/spaces/mteb/leaderboard.
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] | I really like what the @jasperAITeam designed with Flash LoRA. It works really well for something that generates so quickly, and I'm excited to test it out with Animate Diff, because I recently was testing LCM on it's own for AD and the results were already promising.
I put together my own page of models using their code and LoRA. Enjoy!
https://huggingface.co/spaces/alvdansen/flash-lora-araminta-k-styles
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] | nanoLLaVA-1.5 is here! Same size (1B), better performance 🔥🔥🔥
And it is much more powerful than v1.0
Try it out now on HF Spaces: https://huggingface.co/spaces/qnguyen3/nanoLLaVA
Model: https://huggingface.co/qnguyen3/nanoLLaVA-1.5
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] | Below we experiment with negative merger weighting (-1.0!) using task arithmetic. Merge formula on the model card and in the repo itself.
This model is steered to behave opposite to what MopeyMule demonstrated.
Based on the implications of the merge technique, we also propose Orthogonalized Vector Adaptation (OVA). We also extract a LoRA of the counter-refusal abliteration steering vector.
The resulting merger is not a perfect model, but it's a behaviorally interesting model. The model name was inspired by a Philip K. Dick story.
https://huggingface.co/grimjim/Llama-3-Perky-Pat-Instruct-8B
Refusal vector weights ready for use:
https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-OVA-8B
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] | An Open-source and super-fast alternative to @OpenAI GPT4o is here! 🚀
In November last year, Iliad announced a fully open-source-oriented AI lab called @kyutai_labs 🧪
In this very short time they have released Moshi! An open speech-to-speech model 🗣️... released publicly even before closed GPT4o (yes, you can try it right now!) 🌐
Demo: https://www.moshi.chat/?queue_id=talktomoshi
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This level of engagement is so new, it almost feels like I am under pressure to keep up with it! 😅
While it hallucinates like crazy! 🤯 I think fundamentally this is what a true assistant would look like. And did I say they are going to open-source it? 🆓
Weights and full technical report are promised to be coming soon! 📜
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] | As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.
Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.
Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.
For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.
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] | New #NVIDIA paper: Improving Hyperparameter Optimization with Checkpointed Model Weights
Hyperparameter optimization often dominates the cost of model design. So, we want cheap surrogate functions that approximate model performance to guide our search. Existing methods can train on optimization metadata – like a trajectory of losses – to build these surrogates.
In our work, we add the ability to train our hyperparameter optimization surrogates on checkpointed model weights with a graph metanetwork. This allows us to leverage a large, pre-existing source of information that can featurize the architecture, dataset, losses, and optimization procedure.
🔍Project page: https://research.nvidia.com/labs/toronto-ai/FMS/
👨💻 Code for reproduction: https://github.com/NVlabs/forecasting-model-search
📄 Full Paper: https://arxiv.org/abs/2406.18630
Our project was a collaboration between NVIDIA’s Toronto AI Lab and the TAO team.
Check out more work from Toronto AI Lab here: https://research.nvidia.com/labs/toronto-ai/
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AI Agents Solved!
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] | 🌟 It's been about a week since @Google dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! 🚀💪
🔍 Here is what a week of Gemma looked like:
1️⃣ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! 🤯
2️⃣ Next, the technical report mentions a 2B model... where is it? 🤔
3️⃣ Simple things like a context length of 8192 tokens, the Rotary Position Embeddings (RoPE), and the approximated GeGLU non-linearity are similar to earlier Gemma's. 📏🔄
4️⃣ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! 🤷♂️ and of course Logit soft-capping. 💡
5️⃣ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). 🎓➡️📚
6️⃣ Model Merging: Combined average models from experiments run with different hyperparameters. 🧪⚗️
7️⃣ And borrowed Grouped-Query Attention (GQA) from @Meta Llama-3. 🔄🤝
📖 One of the best articles on Gemma 2: https://huggingface.co/blog/gemma2
📊 Technical report: https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf
🔗 Models: https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315
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"value": "It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!",
"raw": "It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!",
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"value": "Have a great day, and enjoy! This was one fun! 🤗",
"raw": "Have a great day, and enjoy! This was one fun! 🤗",
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] | Hello!
I've been in the lab, I think one or two of you saw my furtive attempts to create a dolphinized 2b Gemma, which is still waiting for more funding. I get paid in a week. `</3`
Once that funding ran out, I dropped my last pinch of API credits to work on this:
https://huggingface.co/datasets/DigitalClockwork/spatial_instruct_v1
It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!
Have a great day, and enjoy! This was one fun! 🤗
`-<3` | {
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] | 2024-07-02T16:36:29.000Z | 2024-07-02T16:37:27.996Z | [] | /posts/MrOvkill/927324136184813 | 2,073 | 0 |
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"value": "It's trendy to share models \"fine-tuned for function calling\"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.",
"raw": "It's trendy to share models \"fine-tuned for function calling\"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.",
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"value": "To name only a few:",
"raw": "To name only a few:",
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"value": "🐦⬛ Nexusflow/𝗡𝗲𝘅𝘂𝘀𝗥𝗮𝘃𝗲𝗻-𝗩𝟮-𝟭𝟯𝗕",
"raw": "🐦⬛ Nexusflow/𝗡𝗲𝘅𝘂𝘀𝗥𝗮𝘃𝗲𝗻-𝗩𝟮-𝟭𝟯𝗕",
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"value": "⌘ CohereForAI/𝗰𝟰𝗮𝗶-𝗰𝗼𝗺𝗺𝗮𝗻𝗱-𝗿-𝗽𝗹𝘂𝘀",
"raw": "⌘ CohereForAI/𝗰𝟰𝗮𝗶-𝗰𝗼𝗺𝗺𝗮𝗻𝗱-𝗿-𝗽𝗹𝘂𝘀",
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"value": "\"Fine-tuned for function-calling\" generally means \"fine-tuned to generate function calls in correct JSON for extremely simple tasks\". In other terms, it means \"improve the formatting of the tool calls\".",
"raw": "\"Fine-tuned for function-calling\" generally means \"fine-tuned to generate function calls in correct JSON for extremely simple tasks\". In other terms, it means \"improve the formatting of the tool calls\".",
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"value": "Yet I discovered two things while improving Transformers Agents:",
"raw": "Yet I discovered two things while improving Transformers Agents:",
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"value": "🧐 Even when used as JSON agents, these fine-tuned models don't perform very well",
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"value": "🏅 𝙂𝙤𝙤𝙙 𝙗𝙖𝙨𝙚 𝙢𝙤𝙙𝙚𝙡𝙨 𝙥𝙚𝙧𝙛𝙤𝙧𝙢 𝙗𝙚𝙩𝙩𝙚𝙧 𝙬𝙞𝙩𝙝𝙤𝙪𝙩 𝙖𝙣𝙮 𝙛𝙞𝙣𝙚-𝙩𝙪𝙣𝙞𝙣𝙜, 𝙟𝙪𝙨𝙩 𝙥𝙡𝙖𝙞𝙣 𝙥𝙧𝙤𝙢𝙥𝙩𝙞𝙣𝙜. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet) ",
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"value": "👇 The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: 𝙰𝚐𝚎𝚗𝚝𝙿𝚊𝚛𝚜𝚒𝚗𝚐𝙴𝚛𝚛𝚘𝚛 and 𝙰𝚐𝚎𝚗𝚝𝙴𝚡𝚎𝚌𝚞𝚝𝚒𝚘𝚗𝙴𝚛𝚛𝚘𝚛 are the ones caused by incorrect formatting.",
"raw": "👇 The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: 𝙰𝚐𝚎𝚗𝚝𝙿𝚊𝚛𝚜𝚒𝚗𝚐𝙴𝚛𝚛𝚘𝚛 and 𝙰𝚐𝚎𝚗𝚝𝙴𝚡𝚎𝚌𝚞𝚝𝚒𝚘𝚗𝙴𝚛𝚛𝚘𝚛 are the ones caused by incorrect formatting.",
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"value": "➤ As you can see, their count is already close to 0!",
"raw": "➤ As you can see, their count is already close to 0!",
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"value": "And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that \"function calling fine-tuning\" is not necessary!",
"raw": "And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that \"function calling fine-tuning\" is not necessary!",
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"value": "The hardest thing to get right in an agent is still to 𝙥𝙡𝙖𝙣 𝙜𝙤𝙤𝙙 𝙩𝙖𝙨𝙠-𝙨𝙤𝙡𝙫𝙞𝙣𝙜 𝙩𝙧𝙖𝙟𝙚𝙘𝙩𝙤𝙧𝙞𝙚𝙨 𝙤𝙫𝙚𝙧 𝙨𝙚𝙫𝙚𝙧𝙖𝙡 𝙨𝙩𝙚𝙥𝙨.",
"raw": "The hardest thing to get right in an agent is still to 𝙥𝙡𝙖𝙣 𝙜𝙤𝙤𝙙 𝙩𝙖𝙨𝙠-𝙨𝙤𝙡𝙫𝙞𝙣𝙜 𝙩𝙧𝙖𝙟𝙚𝙘𝙩𝙤𝙧𝙞𝙚𝙨 𝙤𝙫𝙚𝙧 𝙨𝙚𝙫𝙚𝙧𝙖𝙡 𝙨𝙩𝙚𝙥𝙨.",
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"value": "To improve this, we could:",
"raw": "To improve this, we could:",
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"value": "- Use more powerful base models",
"raw": "- Use more powerful base models",
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"value": "- Make tool calling datasets with complex solving trajectories",
"raw": "- Make tool calling datasets with complex solving trajectories",
"resource": null,
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"value": "- Use RL! cc ",
"raw": "- Use RL! cc ",
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] | 𝗬𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 "𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴" 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗴𝗼𝗼𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 ⛔
It's trendy to share models "fine-tuned for function calling"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.
To name only a few:
🐦⬛ Nexusflow/𝗡𝗲𝘅𝘂𝘀𝗥𝗮𝘃𝗲𝗻-𝗩𝟮-𝟭𝟯𝗕
⌘ CohereForAI/𝗰𝟰𝗮𝗶-𝗰𝗼𝗺𝗺𝗮𝗻𝗱-𝗿-𝗽𝗹𝘂𝘀
⛵️ mistralai/𝗠𝗶𝘅𝘁𝗿𝗮𝗹-𝟴𝘅𝟮𝟮𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁-𝘃𝟬.𝟭
"Fine-tuned for function-calling" generally means "fine-tuned to generate function calls in correct JSON for extremely simple tasks". In other terms, it means "improve the formatting of the tool calls".
Yet I discovered two things while improving Transformers Agents:
🧐 Even when used as JSON agents, these fine-tuned models don't perform very well
🏅 𝙂𝙤𝙤𝙙 𝙗𝙖𝙨𝙚 𝙢𝙤𝙙𝙚𝙡𝙨 𝙥𝙚𝙧𝙛𝙤𝙧𝙢 𝙗𝙚𝙩𝙩𝙚𝙧 𝙬𝙞𝙩𝙝𝙤𝙪𝙩 𝙖𝙣𝙮 𝙛𝙞𝙣𝙚-𝙩𝙪𝙣𝙞𝙣𝙜, 𝙟𝙪𝙨𝙩 𝙥𝙡𝙖𝙞𝙣 𝙥𝙧𝙤𝙢𝙥𝙩𝙞𝙣𝙜. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet)
👇 The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: 𝙰𝚐𝚎𝚗𝚝𝙿𝚊𝚛𝚜𝚒𝚗𝚐𝙴𝚛𝚛𝚘𝚛 and 𝙰𝚐𝚎𝚗𝚝𝙴𝚡𝚎𝚌𝚞𝚝𝚒𝚘𝚗𝙴𝚛𝚛𝚘𝚛 are the ones caused by incorrect formatting.
➤ As you can see, their count is already close to 0!
And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that "function calling fine-tuning" is not necessary!
The hardest thing to get right in an agent is still to 𝙥𝙡𝙖𝙣 𝙜𝙤𝙤𝙙 𝙩𝙖𝙨𝙠-𝙨𝙤𝙡𝙫𝙞𝙣𝙜 𝙩𝙧𝙖𝙟𝙚𝙘𝙩𝙤𝙧𝙞𝙚𝙨 𝙤𝙫𝙚𝙧 𝙨𝙚𝙫𝙚𝙧𝙖𝙡 𝙨𝙩𝙚𝙥𝙨.
To improve this, we could:
- Use more powerful base models
- Make tool calling datasets with complex solving trajectories
- Use RL! cc @lvwerra | {
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📄 paper: https://huggingface.co/papers/2303.15764 | {
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"value": "👥 More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.",
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"value": "🌌 For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total). ",
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] | 🐦 Do you remember IBIS? Not a fancy bird but the open challenge in Inferring Binding Specificities of unexplored human transcription factors. Check our site (https://ibis.autosome.org/) and have a sip of fresh news below.
👥 More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.
🌐 Remember, the training data for Leaderboard and Final are available online, and you are free to mix-and-match it in any combination.
🌌 For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total).
🚀 For PWMs, the baseline is left far behind, but some TFs remain tough nuts to be cracked (see the attached figure 1).
📈 For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance!
💡 Another free hint: If your model tends to overfit given a limited set of data for some TFs don't forget to use reverse-complement and shift augmentations. Also, don't hesitate to use multitarget models i.e. predicting the binding of multiple TFs at the same time.
💡 Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model!
📣 Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline.
🗓️ The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at https://ibis.autosome.org). | {
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📄 Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture 🔝
📝 Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images.
👥 Authors: @Dazz1e, Y. Cheng, @Ryan-sjtu, H. Jia, D. Xu, W. Zhu, Y. Yan
📅 Conference: ECCV, 29 Sep – 4 Oct, 2024 | Milano, Italy 🇮🇹
📄 Paper: https://huggingface.co/papers/2406.00440
🌐 Github Page: https://xuanchenli.github.io/Topo4D/
📁 Repository: https://github.com/XuanchenLi/Topo4D
🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
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] | I've created a Stable Diffusion 3 (SD3) image generation space for convenience. Now you can:
1. Generate SD3 prompts from images
2. Enhance your text prompts (turn 1-2 words into full SD3 prompts)
https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer
These features are based on my custom models:
- VLM captioner for prompt generation:
- https://huggingface.co/gokaygokay/sd3-long-captioner
- Prompt Enhancers for SD3 Models:
- https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long
- https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance
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📜 Arxiv: https://arxiv.org/abs/2407.01231
🔗 Project page: https://mirai-llm.github.io
💻 GitHub Repo: https://github.com/yecchen/MIRAI
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Kudos to @AnthropicAI for this elegant API! 👏 #AI #CodeMagic #AnthropicAI Thanks Huggingface for hosting the best hub in the world for AI development!
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] | 🚀 Transformers are not here to take part but take over... and down goes real-time object detection! 💥
Enter Real-time DEtection Transformer (RT-DETR) 🦾 as suggested capable of real-time object detection. 🎯
Object DEtection Transformer (DETR) is not new (@Meta did it eons ago) but it had the issue of every other transformer, high computational cost 💸
RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed 🏎️
Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. ⚡
It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. ⚖️
This makes RT-DETR faster and more accurate than previous YOLO models. 🏆
How much better😎/faster? ⏱️
RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. 🚀✨
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] | Search Hugging Face datasets by column names with a new experimental API! This API allows you to:
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🔖 models: https://huggingface.co/PekingU
🔖 demo: https://huggingface.co/spaces/merve/RT-DETR-tracking-coco
📝 paper: https://huggingface.co/papers/2304.08069
📖 notebook: https://github.com/merveenoyan/example_notebooks/blob/main/RT_DETR_Notebook.ipynb
YOLO models are known to be super fast for real-time computer vision, but they have a downside with being volatile to NMS 🥲
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Isn't there something in between? Enter RT-DETR!
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The authors find out that the model performs better in terms of speed and accuracy compared to the previous state-of-the-art. 🤩
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] | **How I train a LoRA: m3lt style training overview**
I've just written an article that takes a step by step approach to outlining the method that I used to train the 'm3lt' lora, a blended style model.
I've used the LoRA Ease trainer by @multimodalart :D
https://huggingface.co/blog/alvdansen/training-lora-m3lt
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] | 🚨Exciting news for the Multilingual Synthetic Data Community!🚨
I’ve taken inspiration from the MAGPIE paper on Llama-3-8B-instruct and extended its capabilities. Here’s what’s new!
🗞 The MAGPIE paper showcased that if you use the instruction-tuned version (`Llama-3-8B-instruct`) to generate synthetic instructions and then fine-tune the base version (`Llama-3-8B`) on this dataset, you can improve even the it-tuned version
🤔 While reading a script by Sebastian Raschka, PhD, I wondered: Could these advancements be replicated in other languages? Specifically, could they benefit non-English datasets?
🎉 And the answer is YES! At least for Spanish. I've successfully adapted the techniques for Spanish, proving the model's flexibility and multilingual capabilities.
👩💻 To make this accessible, I created a basic script (heavily inspired by the Sebastian Raschka one) that allows you to generate similar datasets using `ollama` models (initially phi and llama3) automatically and upload it to the Hugging Face Hub!
[Script](https://gist.github.com/mrm8488/4650a5e3cc45523798a527a3446eb312)
🔍 Explore the datasets 📚 generated using our new script!
- [Llama-3-8B](https://huggingface.co/datasets/mrm8488/dataset_llama3_5000_samples_es_4231_filtered)
- [Phi-3-medium](https://huggingface.co/datasets/mrm8488/dataset_phi3-medium_5000_samples_es_3906_filtered)
- [Phi-3-mini](https://huggingface.co/datasets/mrm8488/dataset_phi3_5000_samples_es_3282_filtered)
Note: These datasets have basic filtering. Apply additional quality filters before using them to fine-tune large language models.
Inspiration and base script:
https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/05_dataset-generation/llama3-ollama.ipynb
https://www.linkedin.com/feed/update/urn:li:activity:7210982019751661568/
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] | I have finished writing a blogpost about building an image-based retrieval system, This is one of the first-ever approaches to building such a pipeline using only open-source models/libraries 🤗
You can checkout the blogpost in https://huggingface.co/blog/not-lain/image-retriever and the associated space at https://huggingface.co/spaces/not-lain/image-retriever .
✨ If you want to request another blog post consider letting me know down below or you can reach out to me through any of my social media
📖 Happy reading !
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"value": "What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥",
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] | Yet another rewarding week in Open Source AI:
1. Google dropped Gemma 27B & 9B - The best open (commercially permissive) LLM out there, according to LYMSYS.
https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315
2. Mars5 TTS - Text to Speech with insane prosodies control & voice cloning.
https://huggingface.co/CAMB-AI/MARS5-TTS
3. Meta shipped LLM Compiler - beats GPT 4 on code optimisation and compiler reasoning.
https://huggingface.co/collections/facebook/llm-compiler-667c5b05557fe99a9edd25cb
4. Arcee-Spark - Qwen2 7B (w/ merging) fine-tuned further to beat GPT 3.5 on MT Bench.
https://huggingface.co/arcee-ai/Arcee-Spark
5. Gemini Nano out in the wild in Chrome - On device LLM with just 2 lines of code (fully offline)
6. Fal released a fully Open Source GAN based Super-Resolution model (with second version already cooking)
https://huggingface.co/fal/AuraSR
7. NYU release Cambrian 1 - Vision Multimodal LLM that beats pretty much all other closed source competition 8-34B model size
https://huggingface.co/nyu-visionx
And.. much more like Open LLM Leaderboard got a major update, LYMSYS released Chat Vision Arena, OpenAI released a paper on CriticGPT!
What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥 | {
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] | The paper is now available at ICML 2024. This paper introduces a foundational approach to analyze deep reinforcement learning decision making. Truly excited to share these results!
Paper: https://openreview.net/pdf?id=s9RKqT7jVM
HF: https://huggingface.co/papers/2406.16979
Twitter: https://x.com/EzgiKorkmazAI/status/1798990328390996002 | {
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] | Announcing the creation of the "HF for Legal" organization, an open-source community dedicated to demystifying language models for legal professionals 🤗
Whether you're a practicing attorney, a legal scholar, or a technologist interested in legal applications of AI, HF for Legal may be your hub for exploration, learning, and free innovation ⚗️
On the occasion of this launch, you'll be able to find several notebooks I've been developing over the last few months for TSDAE pre-training of embedding models, the generation of indexes for semantic search, based on the formidable work of @tomaarsen and @nreimers, adapted to the field of French law, or the addition of information retrieval tasks to the MTEB.
Join us in our mission to make AI more accessible and understandable for the legal world, ensuring that the power of language models can be harnessed effectively and ethically.
Link to the org: https://huggingface.co/HFforLegal
Special thanks to @clem for encouraging me to start this organization. Let's hope we can bring together all the enthusiasts who work in this field.
Let's code and share together! 🚀🔗 | {
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"value": "💡 A recent paper, \"Refusal in Language Models Is Mediated by a Single Direction,\" showed how to find the refusal direction in the activation space of Chat Language Models and either erase or amplify it.",
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"raw": "A clever jailbreak method for open weights models.",
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"value": " took it a step further by modifying the models to amplify different traits, such as making a model seem grumpy or irritable.",
"raw": " took it a step further by modifying the models to amplify different traits, such as making a model seem grumpy or irritable.",
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"value": "𝐇𝐨𝐰 𝐝𝐢𝐝 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞 𝐲𝐨-𝐋𝐥𝐚𝐦𝐚?",
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"raw": "1️⃣ Load the Llama-3-8B-Instruct model.",
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"raw": "2️⃣ Load 1024 examples from Alpaca (instruction dataset).",
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] | How to alter the behavior of a Language Model without fine-tuning or prompting? Say hello to 🎤 yo-Llama 🦙!
Model https://huggingface.co/anakin87/yo-Llama-3-8B-Instruct
This experiment steers Llama-3-8B-Instruct to respond in a rap style.
How? Amplifying the rap direction in the activation space. 😎
𝐖𝐡𝐚𝐭 𝐬𝐩𝐚𝐫𝐤𝐞𝐝 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚?
Lately, I got interested in mechanistic interpretability of LLMs.
💡 A recent paper, "Refusal in Language Models Is Mediated by a Single Direction," showed how to find the refusal direction in the activation space of Chat Language Models and either erase or amplify it.
A clever jailbreak method for open weights models.
Then, @failspy took it a step further by modifying the models to amplify different traits, such as making a model seem grumpy or irritable.
𝐇𝐨𝐰 𝐝𝐢𝐝 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞 𝐲𝐨-𝐋𝐥𝐚𝐦𝐚?
(📓 notebook in the HF repository, heavily inspired by Failspy's work)
1️⃣ Load the Llama-3-8B-Instruct model.
2️⃣ Load 1024 examples from Alpaca (instruction dataset).
3️⃣ Prepare a system prompt to make the original model act like a rapper.
4️⃣ Run inference on the examples, with and without the system prompt, and cache the activations.
5️⃣ Compute the rap feature directions (one for each layer) from the activations.
6️⃣ Apply the feature directions one by one, checking the results on some examples.
7️⃣ Pick the best-performing feature direction.
8️⃣ Apply this feature direction and voilà!
yo-Llama-3-8B-Instruct is born! 🥳🎶
This was a fun experiment.
📚 Resources
Refusal in Language Models Is Mediated by a Single Direction - https://arxiv.org/abs/2406.11717
Uncensor any LLM with abliteration: great practical blog post by @mlabonne https://huggingface.co/blog/mlabonne/abliteration
Practical materials by @failspy
- abliterator library https://github.com/FailSpy/abliterator
- Llama-MopeyMule-3-8B-Instruct model (+ notebook) https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule | {
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] | 🚀 Llama-3-ELYZA-JP-8B
ELYZA, Inc. has developed two large language models (LLMs) for Japanese called "Llama-3-ELYZA-JP-70B" with 70 billion parameters and "Llama-3-ELYZA-JP-8B" with 8 billion parameters, based on Meta's "Llama 3" series. These models have been fine-tuned through additional pre-training and post-training to improve Japanese language capabilities significantly.
Key Points:
Performance:
- Llama-3-ELYZA-JP-70B surpasses global models such as GPT-4, Claude 3 Sonnet, and Gemini 1.5 Flash.
- Llama-3-ELYZA-JP-8B matches models like GPT-3.5 Turbo and Claude 3 Haiku despite having fewer parameters.
Availability:
- The 8B model is available on Hugging Face Hub and can be used for both research and commercial purposes under the Llama 3 Community License.
Methodology:
- ELYZA enhanced the Japanese performance of the Llama 3 models through additional training with high-quality Japanese corpora and Instruction Tuning with proprietary datasets.
Benchmarks:
- Evaluations using ELYZA Tasks 100 and Japanese MT-Bench showed significant improvements in Japanese language generation.
Inference Speed:
- To address inference speed issues due to model size, ELYZA implemented Speculative Decoding, which achieved up to 1.6 times faster inference for the 70B model.
Overall, ELYZA's models demonstrate state-of-the-art performance in Japanese language tasks and are optimized for both efficiency and effectiveness.
Model URL:
- https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B
- https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-AWQ
- https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-GGUF
Blog post (in Japanese):
https://note.com/elyza/n/n360b6084fdbd | {
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] | 📢 I've tested google/Gemma-2-9b-it in Target Sentiment Analysis (TSA), in zero-shot learning mode on RuSentNE-2023 dataset with texts translated into English (🇺🇸).
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Model: https://huggingface.co/google/gemma-2-9b-it
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] | 📅 Remember when at the beginning of the year @Google gave an update on knowledge distillation! Introducing a way of learning from Self-Generated Mistakes?
📊 Resulting in significant improvements across tasks:
- 📄 2.1x in summarization
- 🌐 1.7x in translation
- 🧠 1.9x in reasoning tasks
🚀 Well, it looks like Google wasn't messing around! According to the Gemma 2 tech report, knowledge distillation was used to pre-train the 9B model, while the 27B model was pre-trained from scratch.
📈 For post-training, the Gemma 2 team generated completions from a stronger teacher model (unspecified in the report, but presumably Gemini Ultra), and then trained the student models on this synthetic data with SFT. This is quite common as seen in many open models, such as Zephyr and OpenHermes.
🤔 Sounds too good to be true? These models suffer from distribution mismatch between output sequences seen during training and those generated by the student during inference.
📰 This is where the January 2024 paper "On-Policy Distillation of Language Models" comes in...
🔍 Gemma 2 team used “on-policy distillation,” where the student generates completions from the SFT prompts. These completions are then used to compute the KL divergence between the teacher’s and student’s logits. By minimizing the KL divergence throughout training, the student learns to model the behavior of the teacher accurately while also minimizing the train-inference mismatch.
📚 Gem🔹 of a blog by @huggingface uncovering everything Gemma 2: https://huggingface.co/blog/gemma2#knowledge-distillation
📄 On-Policy Distillation of Language Models: https://huggingface.co/papers/2306.13649 | {
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A recent supply chain attack on polyfill.io affected over 100,000 websites (see https://www.patched.codes/blog/patching-the-polyfill-supply-chain-attack). To address this issue, we show how developers can leverage Large Language Models (LLMs) for efficient vulnerability patching:
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3. Custom Workflows: The "Fixpolyfill" patchflow (https://github.com/patched-codes/patchwork-configs/tree/main/patchflows/Fixpolyfill) , tailored for this specific attack, can be easily run across multiple repositories.
4. Scalable Solutions: Options to scan and patch entire GitHub/GitLab organizations, with automated pull request generation.
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] | New LoRA Model!
I trained this model on a new spot I'm really excited to share (soon!)
This Monday I will be posting my first beginning to end blog showing the tool I've used, dataset, captioning techniques, and parameters to finetune this LoRA.
For now, check out the model in the link below.
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Tutorial link : https://youtu.be/XFUZof6Skkw
It has manually written captions / subtitles and also video chapters.
If you are a GPU poor this is the video you need
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🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985
🔗 Windows Tutorial for Learn How to Use SwarmUI ➡️ https://youtu.be/HKX8_F1Er_w
🔗 How to download models very fast to Massed Compute, RunPod and Kaggle and how to upload models or files to Hugging Face very fast tutorial ➡️ https://youtu.be/X5WVZ0NMaTg
🔗 SECourses Discord ➡️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 Stable Diffusion GitHub Repo (Please Star, Fork and Watch) ➡️ https://github.com/FurkanGozukara/Stable-Diffusion
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Coupon works on Alt Config RTX A6000 and also RTX A6000 GPUs
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] | Hello!
I've been playing with Claude, and we decided to tackle a real thorn in my side.
"The Truthiness Model" - Analyze arbitrary input text for "truthiness", or likelihood of containing true information according to seed text.
P.S. Yes, v1 was broken. I saw the loss rate going down and go excited. Anyway, it just needed some data and a rollback, me and Claude got WAY too carried away trying to tack on features.
Anyway, fixed now, and working! :D
http://samuelmeyerscode.serveblog.net/?p=49 | {
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] | 🤗 Hi HF Community!
🧬 As you may now, Evolutionary Scale recently released https://huggingface.co/EvolutionaryScale/esm3-sm-open-v1 model here on the Hub, "a frontier generative model for biology, able to jointly reason across three fundamental biological properties of proteins: sequence, structure, and function" - as it is described on the dedicated GitHub page.
⚡ If you are curious about it and you want to try it out, you can do it with a space I built, https://huggingface.co/spaces/as-cle-bert/proteins-with-esm
Hope this helps with your research!🚀 | {
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Model: https://huggingface.co/google/gemma-2-9b-it
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
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] | Hi Huggingfacers!
Thrilled to introduce Adam-mini, an optimizer that achieves on-par or better performance than AdamW with 45% to 50% less memory footprint. Adam-mini can also achieve 49.5% higher throughput than AdamW on Llama2-7B pre-training.
The design of Adam-mini is inspired by certain Hessian structures we observed on Transformers.
Feel free to try it out! Try switching to Adam-mini with the same hyperparams of AdamW, it would work with only half memory. Hope Adam-mini can help save time, cost, and energy in your tasks!
Paper: "Adam-mini: Use Fewer Learning Rates To Gain More" https://arxiv.org/abs/2406.16793
Code: https://github.com/zyushun/Adam-mini
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] | I started Friday with decentralized AI using Gemma-2, and it all works without blockchain. This is what I did:
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• All my browsers
• My Copilot++ in VSCode
• My Open Apple Intelligence (OAI, not to be confused with the other closed OAI owned by a nonprofit foundation and BigTech)
The llama-ipfs server’s RPC support lets me decentralize inferencing across all my devices, supercharging computing and energy efficiency.
Make sure you own your AI. AI in the cloud is not aligned with you, it’s aligned with the company that owns it. | {
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"value": "💥 If you are Bioinformaticians or Biologists, you may be familiar with BLAST, a search algorithm that allows researchers to identify the group of organisms (species, taxa...) from which DNA/Protein sequences come.",
"raw": "💥 If you are Bioinformaticians or Biologists, you may be familiar with BLAST, a search algorithm that allows researchers to identify the group of organisms (species, taxa...) from which DNA/Protein sequences come.",
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"value": "🥱 You may also be familiar with the difficulties to interpret long and multi-parametric results coming out from BLAST searches: here's where we can operate with LLMs, summarizing the outputs and/or replying to queries about them!",
"raw": "🥱 You may also be familiar with the difficulties to interpret long and multi-parametric results coming out from BLAST searches: here's where we can operate with LLMs, summarizing the outputs and/or replying to queries about them!",
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"value": "🧬 You can now run BLAST for 16S rRNA bacterial sequences here on HF, summarizing and/or asking questions about the results, or make sense of your online BLAST searches uploading description tables, using the last space I built: ",
"raw": "🧬 You can now run BLAST for 16S rRNA bacterial sequences here on HF, summarizing and/or asking questions about the results, or make sense of your online BLAST searches uploading description tables, using the last space I built: ",
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] | Hi HuggingFacers!🤗
💥 If you are Bioinformaticians or Biologists, you may be familiar with BLAST, a search algorithm that allows researchers to identify the group of organisms (species, taxa...) from which DNA/Protein sequences come.
🥱 You may also be familiar with the difficulties to interpret long and multi-parametric results coming out from BLAST searches: here's where we can operate with LLMs, summarizing the outputs and/or replying to queries about them!
🧬 You can now run BLAST for 16S rRNA bacterial sequences here on HF, summarizing and/or asking questions about the results, or make sense of your online BLAST searches uploading description tables, using the last space I built: https://huggingface.co/spaces/as-cle-bert/BLAST-SummarAIzer
Have fun and may this be helpful to your research!💻 | {
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] | Mixture of Agents now in MLC/LMStudio/Ollama
I've been a bit obsessed with the recent MoA paper and its implementation. I've noticed a HUGE upgrade in the final output and it seems to really be a great way to harness the power of a team of different LLMs. The downside is that it can be a bit slow to generate responses with the bigger models (but worth it if you want to wait). I wanted to get faster results so I made an MLC version and it actually works out great! Much quicker and the responses definitely are better than compared to just running one.
I'm going to keep working on seeing how it can be further integrated (API endpoints, RAG, synthetic data generation, etc) and will share the stuff that I can get to work decently enough :)
https://github.com/severian42/MoA-MLC-Chat
https://github.com/severian42/MoA-Ollama-Chat
https://github.com/severian42/MoA-LMStudio-Chat | {
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] | Hello!
https://www.youtube.com/watch?v=6NyDkpfNfUs
I had some feedback recently, that perhaps it would be beneficial to expand upon the fallacy dataset. I took this deeply to heart, and exploded it 10x.
https://huggingface.co/datasets/MrOvkill/fallacies-fallacy-base
Produced synthetically with *ALL* the Gemini models on Vertex AI.
*phew* This was a rush. I can promise over 8 it might have been like 16 of straight prompt/copy/paste/fix/re-splice/fix/prompt again/chug caffeine/repeat, but we got there! Thanks for egging me on, all! I appreciate being driven to work! So much better than boredom! 🤗
Have fun!
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] | 🚀🎭🌟 New Research Alert - Portrait4D-v2 (Avatars Collection)! 🌟🎭🚀
📄 Title: Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer 🔝
📝 Description: Portrait4D-v2 is a novel method for one-shot 4D head avatar synthesis using pseudo multi-view videos and a vision transformer backbone, achieving superior performance without relying on 3DMM reconstruction.
👥 Authors: Yu Deng, Duomin Wang, and Baoyuan Wang
📄 Paper: https://huggingface.co/papers/2403.13570
🌐 GitHub Page: https://yudeng.github.io/Portrait4D-v2/
📁 Repository: https://github.com/YuDeng/Portrait-4D
📺 Video: https://www.youtube.com/watch?v=5YJY6-wcOJo
🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
📚 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
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"value": "𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐀𝐠𝐞𝐧𝐭𝐬 𝐫𝐞𝐚𝐜𝐡𝐞𝐬 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐨𝐟 𝐆𝐀𝐈𝐀 𝐥𝐞𝐚𝐝𝐞𝐫𝐛𝐨𝐚𝐫𝐝! 🥳",
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"raw": "🔢 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗹𝗼𝗴𝗶𝗰, many questions having strong math aspects",
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"value": "> \"In NASA’s Astronomy Picture of the Day on 2006 January 21, two astronauts are visible, with one appearing much smaller than the other. As of August 2023, out of the astronauts in the NASA Astronaut Group that the smaller astronaut was a member of, which one spent the least time in space, and how many minutes did he spend in space, rounded to the nearest minute?\"",
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"value": "(𝘯𝘰 𝘧𝘪𝘭𝘦 𝘢𝘵𝘵𝘢𝘤𝘩𝘦𝘥 𝘰𝘧 𝘤𝘰𝘶𝘳𝘴𝘦, 𝘵𝘩𝘦 𝘢𝘨𝘦𝘯𝘵 𝘩𝘢𝘴 𝘵𝘰 𝘧𝘪𝘯𝘥 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘪𝘯𝘧𝘰)",
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"value": "➡️ We used Transformers Agents' React Code Agent, that writes its actions in code. We created a new planning component that we'll incorporate in the framework. More info soon in a blog post!",
"raw": "➡️ We used Transformers Agents' React Code Agent, that writes its actions in code. We created a new planning component that we'll incorporate in the framework. More info soon in a blog post!",
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"value": "𝐑𝐞𝐬𝐮𝐥𝐭𝐬:",
"raw": "𝐑𝐞𝐬𝐮𝐥𝐭𝐬:",
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"value": "🚀 Our submission scores #2 overall on the test set and #1 on the validation set. On both sets we're the leading submission based on a public framework, beating Microsoft's Autogen.",
"raw": "🚀 Our submission scores #2 overall on the test set and #1 on the validation set. On both sets we're the leading submission based on a public framework, beating Microsoft's Autogen.",
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"value": "🥇 On both sets we are #1 on the hardest Level 3 questions, reaching nearly 20%.",
"raw": "🥇 On both sets we are #1 on the hardest Level 3 questions, reaching nearly 20%.",
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"value": "𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉 ",
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] | 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐀𝐠𝐞𝐧𝐭𝐬 𝐫𝐞𝐚𝐜𝐡𝐞𝐬 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐨𝐟 𝐆𝐀𝐈𝐀 𝐥𝐞𝐚𝐝𝐞𝐫𝐛𝐨𝐚𝐫𝐝! 🥳
We've been improving Transformers Agents a lot lately.
So with @sergeipetrov we set out to prove that it's the best agent framework out there.
To prove this, we went to beat the 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱, the most comprehensive benchmark out there for evaluating LLM agents.
Its questions make you explore different flavours of pain:
🛠️ 𝗥𝗲𝗾𝘂𝗶𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀, at least a web browser
🔢 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗹𝗼𝗴𝗶𝗰, many questions having strong math aspects
🖼️ 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹, the agent had to handle all file types: 🔊, 🖼️, 🎬...
👣 𝗠𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽, with many questions requiring over 10 steps to be solved.
Some Level 3 questions are crazy hard 😳
> "In NASA’s Astronomy Picture of the Day on 2006 January 21, two astronauts are visible, with one appearing much smaller than the other. As of August 2023, out of the astronauts in the NASA Astronaut Group that the smaller astronaut was a member of, which one spent the least time in space, and how many minutes did he spend in space, rounded to the nearest minute?"
(𝘯𝘰 𝘧𝘪𝘭𝘦 𝘢𝘵𝘵𝘢𝘤𝘩𝘦𝘥 𝘰𝘧 𝘤𝘰𝘶𝘳𝘴𝘦, 𝘵𝘩𝘦 𝘢𝘨𝘦𝘯𝘵 𝘩𝘢𝘴 𝘵𝘰 𝘧𝘪𝘯𝘥 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘪𝘯𝘧𝘰)
➡️ We used Transformers Agents' React Code Agent, that writes its actions in code. We created a new planning component that we'll incorporate in the framework. More info soon in a blog post!
𝐑𝐞𝐬𝐮𝐥𝐭𝐬:
🚀 Our submission scores #2 overall on the test set and #1 on the validation set. On both sets we're the leading submission based on a public framework, beating Microsoft's Autogen.
🥇 On both sets we are #1 on the hardest Level 3 questions, reaching nearly 20%.
𝙂𝙤 𝙘𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙡𝙚𝙖𝙙𝙚𝙧𝙗𝙤𝙖𝙧𝙙 👉 https://huggingface.co/spaces/gaia-benchmark/leaderboard | {
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] | I am delighted to announce the publication of my LegalKit, a French labeled dataset built for legal ML training 🤗
This dataset comprises multiple query-document pairs (+50k) curated for training sentence embedding models within the domain of French law.
The labeling process follows a systematic approach to ensure consistency and relevance:
- Initial Query Generation: Three instances of the LLaMA-3-70B model independently generate three different queries based on the same document.
- Selection of Optimal Query: A fourth instance of the LLaMA-3-70B model, using a dedicated selection prompt, evaluates the generated queries and selects the most suitable one.
- Final Label Assignment: The chosen query is used to label the document, aiming to ensure that the label accurately reflects the content and context of the original text.
Dataset: https://huggingface.co/datasets/louisbrulenaudet/legalkit
Stay tuned for further updates and release information 🔥
@clem, if we can create an "HF for Legal" organization, similar to what exists for journalists, I am available!
Note : My special thanks to @alvdansen for their illustration models ❤️ | {
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I don't agree with some of the assertions made here, but it is an interesting paper and a good overview.
https://arxiv.org/abs/2401.13142 | {
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] | Per popular request, I'm working on a beginning to end LoRA training workflow blog for a style.
It will focus on dataset curation through training on a pre-determined style to give a better insight on my process.
Curious what are some questions you might have that I can try to answer in it? | {
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] | It is time for some Aura.
First in our series of fully open sourced / commercially available models by @fal-ai: AuraSR - a 600M parameter upscaler based on GigaGAN.
Blog: https://blog.fal.ai/introducing-aurasr-an-open-reproduction-of-the-gigagan-upscaler-2/
HF: https://huggingface.co/fal-ai/AuraSR
Code: https://github.com/fal-ai/aura-sr
Playground: https://fal.ai/models/fal-ai/aura-sr/playground
What other models would you like to see open-sourced and commercially available? :)
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Actual benchmarks have become too easy for recent models, much like grading high school students on middle school problems makes little sense. So the team worked on a new version of the Open LLM Leaderboard with new benchmarks.
Stellar work from @clefourrier @SaylorTwift and the team!
👉 Read the blog post: https://huggingface.co/spaces/open-llm-leaderboard/blog
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] | 🌌 Creating adventures with local LLMs
What if 🤔... Homer Simpson met Spider-Man and they went on a quest for donuts? 🍩
Or if Fred Astaire and Corporal Hicks teamed up to fight xenomorphs? 👾
In the words of Karpathy, LLMs are dream machines...
they seem specially made to simulate these wild scenarios!
𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐢𝐝𝐞𝐚 👇
Nous Research / @teknium recently released https://huggingface.co/datasets/NousResearch/CharacterCodex:
a massive dataset with information on 16k characters, both fictional and real.
I couldn't wait to play it...
After a few attempts, I found that combining the information in this dataset with a good model (like https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) opens the doors to a myriad of chat adventures.
🛠️ Stack:
🔹Haystack for orchestration 🏗️
🔹llamafile 🦙🗂️ to run our model locally.
📓 Check out the notebook: https://t.ly/y6jrZ
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I'm Alex, I'm 16, I've been an internship at Hugging Face for a little over a week and I've already learned a lot about using and prompting LLM models. With @victor as tutor I've just finished a space that analyzes your feelings by prompting an LLM chat model. The aim is to extend it so that it can categorize hugging face posts.
https://huggingface.co/spaces/alex-abb/LLM_Feeling_Analyzer
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] | Hello!
I've been in the lab playing with various data formats today, and jammed out with some plain text to produce a nice list of fallacies and their solutions from Wikipedia's List of fallacies as JSONL for data processing.
Had some bumps along the way, but me and Gemini 1.5 Pro got there in the end. I must really learn to work with Gemini 1.5 Flash more effectively in future.
https://huggingface.co/datasets/MrOvkill/fallacies-list-wikipedia
Enjoy!
```
-<3
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] | Looking to move away from OpenAI's closed-source models?
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Check out the docs here: https://postgresml.org/docs/guides/opensourceai
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I wanted to thank everyone that read my blogpost and I am glad to share that we have achieved 11000 readers 🥳
I couldn't have done this without you, so once again thanks a lot everyone for the support 💖
If you haven't already you can read my blog post at: https://huggingface.co/blog/not-lain/rag-chatbot-using-llama3 | {
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Join the Aya Discord: https://discord.com/invite/q9QRYkjpwk
Visit the Expedition Aya Minisite: https://sites.google.com/cohere.com/expedition-aya/home
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Imagine an open-source AI that's as decentralized as a potluck dinner - everyone brings something to the table, and there's ZERO need for blockchain. It's like a digital fortress, with security and privacy baked right in, not to mention a dollop of integrity and trust. This could be the secret sauce for an enterprise AI platform, complete with an integrated IT policy. It might just be the cherry on top for the next generation of Apple Intelligence and Copilot+ PCs.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it. | {
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] | NEW #DecentralizeAI Writing Contest, by InternetComputer.org and HackerNoon.com! 😜 https://www.contests.hackernoon.com/decentralize-ai-writing-contest 🤪
"Not going to beat centralized AI with more centralized AI." - Emad Mostaque
To enter, submit a blog post with the #decentralize-ai tag on HackerNoon. | {
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] | Use GPT-4o + GPT-4-Turbo-Preview + GPT-3.5-Turbo + BingAI
https://huggingface.co/spaces/NiansuhAI/Copilot | {
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