Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering
Paper
•
2508.04683
•
Published
AI & ML interests
Building interactive demos to scikit-learn examples 🧡
1024m
authored
a
paper
1 day ago
Post
2151
GPT-4.1-mini level model right in your iPhone 🤯
openbmb/MiniCPM-V-4 is only 4B while surpassing GPT-4.1-mini in vision benchmarks 🔥
allows commercial use as well!
openbmb/MiniCPM-V-4 is only 4B while surpassing GPT-4.1-mini in vision benchmarks 🔥
allows commercial use as well!
1024m
authored
a
paper
3 days ago
Post
908
we're all sleeping on this OCR model
rednote-hilab/dots.ocr 🔥
dots.ocr is a new 3B model with sota performance, support for 100 languages & allowing commercial use! 🤯
single e2e model to extract image, convert tables, formula, and more into markdown 📝
try it MohamedRashad/Dots-OCR
dots.ocr is a new 3B model with sota performance, support for 100 languages & allowing commercial use! 🤯
single e2e model to extract image, convert tables, formula, and more into markdown 📝
try it MohamedRashad/Dots-OCR
Post
514
massive releases and tons of Flux 1. Krea LoRas past week!
here's some of the picks, find more models in collection 🫡 merve/releases-august-2-6890c14248203522b7d0267f
LLMs 💬
> Tencent dropped tencent/Hunyuan-7B-Instruct
> Qwen released Qwen/Qwen3-Coder-30B-A3B-Instruct, 30B MoE with 3B params for coding (OS)
vision/multimodal
> RedNote released rednote-hilab/dots.ocr - 3B OCR model (OS)
> Cohere released CohereLabs/command-a-vision-07-2025 - 112B (dense!) VLM for 6 languages
> StepFun-AI shipped stepfun-ai/step3 - 321B MoE VLM (OS)
> Skywork shipped Skywork/Skywork-UniPic-1.5B - new any-to-any model (image+text → image+text) (OS)
here's some of the picks, find more models in collection 🫡 merve/releases-august-2-6890c14248203522b7d0267f
LLMs 💬
> Tencent dropped tencent/Hunyuan-7B-Instruct
> Qwen released Qwen/Qwen3-Coder-30B-A3B-Instruct, 30B MoE with 3B params for coding (OS)
vision/multimodal
> RedNote released rednote-hilab/dots.ocr - 3B OCR model (OS)
> Cohere released CohereLabs/command-a-vision-07-2025 - 112B (dense!) VLM for 6 languages
> StepFun-AI shipped stepfun-ai/step3 - 321B MoE VLM (OS)
> Skywork shipped Skywork/Skywork-UniPic-1.5B - new any-to-any model (image+text → image+text) (OS)
Post
3105
🫡 I am the first and only one to like the French Tax Code Dataset
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
- 2 replies
Post
2107
Cohere just dropped
CohereLabs/command-a-vision-07-2025, a 112B (dense!) vision LM
> based on SigLIP2 & Command-A
> built for enterprise use cases 🔥
> use with Inference Providers or transformers 🤗
read their blog https://huggingface.co/blog/CohereLabs/introducing-command-a-vision-07-2025
> based on SigLIP2 & Command-A
> built for enterprise use cases 🔥
> use with Inference Providers or transformers 🤗
read their blog https://huggingface.co/blog/CohereLabs/introducing-command-a-vision-07-2025
- 2 replies
Post
3519
past week in open AI was insane 🔥 here's some of picks, find more here
merve/releases-july-25-688768ca47fe3693407e02d1
💬 LLMs & VLMs
> Qwen/Qwen3-235B-A22B-Thinking-2507 had a new update (OS)
> Qwen/Qwen3-Coder-480B-A35B-Instruct is out with 480B total 35B active params 🤯 (OS)
> AllenAI dropped an update to allenai/olmOCR-7B-0725 📝
> InternLM released internlm/Intern-S1 - 235B Qwen3 MoE + 6B InternViT encoder (OS)
> OmniSVG/OmniSVG is a new SVG generation VLM (OS)
🖼️ image/video/3D generation
> WanAI released Wan2.2 series - both T2V and I2V 14B models for high-quality video generation (OS) multimodalart/wan-22-688767e313337b434ed55112
> Tencent dropped tencent/HunyuanWorld-1 - image-to-3D scene generation model
💬 LLMs & VLMs
> Qwen/Qwen3-235B-A22B-Thinking-2507 had a new update (OS)
> Qwen/Qwen3-Coder-480B-A35B-Instruct is out with 480B total 35B active params 🤯 (OS)
> AllenAI dropped an update to allenai/olmOCR-7B-0725 📝
> InternLM released internlm/Intern-S1 - 235B Qwen3 MoE + 6B InternViT encoder (OS)
> OmniSVG/OmniSVG is a new SVG generation VLM (OS)
🖼️ image/video/3D generation
> WanAI released Wan2.2 series - both T2V and I2V 14B models for high-quality video generation (OS) multimodalart/wan-22-688767e313337b434ed55112
> Tencent dropped tencent/HunyuanWorld-1 - image-to-3D scene generation model
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- 1 reply
Post
4321
🤯 241B VLM with apache-2.0 license
internlm/Intern-S1
internlm released Intern-S1: multimodal reasoning model based on 235B MoE Qwen3 and 6B InternViT 😍
benchmarks look great (👑 best model ✅ best open model)
internlm released Intern-S1: multimodal reasoning model based on 235B MoE Qwen3 and 6B InternViT 😍
benchmarks look great (👑 best model ✅ best open model)
Post
782
so many open LLMs and image LoRAs dropped past week, here's some picks for you 🫡
merve/releases-july-18-687e3fbd2ab9b39c51f9238b
LLMs
> ByteDance released a bunch of translation models called Seed-X-RM (7B) ByteDance-Seed/Seed-X-RM-7B
> NVIDIA released reasoning models of which 32B surpassing the giant Qwen3-235B with cc-by-4.0 license 👏 nvidia/openreasoning-nemotron-687730dae0170059860f1f01
> LG released a new EXAONE model (32B) LGAI-EXAONE/EXAONE-4.0-32B
VLMs/any-to-any
> vidore/colqwen-omni-v0.1 is a new any-to-any retriever (MIT)
> HiDream-ai/HiDream-E1-1 is image+text in image+text out model (MIT)
LoRAs
> There's a bunch of LoRAs based on Flux Kontext, gotta check out the collection 🤠
LLMs
> ByteDance released a bunch of translation models called Seed-X-RM (7B) ByteDance-Seed/Seed-X-RM-7B
> NVIDIA released reasoning models of which 32B surpassing the giant Qwen3-235B with cc-by-4.0 license 👏 nvidia/openreasoning-nemotron-687730dae0170059860f1f01
> LG released a new EXAONE model (32B) LGAI-EXAONE/EXAONE-4.0-32B
VLMs/any-to-any
> vidore/colqwen-omni-v0.1 is a new any-to-any retriever (MIT)
> HiDream-ai/HiDream-E1-1 is image+text in image+text out model (MIT)
LoRAs
> There's a bunch of LoRAs based on Flux Kontext, gotta check out the collection 🤠
Post
2786
Now it's possible to do RAG with any-to-any models 🔥
Learn how to search in a video dataset and generate using Tevatron/OmniEmbed-v0.1-multivent an all modality retriever, and Qwen/Qwen2.5-Omni-7B, any-to-any model in this notebook 🤝 merve/smol-vision
Learn how to search in a video dataset and generate using Tevatron/OmniEmbed-v0.1-multivent an all modality retriever, and Qwen/Qwen2.5-Omni-7B, any-to-any model in this notebook 🤝 merve/smol-vision
Post
696
👋 Hey there folks,
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
Post
540
🙋🏻♂️ Hey there folks ,
Yesterday , Nvidia released a reasoning model that beats o3 on science, math and coding !
Today you can try it out here : Tonic/Nvidia-OpenReasoning
hope you like it !
Yesterday , Nvidia released a reasoning model that beats o3 on science, math and coding !
Today you can try it out here : Tonic/Nvidia-OpenReasoning
hope you like it !
Post
2055
all modality RAG 🔥
ColQwen-Omni is a new multimodal retrieval model that can retrieve anything (videos, audios, documents and more!)
use with transformers 🤗
read the blog https://huggingface.co/blog/manu/colqwen-omni-omnimodal-retrieval
model repository vidore/colqwen-omni-v0.1
ColQwen-Omni is a new multimodal retrieval model that can retrieve anything (videos, audios, documents and more!)
use with transformers 🤗
read the blog https://huggingface.co/blog/manu/colqwen-omni-omnimodal-retrieval
model repository vidore/colqwen-omni-v0.1
Post
2607
Fine-tune Gemma3n on videos with audios inside with Colab A100 🔥
Just dropped the notebook where you can learn how to fine-tune Gemma3n on images+audio+text at the same time!
keep in mind, it's made for educational purposes 🫡 we do LoRA, audio resampling & video downsampling to be able to train <40GB VRAM
stretch modalities and unfreeze layers as you wish! 🙏🏻 merve/smol-vision
Just dropped the notebook where you can learn how to fine-tune Gemma3n on images+audio+text at the same time!
keep in mind, it's made for educational purposes 🫡 we do LoRA, audio resampling & video downsampling to be able to train <40GB VRAM
stretch modalities and unfreeze layers as you wish! 🙏🏻 merve/smol-vision
- 1 reply
Post
2435
past week had huuuge releases 💗
here's our picks 🔥 find more models, datasets, demos here merve/releases-july-11-68750452c358c98b0fa663f7
> moonshotai/Kimi-K2-Instruct is the new sota LLM with 1T total 32B active parameters 🤯
> HuggingFaceTB/SmolLM3-3B is the new best LM for it's size, offers thinking mode 💭 as well as the dataset HuggingFaceTB/smoltalk2
> Alibaba-NLP/WebSailor-3B is the new agentic LLM for complex browsing
> Google DeepMind released medical vision LMs with an agentic doctor-patient app google/medgemma-release-680aade845f90bec6a3f60c4
> fal released a LoRA to improve details on face images fal/Realism-Detailer-Kontext-Dev-LoRA
here's our picks 🔥 find more models, datasets, demos here merve/releases-july-11-68750452c358c98b0fa663f7
> moonshotai/Kimi-K2-Instruct is the new sota LLM with 1T total 32B active parameters 🤯
> HuggingFaceTB/SmolLM3-3B is the new best LM for it's size, offers thinking mode 💭 as well as the dataset HuggingFaceTB/smoltalk2
> Alibaba-NLP/WebSailor-3B is the new agentic LLM for complex browsing
> Google DeepMind released medical vision LMs with an agentic doctor-patient app google/medgemma-release-680aade845f90bec6a3f60c4
> fal released a LoRA to improve details on face images fal/Realism-Detailer-Kontext-Dev-LoRA
Post
3277
🙋🏻♂️ Normalize adding compute & runtime traces to your model cards
- 2 replies
louisbrulenaudet
posted
an
update
about 1 month ago
Post
2778
Because hackathons are often the starting point for many AI projects, I've created a Python-backend template incorporating my feedback to streamline collaboration and urgent deployments 🏎️
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend
Simply issue these commands and you can ship your code at the speed of light:
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend
Simply issue these commands and you can ship your code at the speed of light:
make init
make devPost
3123
GitHub refuses to render notebooks for a long time now 💔
so smol-vision now lives in Hugging Face model repository 🤗 merve/smol-vision
so smol-vision now lives in Hugging Face model repository 🤗 merve/smol-vision
- 1 reply
Post
3451
ByteDance released Tar 1.5B and 7B: image-text in image-text out models, fully open-source 👏
ByteDance-Seed/tar-6864cf0d9fe59a3b91cc4260
They have an image tokenizer unified with text, and they de-tokenize using either of two models (LLM and diffusion)
The model is actually a full LLM (Qwen2), the tokenizer converts image tokens 🤯
They have an image tokenizer unified with text, and they de-tokenize using either of two models (LLM and diffusion)
The model is actually a full LLM (Qwen2), the tokenizer converts image tokens 🤯