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albertvillanovaΒ 
posted an update 1 day ago
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605
πŸš€ SmolAgents v1.19.0 is live!
This release brings major improvements to agent flexibility, UI usability, streaming architecture, and developer experience: making it easier than ever to build smart, interactive AI agents. Here's what's new:

πŸ”§ Agent Upgrades
- Support for managed agents in ToolCallingAgent
- Context manager support for cleaner agent lifecycle handling
- Output formatting now uses XML tags for consistency

πŸ–₯️ UI Enhancements
- GradioUI now supports reset_agent_memory: perfect for fresh starts in dev & demos.

πŸ”„ Streaming Refactor
- Streaming event aggregation moved off the Model class
- ➑️ Better architecture & maintainability

πŸ“¦ Output Tracking
- CodeAgent outputs are now stored in ActionStep
- βœ… More visibility and structure to agent decisions

πŸ› Bug Fixes
- Smarter planning logic
- Cleaner Docker logs
- Better prompt formatting for additional_args
- Safer internal functions and final answer matching

πŸ“š Docs Improvements
- Added quickstart examples with tool usage
- One-click Colab launch buttons
- Expanded reference docs (AgentMemory, GradioUI docstrings)
- Fixed broken links and migrated to .md format

πŸ”— Full release notes:
https://github.com/huggingface/smolagents/releases/tag/v1.19.0

πŸ’¬ Try it out, explore the new features, and let us know what you build!

#smolagents #opensource #AIagents #LLM #HuggingFace
albertvillanovaΒ 
posted an update 30 days ago
albertvillanovaΒ 
posted an update about 1 month ago
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2474
New in smolagents v1.16.0:
πŸ” Bing support in WebSearchTool
🐍 Custom functions & executor_kwargs in LocalPythonExecutor
πŸ”§ Streaming GradioUI fixes
🌐 Local web agents via api_base & api_key
πŸ“š Better docs

πŸ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.16.0
albertvillanovaΒ 
posted an update 2 months ago
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smolagents v1.14.0 is out! πŸš€
πŸ”Œ MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
πŸͺ¨ Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. πŸ’Ό

Full release πŸ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
albertvillanovaΒ 
posted an update 4 months ago
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πŸš€ New smolagents update: Safer Local Python Execution! 🦾🐍

With the latest release, we've added security checks to the local Python interpreter: every evaluation is now analyzed for dangerous builtins, modules, and functions. πŸ”’

Here's why this matters & what you need to know! πŸ§΅πŸ‘‡

1️⃣ Why is local execution risky? ⚠️
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.

2️⃣ New Safety Layer in smolagents πŸ›‘οΈ
We now inspect every return value during execution:
βœ… Allowed: Safe built-in types (e.g., numbers, strings, lists)
β›” Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)

3️⃣ Immediate Benefits πŸ’‘
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities

4️⃣ Security Disclaimer ⚠️
🚨 Despite these improvements, local Python execution is NEVER 100% safe. 🚨
If you need true isolation, use a remote sandboxed executor like Docker or E2B.

5️⃣ The Best Practice: Use Sandboxed Execution πŸ”
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.

6️⃣ Upgrade Now & Stay Safe! πŸš€
Check out the latest smolagents release and start building safer AI agents today.

πŸ”— https://github.com/huggingface/smolagents

What security measures do you take when running AI-generated code? Let’s discuss! πŸ‘‡

#AI #smolagents #Python #Security
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albertvillanovaΒ 
posted an update 4 months ago
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πŸš€ Big news for AI agents! With the latest release of smolagents, you can now securely execute Python code in sandboxed Docker or E2B environments. πŸ¦ΎπŸ”’

Here's why this is a game-changer for agent-based systems: πŸ§΅πŸ‘‡

1️⃣ Security First πŸ”
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 πŸ“¦
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable settingβ€”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 πŸ› οΈ
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backendβ€”no need for complex security setups!

6️⃣ Perfect for Autonomous AI Agents πŸ€–
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! πŸš€πŸ’‘
albertvillanovaΒ 
posted an update 5 months ago
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πŸš€ Introducing @huggingface Open Deep-ResearchπŸ’₯

In just 24 hours, we built an open-source agent that:
βœ… Autonomously browse the web
βœ… Search, scroll & extract info
βœ… Download & manipulate files
βœ… Run calculations on data

55% on GAIA validation set! Help us improve it!πŸ’‘
https://huggingface.co/blog/open-deep-research
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albertvillanovaΒ 
posted an update 6 months ago
albertvillanovaΒ 
posted an update 7 months ago
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🚨 How green is your model? 🌱 Introducing a new feature in the Comparator tool: Environmental Impact for responsible #LLM research!
πŸ‘‰ open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!

🌍 The Comparator calculates COβ‚‚ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... πŸ› οΈ
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!
albertvillanovaΒ 
posted an update 8 months ago
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πŸš€ New feature of the Comparator of the πŸ€— Open LLM Leaderboard: now compare models with their base versions & derivatives (finetunes, adapters, etc.). Perfect for tracking how adjustments affect performance & seeing innovations in action. Dive deeper into the leaderboard!

πŸ› οΈ Here's how to use it:
1. Select your model from the leaderboard.
2. Load its model tree.
3. Choose any base & derived models (adapters, finetunes, merges, quantizations) for comparison.
4. Press Load.
See side-by-side performance metrics instantly!

Ready to dive in? πŸ† Try the πŸ€— Open LLM Leaderboard Comparator now! See how models stack up against their base versions and derivatives to understand fine-tuning and other adjustments. Easier model analysis for better insights! Check it out here: open-llm-leaderboard/comparator 🌐
albertvillanovaΒ 
posted an update 8 months ago
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πŸš€ Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! πŸ“Š

open-llm-leaderboard/comparator

Dive into multi-model evaluations, pinpoint the best model for your needs, and explore insights across top open LLMs all in one place. Ready to level up your model comparison game?
albertvillanovaΒ 
posted an update 8 months ago
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🚨 Instruct-tuning impacts models differently across families! Qwen2.5-72B-Instruct excels on IFEval but struggles with MATH-Hard, while Llama-3.1-70B-Instruct avoids MATH performance loss! Why? Can they follow the format in examples? πŸ“Š Compare models: open-llm-leaderboard/comparator
albertvillanovaΒ 
posted an update 8 months ago
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Finding the Best SmolLM for Your Project

Need an LLM assistant but unsure which hashtag#smolLM to run locally? With so many models available, how can you decide which one suits your needs best? πŸ€”

If the model you’re interested in is evaluated on the Hugging Face Open LLM Leaderboard, there’s an easy way to compare them: use the model Comparator tool: open-llm-leaderboard/comparator
Let’s walk through an exampleπŸ‘‡

Let’s compare two solid options:
- Qwen2.5-1.5B-Instruct from Alibaba Cloud Qwen (1.5B params)
- gemma-2-2b-it from Google (2.5B params)

For an assistant, you want a model that’s great at instruction following. So, how do these two models stack up on the IFEval task?

What about other evaluations?
Both models are close in performance on many other tasks, showing minimal differences. Surprisingly, the 1.5B Qwen model performs just as well as the 2.5B Gemma in many areas, even though it's smaller in size! πŸ“Š

This is a great example of how parameter size isn’t everything. With efficient design and training, a smaller model like Qwen2.5-1.5B can match or even surpass larger models in certain tasks.

Looking for other comparisons? Drop your model suggestions below! πŸ‘‡
albertvillanovaΒ 
posted an update 8 months ago
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🚨 We’ve just released a new tool to compare the performance of models in the πŸ€— Open LLM Leaderboard: the Comparator πŸŽ‰
open-llm-leaderboard/comparator

Want to see how two different versions of LLaMA stack up? Let’s walk through a step-by-step comparison of LLaMA-3.1 and LLaMA-3.2. πŸ¦™πŸ§΅πŸ‘‡

1/ Load the Models' Results
- Go to the πŸ€— Open LLM Leaderboard Comparator: open-llm-leaderboard/comparator
- Search for "LLaMA-3.1" and "LLaMA-3.2" in the model dropdowns.
- Press the Load button. Ready to dive into the results!

2/ Compare Metric Results in the Results Tab πŸ“Š
- Head over to the Results tab.
- Here, you’ll see the performance metrics for each model, beautifully color-coded using a gradient to highlight performance differences: greener is better! 🌟
- Want to focus on a specific task? Use the Task filter to hone in on comparisons for tasks like BBH or MMLU-Pro.

3/ Check Config Alignment in the Configs Tab βš™οΈ
- To ensure you’re comparing apples to apples, head to the Configs tab.
- Review both models’ evaluation configurations, such as metrics, datasets, prompts, few-shot configs...
- If something looks off, it’s good to know before drawing conclusions! βœ…

4/ Compare Predictions by Sample in the Details Tab πŸ”
- Curious about how each model responds to specific inputs? The Details tab is your go-to!
- Select a Task (e.g., MuSR) and then a Subtask (e.g., Murder Mystery) and then press the Load Details button.
- Check out the side-by-side predictions and dive into the nuances of each model’s outputs.

5/ With this tool, it’s never been easier to explore how small changes between model versions affect performance on a wide range of tasks. Whether you’re a researcher or enthusiast, you can instantly visualize improvements and dive into detailed comparisons.

πŸš€ Try the πŸ€— Open LLM Leaderboard Comparator now and take your model evaluations to the next level!
albertvillanovaΒ 
posted an update 9 months ago
albertvillanovaΒ 
posted an update about 1 year ago
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Easily convert your script-based datasets to Parquet and explore them in the dataset viewer. 🌟

πŸ› οΈ Use @huggingface Datasets CLI:
$ 𝚍𝚊𝚝𝚊𝚜𝚎𝚝𝚜-πšŒπš•πš’ πšŒπš˜πš—πšŸπšŽπš›πš_𝚝𝚘_πš™πšŠπš›πššπšžπšŽπš πš„πš‚π™΄πšπ™½π™°π™Όπ™΄/π™³π™°πšƒπ™°πš‚π™΄πšƒ_𝙽𝙰𝙼𝙴

Learn more: https://huggingface.co/docs/datasets/main/en/cli#convert-to-parquet
#Data #AI
albertvillanovaΒ 
posted an update about 1 year ago
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Recently, the Hugging Face πŸ€— datasets team met with the Language Technologies team led by Marta Villegas ( @mvillegas ) at Barcelona Supercomputing Center @BSC-LT . Eager to collaborate to promote AI across Catalan, Spanish, Basque, and Galician languages and share open-source datasets/models. 🀝 #AI #LanguageTech #OpenSource
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albertvillanovaΒ 
posted an update about 1 year ago
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πŸš€ We recently released datasets 2.19.0! πŸ“¦

πŸ”₯ What's New:
- Polars integration πŸ»β€β„οΈ
- fsspec support for conversion to JSON, CSV, and Parquet
- Mode parameter for Image feature
- CLI function to convert script-datasets to Parquet
- Dataset.take and Dataset.skip

Plus, a bunch of general improvements & bug fixes!

Check out the release notes: https://github.com/huggingface/datasets/releases/tag/2.19.0

Upgrade now and power up your data workflows! πŸ’₯
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albertvillanovaΒ 
authored 2 papers about 2 years ago