Weβre launching a FREE and CERTIFIED course on Agents!
We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions. - Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors. - Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents. - Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents. Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
Quick update from week 1 of smol course. The community is taking the driving seat and using the material for their own projects. If you want to do the same, join in!
- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish - 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning - 3 chapters are in progress on evaluation, vision language models, and synthetic data. - around 780 people have forked the repo to use it for learning, teaching, sharing.
βοΈ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR
There's a new timm release, v 1.0.12, with a focus on optimizers. The optimizer factory has been refactored, there's now a timm.optim.list_optimizers() and new way to register optimizers and their attributes. As always you can use an timm optimizer like a torch one, just replace torch.optim with timm.optim
New optimizers include: * AdafactorBigVision - adfactorbv * ADOPT - adopt / adoptw (decoupled decay) * MARS - mars * LaProp - laprop * Cautious Optimizers - a modification to all of the above, prefix with c as well as cadamw, cnadamw, csgdw, clamb, crmsproptf
Six predictions for AI in 2025 (and a review of how my 2024 predictions turned out):
- There will be the first major public protest related to AI - A big company will see its market cap divided by two or more because of AI - At least 100,000 personal AI robots will be pre-ordered - China will start to lead the AI race (as a consequence of leading the open-source AI race). - There will be big breakthroughs in AI for biology and chemistry. - We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face.
How my predictions for 2024 turned out:
- A hyped AI company will go bankrupt or get acquired for a ridiculously low price β (Inflexion, AdeptAI,...)
- Open-source LLMs will reach the level of the best closed-source LLMs β with QwQ and dozens of others
- Big breakthroughs in AI for video, time-series, biology and chemistry β for video π΄for time-series, biology and chemistry
- We will talk much more about the cost (monetary and environmental) of AI β Monetary π΄Environmental (π’)
- A popular media will be mostly AI-generated β with NotebookLM by Google
- 10 millions AI builders on Hugging Face leading to no increase of unemployment πcurrently 7M of AI builders on Hugging Face
4 replies
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reacted to merve's
post with ππ₯π5 months ago
small but mighty π₯ you can fine-tune SmolVLM on an L4 with batch size of 4 and it will only take 16.4 GB VRAM π«°π» also with gradient accumulation simulated batch size is 16 β¨ I made a notebook that includes all the goodies: QLoRA, gradient accumulation, gradient checkpointing with explanations on how they work π https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
πΌοΈ Multimodal > At Hugging Face we released SmolVLM, a performant and efficient smol vision language model π > Show Lab released ShowUI-2B: new vision-language-action model to build GUI/web automation agents π€ > 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 modelπ > Dataset: LLaVA-CoT-100k dataset used to train Llava-CoT released by creators of Llava-CoT π
π¬ LLMs > Qwen team released QwQ-32B-Preview, state-of-the-art open-source reasoning model, broke the internet π₯ > AliBaba has released Marco-o1, a new open-source reasoning model π₯ > NVIDIA released Hymba 1.5B Base and Instruct, the new state-of-the-art SLMs with hybrid architecture (Mamba + transformer)
β―οΈ 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 β―οΈ > Dataset: Image Preferences is a new image generation preference dataset made with DIBT community effort of Argilla π·οΈ
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
