Interact with your PDF documents like never before! ๐คฏ Extract text & images, then ask context-aware questions based on both. Powered by RAG techniques & multimodal LLMs. Perfect for studying, research & more! ๐๐ Try it out now!!!! โ๏ธ
With the big hype around AI agents these days, I couldnโt stop thinking about how AI agents could truly enhance real-world activities. What sort of applications could we build with those AI agents: agentic RAG? self-correcting text-to-sql? Nah, boringโฆ
Passionate about outdoors, Iโve always dreamed of a tool that could simplify planning mountain trips while accounting for all potential risks. Thatโs why I built ๐๐น๐ฝ๐ถ๐ป๐ฒ ๐๐ด๐ฒ๐ป๐, a smart assistant designed to help you plan safe and enjoyable itineraries in the French Alps and Pyrenees.
Built using Hugging Face's ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ library, Alpine Agent combines the power of AI with trusted resources like ๐๐ฌ๐ช๐ต๐ฐ๐ถ๐ณ.๐ง๐ณ (https://skitour.fr/) and METEO FRANCE. Whether itโs suggesting a route with moderate difficulty or analyzing avalanche risks and weather conditions, this agent dynamically integrates data to deliver personalized recommendations.
In my latest blog post, I share how I developed this projectโfrom defining tools and integrating APIs to selecting the best LLMs like ๐๐ธ๐ฆ๐ฏ2.5-๐๐ฐ๐ฅ๐ฆ๐ณ-32๐-๐๐ฏ๐ด๐ต๐ณ๐ถ๐ค๐ต, ๐๐ญ๐ข๐ฎ๐ข-3.3-70๐-๐๐ฏ๐ด๐ต๐ณ๐ถ๐ค๐ต, or ๐๐๐-4.
๐ Multimodal - MiniCPM-o 2.6 is a new sota any-to-any model by OpenBMB (vision, speech and text!) - VideoChat-Flash-Qwen2.5-2B is new video multimodal models by OpenGVLab that come in sizes 2B & 7B in resolutions 224 & 448 - ByteDance released larger SA2VA that comes in 26B parameters - Dataset: VRC-Bench is a new diverse benchmark for multimodal LLM reasoning performance
๐ฌ LLMs - MiniMax-Text-01 is a new huge language model (456B passive 45.9B active params) by MiniMaxAI with context length of 4M tokens ๐คฏ - Dataset: Sky-T1-data-17k is a diverse dataset used to train Sky-T1-32B - kyutai released Helium-1-Preview-2B is a new small multilingual LM - Wayfarer-12B is a new LLM able to write D&D ๐ง๐ปโโ๏ธ - ReaderLM-v2 is a new HTML parsing model by Jina AI - Dria released, Dria-Agent-a-3B, new agentic coding model (Pythonic function calling) based on Qwen2.5 Coder - Unsloth released Phi-4, faster and memory efficient Llama 3.3
๐ผ๏ธ Vision - MatchAnything is a new foundation model for matching - FitDit is a high-fidelity VTON model based on DiT architecture
๐ฃ๏ธ Audio - OuteTTS-0.3-1B is a new multilingual text-to-speech model with voice cloning and emotion control capabilities
๐ Retrieval - lightblue released a new reranker based on Qwen2.5 LB-reranker-0.5B-v1.0 that can handle 95+ languages - cde-small-v2 is a new sota small retrieval model by @jxm
Multimodal ๐ผ๏ธ > ByteDance released SA2VA: a family of vision LMs that can take image, video, text and visual prompts > moondream2 is out with new capabilities like outputting structured data and gaze detection! > Dataset: Alibaba DAMO lab released multimodal textbook โ 22k hours worth of samples from instruction videos ๐คฏ > Dataset: SciCap captioning on scientific documents benchmark dataset is released along with the challenge!
LLMs ๐ฌ > Microsoft released Phi-4, sota open-source 14B language model ๐ฅ > Dolphin is back with Dolphin 3.0 Llama 3.1 8B ๐ฌ๐ฌ > Prime-RL released Eurus-2-7B-PRIME a new language model trained using PRIME alignment > SmallThinker-3B is a new small reasoning LM based on Owen2.5-3B-Instruct ๐ญ > Dataset: QWQ-LONGCOT-500K is the dataset used to train SmallThinker, generated using QwQ-32B-preview ๐ > Dataset: @cfahlgren1 released React Code Instructions: a dataset of code instruction-code pairs ๐ > Dataset: Qwen team is on the roll, they just released CodeElo, a dataset of code preferences ๐ฉ๐ปโ๐ป
Embeddings ๐ > @MoritzLaurer released zero-shot version of ModernBERT large ๐ > KaLM is a new family of performant multilingual embedding models with MIT license built using Qwen2-0.5B
Image/Video Generation โฏ๏ธ > NVIDIA released Cosmos, a new family of diffusion/autoregressive World Foundation Models generating worlds from images, videos and texts ๐ฅ > Adobe released TransPixar: a new text-to-video model that can generate assets with transparent backgrounds (a first!) > Dataset: fal released cosmos-openvid-1m Cosmos-tokenized OpenVid-1M with samples from OpenVid-1M
Others > Prior Labs released TabPFNv2, the best tabular transformer is out for classification and regression > Metagene-1 is a new RNA language model that can be used for pathogen detection, zero-shot embedding and genome understanding
Checkout phi-4 from Microsoft, dropped a day ago... If you โค๏ธ the Phi series, then here is the GGUF - Sri-Vigneshwar-DJ/phi-4-GGUF. phi-4 is a 14B highly efficient open LLM that beats much larger models at math and reasoning - check out evaluations on the Open LLM.
> The models are capable of tasks involving vision-language understanding and visual referrals (referring segmentation) both for images and videos โฏ๏ธ
> The models come in 1B, 4B and 8B and are based on InternVL2.5 for base architecture and Qwen2, Qwen2.5 and InternLM2 for language model part (depending on the checkpoint)
> The model is very interesting, it has different encoders for different modalities each (visual prompt, text prompt, image and video) then it concatenates these to feed into LLM ๐ฌ
the output segmentation tokens are passed to SAM2, to sort of match text (captions or semantic classes) to masks โคต๏ธ
> Their annotation pipeline is also interesting, they seems to use two open large vision LMs to refine the annotations, and have different levels of descriptions to provide consistency.
Just sharing a thought: I started using DeepSeek V3 a lot, and an idea struck me about agents "orchestrating during inference" on a test-time compute model like DeepSeek V3 or the O1 series.
Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
Combining smolagents with Anthropicโs best practices simplifies building powerful AI agents:
1. Code-Based Agents: Write actions as Python code, reducing steps by 30%. 2. Prompt Chaining: Break tasks into sequential subtasks with validation gates. 3. Routing: Classify inputs and direct them to specialized handlers. 4. Fallback: Handle tasks even if classification fails.
Hey everyone ๐ค! Check out this new Virtual Try Off model (based on SD1.5): 1aurent/TryOffAnyone This model isn't as accurate as others (e.g. xiaozaa/cat-try-off-flux based on FLUX.1) but it sure is fast!
Coming back to Paris Friday to open our new Hugging Face office!
We're at capacity for the party but add your name in the waiting list as we're trying to privatize the passage du Caire for extra space for robots ๐ค๐ฆพ๐ฆฟ
