Crazy that this is a day 0 release.
Kenneth Hamilton PRO
ZennyKenny
AI & ML interests
Building and enablement @ montebello.ai
Certified vibe coder
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replied to
burtenshaw's
post
about 8 hours ago
Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:
In this notebooks I combine together google’s model with some community tooling
- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3
- I use PEFT and bitsandbytes to get it running on Colab
- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k
- Finally, I used TRL’s GRPOTrainer to train the model
Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.
https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing
reacted
to
burtenshaw's
post
with 🤗
about 8 hours ago
Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:
In this notebooks I combine together google’s model with some community tooling
- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3
- I use PEFT and bitsandbytes to get it running on Colab
- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k
- Finally, I used TRL’s GRPOTrainer to train the model
Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.
https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing
liked
a model
about 9 hours ago
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ZennyKenny's activity
replied to
burtenshaw's
post
about 8 hours ago
reacted to
burtenshaw's
post with 🤗
about 8 hours ago
Post
1156
Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:
In this notebooks I combine together google’s model with some community tooling
- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3
- I use PEFT and bitsandbytes to get it running on Colab
- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k
- Finally, I used TRL’s GRPOTrainer to train the model
Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.
https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing
In this notebooks I combine together google’s model with some community tooling
- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3
- I use PEFT and bitsandbytes to get it running on Colab
- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k
- Finally, I used TRL’s GRPOTrainer to train the model
Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.
https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing
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- 3 replies
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about 9 hours ago
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1 day ago
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Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM
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2 days ago
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4 days ago
Multiple zeroGPU calls in same code
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#155 opened 5 days ago
by
hen
reacted to
mcpotato's
post with 🤗
6 days ago
Post
2383
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
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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6 days ago
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fdaudens's
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7 days ago
Post
4039
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
Must-read by @thomwolf deflating overblown AI promises and explaining what real scientific breakthroughs require.
https://thomwolf.io/blog/scientific-ai.html
- 2 replies
reacted to
albertvillanova's
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7 days ago
Post
3769
🚀 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! 🚀💡
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
8 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.
upvoted
an
article
8 days ago
Article
Deepseek R1 Robotic Reasoning with Checkers
By
and 4 others
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8 days ago
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8 days ago
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8 days ago
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update
8 days ago
Post
482
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.
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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- 2 replies