HuggingFaceTB/SmolVLM-256M-Instruct
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Adriel Martins
Martins6
AI & ML interests
Graph Neural Networks (GNN) &
Robot Learning &
Multimodal AI
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๐๐ฑ๐๐ฒ๐ป'๐ ๐ป๐ฒ๐ ๐๐ฎ๐๐ฎ ๐๐ด๐ฒ๐ป๐๐ ๐๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ ๐๐ต๐ผ๐๐ ๐๐ต๐ฎ๐ ๐๐ฒ๐ฒ๐ฝ๐ฆ๐ฒ๐ฒ๐ธ-๐ฅ๐ญ ๐๐๐ฟ๐๐ด๐ด๐น๐ฒ๐ ๐ผ๐ป ๐ฑ๐ฎ๐๐ฎ ๐๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฎ๐๐ธ๐! โ
โก๏ธ How well do reasoning models perform on agentic tasks? Until now, all indicators seemed to show that they worked really well. On our recent reproduction of Deep Search, OpenAI's o1 was by far the best model to power an agentic system.
So when our partner Adyen built a huge benchmark of 450 data science tasks, and built data agents with smolagents to test different models, I expected reasoning models like o1 or DeepSeek-R1 to destroy the tasks at hand.
๐ But they really missed the mark. DeepSeek-R1 only got 1 or 2 out of 10 questions correct. Similarly, o1 was only at ~13% correct answers.
๐ง These results really surprised us. We thoroughly checked them, we even thought our APIs for DeepSeek were broken and colleagues Leandro Anton helped me start custom instances of R1 on our own H100s to make sure it worked well.
But there seemed to be no mistake. Reasoning LLMs actually did not seem that smart. Often, these models made basic mistakes, like forgetting the content of a folder that they had just explored, misspelling file names, or hallucinating data. Even though they do great at exploring webpages through several steps, the same level of multi-step planning seemed much harder to achieve when reasoning over files and data.
It seems like there's still lots of work to do in the Agents x Data space. Congrats to Adyen for this great benchmark, looking forward to see people proposing better agents! ๐
Read more in the blog post ๐ https://huggingface.co/blog/dabstep
โก๏ธ How well do reasoning models perform on agentic tasks? Until now, all indicators seemed to show that they worked really well. On our recent reproduction of Deep Search, OpenAI's o1 was by far the best model to power an agentic system.
So when our partner Adyen built a huge benchmark of 450 data science tasks, and built data agents with smolagents to test different models, I expected reasoning models like o1 or DeepSeek-R1 to destroy the tasks at hand.
๐ But they really missed the mark. DeepSeek-R1 only got 1 or 2 out of 10 questions correct. Similarly, o1 was only at ~13% correct answers.
๐ง These results really surprised us. We thoroughly checked them, we even thought our APIs for DeepSeek were broken and colleagues Leandro Anton helped me start custom instances of R1 on our own H100s to make sure it worked well.
But there seemed to be no mistake. Reasoning LLMs actually did not seem that smart. Often, these models made basic mistakes, like forgetting the content of a folder that they had just explored, misspelling file names, or hallucinating data. Even though they do great at exploring webpages through several steps, the same level of multi-step planning seemed much harder to achieve when reasoning over files and data.
It seems like there's still lots of work to do in the Agents x Data space. Congrats to Adyen for this great benchmark, looking forward to see people proposing better agents! ๐
Read more in the blog post ๐ https://huggingface.co/blog/dabstep
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SmolLM2 paper is out! ๐
๐ Why do I love it? Because it facilitates teaching and learning!
Over the past few months I've engaged with (no joke) thousands of students based on SmolLM.
- People have inferred, fine-tuned, aligned, and evaluated this smol model.
- People used they're own machines and they've used free tools like colab, kaggle, and spaces.
- People tackled use cases in their job, for fun, in their own language, and with their friends.
upvote the paper SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model (2502.02737)
๐ Why do I love it? Because it facilitates teaching and learning!
Over the past few months I've engaged with (no joke) thousands of students based on SmolLM.
- People have inferred, fine-tuned, aligned, and evaluated this smol model.
- People used they're own machines and they've used free tools like colab, kaggle, and spaces.
- People tackled use cases in their job, for fun, in their own language, and with their friends.
upvote the paper SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model (2502.02737)
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Open-R1: a fully open reproduction of DeepSeek-R1
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Hey HF community! ๐
Excited to share Monkt - a tool I built to solve the eternal headache of processing documents for ML/AI pipelines.
What it does: Converts PDFs, Word, PowerPoint, Excel, Web pages or raw HTML into clean Markdown or structured JSON.
Great for:
โ LLM training dataset preparation;
โ Knowledge base construction;
โ Research paper processing;
โ Technical documentation management.
It has API access for integration into ML pipelines.
Check it out at https://monkt.com/ if you want to save time on document processing infrastructure.
Looking forward to your feedback!
Excited to share Monkt - a tool I built to solve the eternal headache of processing documents for ML/AI pipelines.
What it does: Converts PDFs, Word, PowerPoint, Excel, Web pages or raw HTML into clean Markdown or structured JSON.
Great for:
โ LLM training dataset preparation;
โ Knowledge base construction;
โ Research paper processing;
โ Technical documentation management.
It has API access for integration into ML pipelines.
Check it out at https://monkt.com/ if you want to save time on document processing infrastructure.
Looking forward to your feedback!
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