This week in open AI was 🔥 Let's recap! 🤗 merve/january-31-releases-679a10669bd4030090c5de4d LLMs 💬 > Huge: AllenAI released new Tülu models that outperform DeepSeek R1 using Reinforcement Learning with Verifiable Reward (RLVR) based on Llama 3.1 405B 🔥 > Mistral AI is back to open-source with their "small" 24B models (base & SFT), with Apache 2.0 license 😱 > Alibaba Qwen released their 1M context length models Qwen2.5-Instruct-1M, great for agentic use with Apache 2.0 license 🔥 > Arcee AI released Virtuoso-medium, 32.8B LLMs distilled from DeepSeek V3 with dataset of 5B+ tokens > Velvet-14B is a new family of 14B Italian LLMs trained on 10T tokens in six languages > OpenThinker-7B is fine-tuned version of Qwen2.5-7B-Instruct on OpenThoughts dataset
VLMs & vision 👀 > Alibaba Qwen is back with Qwen2.5VL, amazing new capabilities ranging from agentic computer use to zero-shot localization 🔥 > NVIDIA released new series of Eagle2 models with 1B and 9B sizes > DeepSeek released Janus-Pro, new any-to-any model (image-text generation from image-text input) with MIT license > BEN2 is a new background removal model with MIT license!
Audio 🗣️ > YuE is a new open-source music generation foundation model, lyrics-to-song generation
We have been cooking a couple of fine-tuning runs on CogVideoX with finetrainers, smol datasets, and LoRA to generate cool video effects like crushing, dissolving, etc.
We are also releasing a LoRA extraction utility from a fully fine-tuned checkpoint. I know that kind of stuff has existed since eternity, but the quality on video models was nothing short of spectacular. Below are some links:
Multimodal 💬 - We have released SmolVLM -- tiniest VLMs that come in 256M and 500M, with it's retrieval models ColSmol for multimodal RAG 💗 - UI-TARS are new models by ByteDance to unlock agentic GUI control 🤯 in 2B, 7B and 72B - Alibaba DAMO lab released VideoLlama3, new video LMs that come in 2B and 7B - MiniMaxAI released Minimax-VL-01, where decoder is based on MiniMax-Text-01 456B MoE model with long context - Dataset: Yale released a new benchmark called MMVU - Dataset: CAIS released Humanity's Last Exam (HLE) a new challenging MM benchmark
LLMs 📖 - DeepSeek-R1 & DeepSeek-R1-Zero: gigantic 660B reasoning models by DeepSeek, and six distilled dense models, on par with o1 with MIT license! 🤯 - Qwen2.5-Math-PRM: new math models by Qwen in 7B and 72B - NVIDIA released AceMath and AceInstruct, new family of models and their datasets (SFT and reward ones too!)
Audio 🗣️ - Llasa is a new speech synthesis model based on Llama that comes in 1B,3B, and 8B - TangoFlux is a new audio generation model trained from scratch and aligned with CRPO
Image/Video/3D Generation ⏯️ - Flex.1-alpha is a new 8B pre-trained diffusion model by ostris similar to Flux - tencent released Hunyuan3D-2, new 3D asset generation from images
smolagents can see 🔥 we just shipped vision support to smolagents 🤗 agentic computers FTW
you can now: 💻 let the agent get images dynamically (e.g. agentic web browser) 📑 pass images at the init of the agent (e.g. chatting with documents, filling forms automatically etc) with few LoC change! 🤯 you can use transformers models locally (like Qwen2VL) OR plug-in your favorite multimodal inference provider (gpt-4o, antrophic & co) 🤠
👀 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
💬 LLMs - MiniMax-Text-01 is a new huge language model (456B passive 45.9B active params) by MiniMaxAI with context length of 4M tokens 🤯 - 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 🧙🏻♂️ - 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
🖼️ Vision - MatchAnything is a new foundation model for matching - FitDit is a high-fidelity VTON model based on DiT architecture
🗣️ Audio - OuteTTS-0.3-1B is a new multilingual text-to-speech model with voice cloning and emotion control capabilities
📖 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 🖼️ > 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 — 22k hours worth of samples from instruction videos 🤯 > Dataset: SciCap captioning on scientific documents benchmark dataset is released along with the challenge!
LLMs 💬 > Microsoft released Phi-4, sota open-source 14B language model 🔥 > Dolphin is back with Dolphin 3.0 Llama 3.1 8B 🐬🐬 > 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 💭 > Dataset: QWQ-LONGCOT-500K is the dataset used to train SmallThinker, generated using QwQ-32B-preview 📕 > Dataset: @cfahlgren1 released React Code Instructions: a dataset of code instruction-code pairs 📕 > Dataset: Qwen team is on the roll, they just released CodeElo, a dataset of code preferences 👩🏻💻
Embeddings 🔖 > @MoritzLaurer released zero-shot version of ModernBERT large 👏 > KaLM is a new family of performant multilingual embedding models with MIT license built using Qwen2-0.5B
Image/Video Generation ⏯️ > NVIDIA released Cosmos, a new family of diffusion/autoregressive World Foundation Models generating worlds from images, videos and texts 🔥 > 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
> The models are capable of tasks involving vision-language understanding and visual referrals (referring segmentation) both for images and videos ⏯️
> 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 💬
the output segmentation tokens are passed to SAM2, to sort of match text (captions or semantic classes) to masks ⤵️
> 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.
* 4 new video models * Multiple image models, including SANA & Flux Control * New quantizers -> GGUF & TorchAO * New training scripts Enjoy this holiday-special Diffusers release 🤗 Notes: https://github.com/huggingface/diffusers/releases/tag/v0.32.0
In the past seven days, the Diffusers team has shipped:
1. Two new video models 2. One new image model 3. Two new quantization backends 4. Three new fine-tuning scripts 5. Multiple fixes and library QoL improvements
Coffee on me if someone can guess 1 - 4 correctly.
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 📈 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 🔥 > 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 🔥
Image Prompt Engineering Guide: ➡️ Artistic styling for Image generation ➡️ Prompt weighting using the parentheses method to generate realistic images. ➡️ Advanced features like style and positioning control[experimental]. ➡️ Image placement on the generated AI image using Recraft V3 Mockup.
