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AI everyday

ai-everyday
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Reacted to fdaudens's post with 👍 about 14 hours ago
Just watched @thomwolf tear down the over-hyped AGI narrative in 30 seconds - and it's refreshingly grounded. No wild speculation about superintelligence timelines or consciousness. Just practical insights from someone who really understands the technology. This is the kind of level-headed perspective that helps us focus on what AI can actually do today (which is already transformative) rather than getting lost in AGI fantasy. Worth your time if you want to understand AI progress without the hype. Watch the full interview at CogX here: https://www.youtube.com/watch?v=IjL_6Th6Ea0
Reacted to Taylor658's post with 👀 about 14 hours ago
The Mystery Bot 🕵️‍♂️ saga I posted about from earlier this week has been solved...🤗 Cohere for AI has just announced its open source Aya Expanse multilingual model. The Initial release supports 23 languages with more on the way soon.🌌 🌍 You can also try Aya Expanse via SMS on your mobile phone using the global WhatsApp number or one of the initial set of country specific numbers listed below.⬇️ 🌍WhatsApp - +14313028498 Germany - (+49) 1771786365 USA – +18332746219 United Kingdom — (+44) 7418373332 Canada – (+1) 2044107115 Netherlands – (+31) 97006520757 Brazil — (+55) 11950110169 Portugal – (+351) 923249773 Italy – (+39) 3399950813 Poland - (+48) 459050281
Reacted to tomaarsen's post with 🔥 about 14 hours ago
I just released Sentence Transformers v3.3.0 & it's huge! 4.5x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free perf. boost, PEFT integration, evaluation on NanoBEIR, and more! Details: 1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new `export_static_quantized_openvino_model` method to quantize a model. 2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the `prompts` argument in `SentenceTransformerTrainingArguments`. Our experiments show that you can easily reach 0.66% to 0.90% relative performance improvement on NDCG@10 at no extra cost by adding "query: " before each training query and "document: " before each training answer. 3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model. 4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks. Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0
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Reacted to fdaudens's post with 👍 about 14 hours ago
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1355
Just watched @thomwolf tear down the over-hyped AGI narrative in 30 seconds - and it's refreshingly grounded.

No wild speculation about superintelligence timelines or consciousness. Just practical insights from someone who really understands the technology.

This is the kind of level-headed perspective that helps us focus on what AI can actually do today (which is already transformative) rather than getting lost in AGI fantasy. Worth your time if you want to understand AI progress without the hype.

Watch the full interview at CogX here: https://www.youtube.com/watch?v=IjL_6Th6Ea0
Reacted to Taylor658's post with 👀 about 14 hours ago
view post
Post
2203
The Mystery Bot 🕵️‍♂️ saga I posted about from earlier this week has been solved...🤗

Cohere for AI has just announced its open source Aya Expanse multilingual model. The Initial release supports 23 languages with more on the way soon.🌌 🌍

You can also try Aya Expanse via SMS on your mobile phone using the global WhatsApp number or one of the initial set of country specific numbers listed below.⬇️

🌍WhatsApp - +14313028498
Germany - (+49) 1771786365
USA – +18332746219
United Kingdom — (+44) 7418373332
Canada – (+1) 2044107115
Netherlands – (+31) 97006520757
Brazil — (+55) 11950110169
Portugal – (+351) 923249773
Italy – (+39) 3399950813
Poland - (+48) 459050281
  • 1 reply
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Reacted to tomaarsen's post with 🔥 about 14 hours ago
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4788
I just released Sentence Transformers v3.3.0 & it's huge! 4.5x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free perf. boost, PEFT integration, evaluation on NanoBEIR, and more! Details:

1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new export_static_quantized_openvino_model method to quantize a model.

2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the prompts argument in SentenceTransformerTrainingArguments. Our experiments show that you can easily reach 0.66% to 0.90% relative performance improvement on NDCG@10 at no extra cost by adding "query: " before each training query and "document: " before each training answer.

3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model.

4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks.

Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0
upvoted 2 collections about 18 hours ago
liked 2 models about 18 hours ago
liked a Space about 18 hours ago
Reacted to prithivMLmods's post with 🔥 about 18 hours ago
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1630
Animaker / Anime Engine : 🤗

Only on🚀: https://huggingface.co/strangerzonehf

Demo: prithivMLmods/FLUX-LoRA-DLC

Adapters:
->Model 1: strangerzonehf/Flux-Animeo-v1-LoRA
->Model 2: strangerzonehf/Flux-Animex-v2-LoRA

Collections:
->Flux LoRA Collection: prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
->Stranger Zones: prithivMLmods/stranger-zone-collections-6737118adcf2cb40d66d0c7e
->LoRA Spaces: prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32
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@prithivMLmods
  • 1 reply
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Reacted to Jaward's post with 🔥 about 18 hours ago
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2088
It's work like this that in some way signal the eventual “dominance” of AI over all the sciences.

“We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement outputs”

The emulator is capable of predicting
the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯
  • 1 reply
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liked a model about 19 hours ago
upvoted a paper about 19 hours ago
Reacted to AdinaY's post with 🚀 about 19 hours ago
Reacted to Symbol-LLM's post with 🔥 about 19 hours ago
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839
🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !

📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection

🔗 Link: Vision-Language Models Can Self-Improve Reasoning via Reflection (2411.00855)

😇Takeaways:

- We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.

- Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !
Reacted to hexgrad's post with 👍 about 19 hours ago
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837
hexgrad/Kokoro-TTS just got an upgrade that substantially improves TTS naturalness for short bursts while maintaining parity for longer utterances! 🔥

Read more and listen to before/after audio samples at https://hf.co/blog/hexgrad/kokoro-short-burst-upgrade

(Probably would have made that Article a Post instead, if audio could be embedded into Posts.)
  • 1 reply
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Reacted to merve's post with 🚀 about 19 hours ago
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2042
your hugging face profile now has your recent activities 🤗
Reacted to merve's post with 🔥 7 days ago
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4719
OmniVision-968M: a new local VLM for edge devices, fast & small but performant
💨 a new vision language model with 9x less image tokens, super efficient
📖 aligned with DPO for reducing hallucinations
⚡️ Apache 2.0 license 🔥

Demo hf.co/spaces/NexaAIDev/omnivlm-dpo-demo
Model NexaAIDev/omnivision-968M
  • 4 replies
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liked a Space 7 days ago
Reacted to hexgrad's post with 🔥 7 days ago
Reacted to rwitz's post with ❤️ 13 days ago
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2663
Pleased to announce Cat1.0, the newest iteration of my roleplay fine-tunes! rwitz/cat1.0
The model is fine-tuned from Llama-3.1 8B on VERY high quality roleplay chat logs, each stretching for thousands of tokens. Also excels at logic, especially in conversational reasoning! Feel free to give it a test!
  • 2 replies
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