ZennyKenny (Kenneth Hamilton)

reacted to mcpotato's post with 🤗 6 days ago

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Stoked to announce we've partnered with JFrog to continue improving safety on the Hub! 🐸

Their model scanner brings new scanning capabilities to the table, aimed at reducing alert fatigue.

More on that in our blog post: https://huggingface.co/blog/jfrog

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reacted to fdaudens's post with 🔥 6 days ago

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AI will bring us "a country of yes-men on servers" instead of one of "Einsteins sitting in a data center" if we continue on current trends.

Must-read by @thomwolf deflating overblown AI promises and explaining what real scientific breakthroughs require.

https://thomwolf.io/blog/scientific-ai.html

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reacted to albertvillanova's post with 🔥 7 days ago

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🚀 Big news for AI agents! With the latest release of smolagents, you can now securely execute Python code in sandboxed Docker or E2B environments. 🦾🔒

Here's why this is a game-changer for agent-based systems: 🧵👇

1️⃣ Security First 🔐
Running AI agents in unrestricted Python environments is risky! With sandboxing, your agents are isolated, preventing unintended file access, network abuse, or system modifications.

2️⃣ Deterministic & Reproducible Runs 📦
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable setting—no more environment mismatches or dependency issues!

3️⃣ Resource Control & Limits 🚦
Docker and E2B allow you to enforce CPU, memory, and execution time limits, so rogue or inefficient agents don’t spiral out of control.

4️⃣ Safer Code Execution in Production 🏭
Deploy AI agents confidently, knowing that any generated code runs in an ephemeral, isolated environment, protecting your host machine and infrastructure.

5️⃣ Easy to Integrate 🛠️
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backend—no need for complex security setups!

6️⃣ Perfect for Autonomous AI Agents 🤖
If your AI agents generate and execute code dynamically, this is a must-have to avoid security pitfalls while enabling advanced automation.

⚡ Get started now: https://github.com/huggingface/smolagents

What will you build with smolagents? Let us know! 🚀💡

replied to their post 7 days ago

Actually the model I've used is a distill of LLaMa so it meets the criteria of Free as in Freedom. Shoutout rms.

posted an update 8 days ago

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It took me a while, but I've finally got it working: ZennyKenny/note-to-text

Using a Meta LLaMa checkpoint from Unsloth and some help from the HF community, you can capture handwritten notes and convert them into digital format in just a few second.

Really exciting times for AI builders on Hugging Face.

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reacted to Bils's post with 👍 11 days ago

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create amazing audio ads in just a few steps
Bils/AIPromoStudio

reacted to fdaudens's post with 🔥 12 days ago

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What if AI becomes as ubiquitous as the internet, but runs locally and transparently on our devices?

Fascinating TED talk by @thomwolf on open source AI and its future impact.

Imagine this for AI: instead of black box models running in distant data centers, we get transparent AI that runs locally on our phones and laptops, often without needing internet access. If the original team moves on? No problem - resilience is one of the beauties of open source. Anyone (companies, collectives, or individuals) can adapt and fix these models.

This is a compelling vision of AI's future that solves many of today's concerns around AI transparency and centralized control.

Watch the full talk here: https://www.ted.com/talks/thomas_wolf_what_if_ai_just_works

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replied to fdaudens's post 12 days ago

Thought that @thomwolf gave a great explanation of why open source matters for the long-term of AI adoption. Really brilliant monologue.

reacted to davanstrien's post with 🔥 12 days ago

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Quick POC: Turn a Hugging Face dataset card into a short podcast introducing the dataset using all open models.

I think I'm the only weirdo who would enjoy listening to something like this though 😅

Here is an example for eth-nlped/stepverify

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posted an update 13 days ago

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I've spent most of time working with AI on user-facing apps like Chatbots and TextGen, but today I decided to work on something that I think has a lot of applications for Data Science teams: ZennyKenny/comment_classification

This Space supports uploading a user CSV and categorizing the fields based on user-defined categories. The applications of AI in production are truly endless. 🚀

posted an update 23 days ago

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Really excited to start contributing to the SWE Arena project: https://swe-arena.com/

Led by IBM PhD fellow @terryyz , our goal is to advance research in code generation and app development by frontier LLMs.

reacted to Quazim0t0's post with 👍 23 days ago

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My first attempt at using SmolAgents:
Quazim0t0/CSVAgent

The video attached was an example for this space.

Based on ZennyKenny's SqlAgent:
ZennyKenny/sqlAgent

You can upload a CSV file and it will automatically populate the table, then you can ask questions about the data.

Grab a sample CSV file here: https://github.com/datablist/sample-csv-files

The questions that can be asked may be limited.

_______________________
Second: Quazim0t0/TXTAgent
Created an Agent that converts a .txt file into a CSV file, then you can ask about the data and also download the CSV file that was generated.

_______________________
Third: Quazim0t0/ReportAgent
Upload Multiple TXT/DOC files to then generate a report from those files.

_______________________
Lastly: Quazim0t0/qResearch
A Research tool that uses DuckDuckGo for Web Searches, Wikipedia and tries to refine the answers in MLA Format.

reacted to clem's post with 🔥 24 days ago

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We crossed 1B+ tokens routed to inference providers partners on HF, that we released just a few days ago.

Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.

Have you been using any integration and how can we make it better?

https://huggingface.co/blog/inference-providers

reacted to burtenshaw's post with 🤗 24 days ago

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NEW COURSE! We’re cooking hard on Hugging Face courses, and it’s not just agents. The NLP course is getting the same treatment with a new chapter on Supervised Fine-Tuning!

👉 Follow to get more updates https://huggingface.co/nlp-course

The new SFT chapter will guide you through these topics:

1️⃣ Chat Templates: Master the art of structuring AI conversations for consistent and helpful responses.

2️⃣ Supervised Fine-Tuning (SFT): Learn the core techniques to adapt pre-trained models to your specific outputs.

3️⃣ Low Rank Adaptation (LoRA): Discover efficient fine-tuning methods that save memory and resources.

4️⃣ Evaluation: Measure your model's performance and ensure top-notch results.

This is the first update in a series, so follow along if you’re upskilling in AI.

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replied to their post 24 days ago

I thought this was interesting, so I worked on it a bit in my own space. Thought you might want to look at it, can't figure out some issues but I made some progress!

https://huggingface.co/spaces/Quazim0t0/CSVAgent

This is a clever continuation because a lot of businesses are using messy CSV data that needs interrogation.

Hmmmm, you've got me thinking...

posted an update 25 days ago

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Okay this is pretty crazy. Snowflake has CortexAI and Uber is already teasing QueryGPT, both of which prominently feature plain text to SQL features to query your database.

I decided to see how hard it would be to put together something similar using 🤗 smolagents. Turns out, it was pretty straightforward. I managed to get it done in London Luton airport this afternoon.

ZennyKenny/sqlAgent

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posted an update 30 days ago

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I've completed the first unit of the just-launched Hugging Face Agents Course. I would highly recommend it, even for experienced builders, because it is a great walkthrough of the smolagents library and toolkit.

posted an update about 1 month ago

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GradientBoostingClassifier is an algorithm supported by the Python SciKit library, and now you can quickly train an ML model using this powerful technique on any (viable) dataset in the Hugging Face Hub without a line of code.

Love finishing a project right when the late night starts to turn into the early morning: sklearn-docs/GradientBoostingClassifier

Long time listener, first time caller, but always pleased to contribute, even if only adjacently, to the power of SciKit.

reacted to lewtun's post with 🔥 about 2 months ago

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We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open!

🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.

🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.

🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.

Follow along: https://github.com/huggingface/open-r1

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posted an update about 2 months ago

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Really pleased with the Bring Your Own Model (BYOM) feature in Brave Browser: https://brave.com/blog/byom-nightly/

Takes about 5 minutes to configure your own locally running LLM as an in-browser assistant. Totally local, totally private, totally yours.

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Kenneth Hamilton PRO

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