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qubvel-hfΒ  new activity 7 days ago
hf-vision/object_detection_leaderboard:🚩 Leaderboard down!
qubvel-hfΒ  new activity 7 days ago
hf-vision/finetuning_demo_for_object_detection:Update README.md
qubvel-hfΒ  updated a Space 7 days ago
hf-vision/finetuning_demo_for_object_detection
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merveΒ 
posted an update about 13 hours ago
merveΒ 
posted an update 7 days ago
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2278
Aya by Cohere For AI can now see! πŸ‘€

C4AI community has built Maya 8B, a new open-source multilingual VLM built on SigLIP and Aya 8B 🌱 works on 8 languages! πŸ—£οΈ

The authors extend Llava dataset using Aya's translation capabilities with 558k examples!
ry it here kkr5155/maya_demo

Dataset maya-multimodal/pretrain

Model maya-multimodal/maya πŸ‘
kudos @nahidalam and team
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Β·
qubvel-hfΒ 
in hf-vision/object_detection_leaderboard 7 days ago

🚩 Leaderboard down!

1
#4 opened 29 days ago by
alvarobartt
qubvel-hfΒ 
in hf-vision/finetuning_demo_for_object_detection 7 days ago

Update README.md
#1 opened 7 days ago by
qubvel-hf
qubvel-hfΒ 
updated a Space 7 days ago
qubvel-hfΒ 
in hf-vision/nougat-transformers 7 days ago

Update README.md
#3 opened 7 days ago by
qubvel-hf
qubvel-hfΒ 
updated a Space 7 days ago
merveΒ 
posted an update 8 days ago
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2892
Apollo is a new family of open-source video language models by Meta, where 3B model outperforms most 7B models and 7B outperforms most 30B models 🧢

✨ the models come in 1.5B https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32, 3B https://huggingface.co/Apollo-LMMs/Apollo-3B-t32 and 7B https://huggingface.co/Apollo-LMMs/Apollo-7B-t32 with A2.0 license, based on Qwen1.5 & Qwen2
✨ the authors also release a benchmark dataset https://huggingface.co/spaces/Apollo-LMMs/ApolloBench

The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work ⏯️

Try the demo for best setup here https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled πŸ“ˆ scaling dataset has diminishing returns for smaller models
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
they find google/siglip-so400m-patch14-384 to be most powerful πŸ”₯
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield

They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models πŸ”₯
  • 3 replies
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asusevskiΒ 
authored a paper 8 days ago
celinahΒ 
posted an update 9 days ago
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566
πŸš€ We've just dropped a new release v0.27.0 of the πš‘πšžπšπšπš’πš—πšπšπšŠπšŒπšŽ_πš‘πšžπš‹ Python library!

This release includes:
- πŸ’Ύ New torch model loading utilities in the serialization module β€” providing a standardized way to save and load torch models with built-in support for sharding and safe serialization.
- πŸ“¦ Tooling for something exciting β€” if you like single-file formats for models like GGUF, you'll love what we're cooking up πŸ‘€ More coming soon!
- πŸ› οΈ Loads of quality-of-life improvements and bug fixes!

release notes and full details here πŸ‘‡
Wauplin/huggingface_hub#10

$ pip install -U huggingface_hub
merveΒ 
posted an update 13 days ago
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1664
A complete RAG pipeline includes a reranker, which ranks the documents to find the best document πŸ““
Same goes for multimodal RAG, multimodal rerankers which we can integrate to multimodal RAG pipelines!
Learn how to build a complete multimodal RAG pipeline with vidore/colqwen2-v1.0 as retriever, lightonai/MonoQwen2-VL-v0.1 as reranker, Qwen/Qwen2-VL-7B-Instruct as VLM in this notebook that runs on a GPU as small as L4 πŸ”₯ https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_reranker_and_vlms
SuryaKrishna02Β 
authored a paper 14 days ago
merveΒ 
posted an update 17 days ago
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5501
This week in open-source AI was insane 🀠 A small recapπŸ•ΊπŸ» merve/dec-6-releases-67545caebe9fc4776faac0a3

Multimodal πŸ–ΌοΈ
> Google shipped a PaliGemma 2, new iteration of PaliGemma with more sizes: 3B, 10B and 28B, with pre-trained and captioning variants πŸ‘
> OpenGVLab released InternVL2, seven new vision LMs in different sizes, with sota checkpoint with MIT license ✨
> Qwen team at Alibaba released the base models of Qwen2VL models with 2B, 7B and 72B ckpts

LLMs πŸ’¬
> Meta released a new iteration of Llama 70B, Llama3.2-70B trained further
> EuroLLM-9B-Instruct is a new multilingual LLM for European languages with Apache 2.0 license πŸ”₯
> Dataset: CohereForAI released GlobalMMLU, multilingual version of MMLU with 42 languages with Apache 2.0 license
> Dataset: QwQ-LongCoT-130K is a new dataset to train reasoning models
> Dataset: FineWeb2 just landed with multilinguality update! πŸ”₯ nearly 8TB pretraining data in many languages!

Image/Video Generation πŸ–ΌοΈ
> Tencent released HunyuanVideo, a new photorealistic video generation model
> OminiControl is a new editing/control framework for image generation models like Flux

Audio πŸ”Š
> Indic-Parler-TTS is a new text2speech model made by community
merveΒ 
posted an update 18 days ago
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New InternVL drop with a state-of-the-art 78B vision language model with MIT license πŸ”₯ https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
The release comes with seven new vision LMs based on InternViT 300M/6B and Qwen2.5 (0.5B, 3B, 32B, 72B) and InternLM2 (8B, 7B, 20B) in different sizes
78B model is of InternViT 6B and Qwen2.5-72B Instruct, can accomplish variety of tasks πŸ‘ Try here OpenGVLab/InternVL
mmhamdyΒ 
authored a paper 20 days ago
lunarfluΒ 
posted an update 20 days ago
Taylor658Β 
posted an update 23 days ago
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432
🌐 The Stanford Institute for Human-Centered AI (https://aiindex.stanford.edu/vibrancy/) has released its 2024 Global AI Vibrancy Tool, a way to explore and compare AI progress across 36 countries.

πŸ“Š It measures progress across the 8 broad pillars of R&D, Responsible AI, Economy, Education, Diversity, Policy and Governance, Public Opinion and Infrastructure. (Each of these pillars have a number of Sub Indices)

πŸ“ˆ As a whole it is not surprising that the USA was at the top in terms of overall score as of 2023 (AI investment activity is a large part of the economic pillar for example and that is a large part of the overall USA ranking) but drilling in to more STRATEGIC Macro pillars like Education, Infrastructure or R&D reveal interesting growth patterns in Asia (particularly China) and Western Europe that I suspect the 2024 metrics will bear out.

πŸ€– Hopefully the 2024 Global Vibrancy ranking will break out AI and ML verticals like Computer Vision or NLP and or the AI Agent space as that may also from a global macro level give indications of what is to come globally for AI in 2025.
merveΒ 
posted an update 23 days ago
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small but mighty πŸ”₯
you can fine-tune SmolVLM on an L4 with batch size of 4 and it will only take 16.4 GB VRAM 🫰🏻 also with gradient accumulation simulated batch size is 16 ✨
I made a notebook that includes all the goodies: QLoRA, gradient accumulation, gradient checkpointing with explanations on how they work πŸ’ https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
merveΒ 
posted an update 23 days ago
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2862
Last week we were blessed with open-source models! A recap πŸ’
merve/nov-29-releases-674ccc255a57baf97b1e2d31

πŸ–ΌοΈ Multimodal
> At Hugging Face we released SmolVLM, a performant and efficient smol vision language model πŸ’—
> Show Lab released ShowUI-2B: new vision-language-action model to build GUI/web automation agents πŸ€–
> Rhymes AI has released the base model of Aria: Aria-Base-64K and Aria-Base-8K with their respective context length
> ViDoRe team released ColSmolVLM: A new ColPali-like retrieval model based on SmolVLM
> Dataset: Llava-CoT-o1-Instruct: new dataset labelled using Llava-CoT multimodal reasoning modelπŸ“–
> Dataset: LLaVA-CoT-100k dataset used to train Llava-CoT released by creators of Llava-CoT πŸ“•

πŸ’¬ LLMs
> Qwen team released QwQ-32B-Preview, state-of-the-art open-source reasoning model, broke the internet πŸ”₯
> AliBaba has released Marco-o1, a new open-source reasoning model πŸ’₯
> NVIDIA released Hymba 1.5B Base and Instruct, the new state-of-the-art SLMs with hybrid architecture (Mamba + transformer)

⏯️ Image/Video Generation
> Qwen2VL-Flux: new image generation model based on Qwen2VL image encoder, T5 and Flux for generation
> Lightricks released LTX-Video, a new DiT-based video generation model that can generate 24 FPS videos at 768x512 res ⏯️
> Dataset: Image Preferences is a new image generation preference dataset made with DIBT community effort of Argilla 🏷️

Audio
> OuteAI released OuteTTS-0.2-500M new multilingual text-to-speech model based on Qwen-2.5-0.5B trained on 5B audio prompt tokens
merveΒ 
posted an update 28 days ago
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The authors of ColPali trained a retrieval model based on SmolVLM 🀠 vidore/colsmolvlm-alpha
TLDR;

- ColSmolVLM performs better than ColPali and DSE-Qwen2 on all English tasks

- ColSmolVLM is more memory efficient than ColQwen2 πŸ’—