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Error loading 'structured-wikipedia' due to schema mismatch
3
#16 opened 2 months ago
by
eb2002
resquito-wmf
updated
a
dataset
7 days ago
Update README.md
#17 opened 7 days ago
by
resquito-wmf
albertvillanova
posted
an
update
9 days ago
Post
402
New in smolagents v1.17.0:
- Structured generation in CodeAgent 🧱
- Streamable HTTP MCP support 🌐
- Agent.run() returns rich RunResult 📦
Smarter agents, smoother workflows.
Try it now: https://github.com/huggingface/smolagents/releases/tag/v1.17.0
- Structured generation in CodeAgent 🧱
- Streamable HTTP MCP support 🌐
- Agent.run() returns rich RunResult 📦
Smarter agents, smoother workflows.
Try it now: https://github.com/huggingface/smolagents/releases/tag/v1.17.0
Error loading 'structured-wikipedia' due to schema mismatch
3
#16 opened 2 months ago
by
eb2002
albertvillanova
posted
an
update
20 days ago
Post
2411
New in smolagents v1.16.0:
🔍 Bing support in WebSearchTool
🐍 Custom functions & executor_kwargs in LocalPythonExecutor
🔧 Streaming GradioUI fixes
🌐 Local web agents via api_base & api_key
📚 Better docs
👉 https://github.com/huggingface/smolagents/releases/tag/v1.16.0
🔍 Bing support in WebSearchTool
🐍 Custom functions & executor_kwargs in LocalPythonExecutor
🔧 Streaming GradioUI fixes
🌐 Local web agents via api_base & api_key
📚 Better docs
👉 https://github.com/huggingface/smolagents/releases/tag/v1.16.0
albertvillanova
posted
an
update
about 1 month ago
Post
2739
smolagents v1.14.0 is out! 🚀
🔌 MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
🪨 Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. 💼
Full release 👉 https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
🔌 MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
🪨 Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. 💼
Full release 👉 https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
sdelbecque
updated
a
dataset
2 months ago
albertvillanova
posted
an
update
3 months ago
Post
4102
🚀 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. 🔒
Here's why this matters & what you need to know! 🧵👇
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 🛡️
We now inspect every return value during execution:
✅ Allowed: Safe built-in types (e.g., numbers, strings, lists)
⛔ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3️⃣ Immediate Benefits 💡
- 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 🔐
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! 🚀
Check out the latest smolagents release and start building safer AI agents today.
🔗 https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Let’s discuss! 👇
#AI #smolagents #Python #Security
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. 🔒
Here's why this matters & what you need to know! 🧵👇
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 🛡️
We now inspect every return value during execution:
✅ Allowed: Safe built-in types (e.g., numbers, strings, lists)
⛔ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3️⃣ Immediate Benefits 💡
- 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 🔐
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! 🚀
Check out the latest smolagents release and start building safer AI agents today.
🔗 https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Let’s discuss! 👇
#AI #smolagents #Python #Security
- 2 replies
albertvillanova
posted
an
update
3 months ago
Post
4006
🚀 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! 🚀💡
frimelle
authored
2
papers
3 months ago
Post
2446
What’s in a name? More than you might think, especially for AI.
Whenever I introduce myself, people often start speaking French to me, even though my French is très basic. It turns out that AI systems do something similar:
Large language models infer cultural identity from names, shaping their responses based on presumed backgrounds. But is this helpful personalization or a reinforcement of stereotypes?
In our latest paper, we explored this question by testing DeepSeek, Llama, Aya, Mistral-Nemo, and GPT-4o-mini on how they associate names with cultural identities. We analysed 900 names from 30 cultures and found strong assumptions baked into AI responses: some cultures were overrepresented, while others barely registered.
For example, a name like "Jun" often triggered Japan-related responses, while "Carlos" was linked primarily to Mexico, even though these names exist in multiple countries. Meanwhile, names from places like Ireland led to more generic answers, suggesting weaker associations in the training data.
This has real implications for AI fairness: How should AI systems personalize without stereotyping? Should they adapt at all based on a name?
Work with some of my favourite researchers: @sidicity Arnav Arora and @IAugenstein
Read the full paper here: Presumed Cultural Identity: How Names Shape LLM Responses (2502.11995)
Whenever I introduce myself, people often start speaking French to me, even though my French is très basic. It turns out that AI systems do something similar:
Large language models infer cultural identity from names, shaping their responses based on presumed backgrounds. But is this helpful personalization or a reinforcement of stereotypes?
In our latest paper, we explored this question by testing DeepSeek, Llama, Aya, Mistral-Nemo, and GPT-4o-mini on how they associate names with cultural identities. We analysed 900 names from 30 cultures and found strong assumptions baked into AI responses: some cultures were overrepresented, while others barely registered.
For example, a name like "Jun" often triggered Japan-related responses, while "Carlos" was linked primarily to Mexico, even though these names exist in multiple countries. Meanwhile, names from places like Ireland led to more generic answers, suggesting weaker associations in the training data.
This has real implications for AI fairness: How should AI systems personalize without stereotyping? Should they adapt at all based on a name?
Work with some of my favourite researchers: @sidicity Arnav Arora and @IAugenstein
Read the full paper here: Presumed Cultural Identity: How Names Shape LLM Responses (2502.11995)
albertvillanova
posted
an
update
4 months ago
Post
4073
🚀 Introducing
@huggingface
Open Deep-Research💥
In just 24 hours, we built an open-source agent that:
✅ Autonomously browse the web
✅ Search, scroll & extract info
✅ Download & manipulate files
✅ Run calculations on data
55% on GAIA validation set! Help us improve it!💡
https://huggingface.co/blog/open-deep-research
In just 24 hours, we built an open-source agent that:
✅ Autonomously browse the web
✅ Search, scroll & extract info
✅ Download & manipulate files
✅ Run calculations on data
55% on GAIA validation set! Help us improve it!💡
https://huggingface.co/blog/open-deep-research
- 3 replies
Post
532
I was quoted in an article about the French Lucie AI in La Presse. While I love the name for obvious reasons 👀 there were still a lot of problems with the model and how and when it was deployed. Nevertheless seeing new smaller models being developed is an exciting direction for the next years of AI development to come!
https://www.lapresse.ca/affaires/techno/2025-02-02/radioscopie/lucie-l-ia-francaise-qui-ne-passe-pas-le-test.php
Also fun to see my comments in French.
https://www.lapresse.ca/affaires/techno/2025-02-02/radioscopie/lucie-l-ia-francaise-qui-ne-passe-pas-le-test.php
Also fun to see my comments in French.
Post
1688
Seeing AI develop has been a wild ride, from trying to explain why we'd bother to generate a single sentence with a *neural network* to explaining that AI is not a magic, all-knowing box. The recent weeks and months have been a lot of talking about how AI works; to policy makers, to other developers, but also and mainly friends and family without a technical background.
Yesterday, the first provisions of the EU AI Act came into force, and one of the the key highlights are the AI literacy requirements for organisations deploying AI systems. This isn't just a box-ticking exercise. Ensuring that employees and stakeholders understand AI systems is crucial for fostering responsible and transparent AI development. From recognising biases to understanding model limitations, AI literacy empowers individuals to engage critically with these technologies and make informed decisions.
In the context of Hugging Face, AI literacy has many facets: allowing more people to contribute to AI development, providing courses and documentation to ensuring access is possible, and accessible AI tools that empower users to better understand how AI systems function. This isn't just a regulatory milestone; it’s an opportunity to foster a culture where AI literacy becomes foundational, enabling stakeholders to recognise biases, assess model limitations, and engage critically with technology.
Embedding these principles into daily practice, and eventually extending our learnings in AI literacy to the general public, is essential for building trustworthy AI that aligns with societal values.
Yesterday, the first provisions of the EU AI Act came into force, and one of the the key highlights are the AI literacy requirements for organisations deploying AI systems. This isn't just a box-ticking exercise. Ensuring that employees and stakeholders understand AI systems is crucial for fostering responsible and transparent AI development. From recognising biases to understanding model limitations, AI literacy empowers individuals to engage critically with these technologies and make informed decisions.
In the context of Hugging Face, AI literacy has many facets: allowing more people to contribute to AI development, providing courses and documentation to ensuring access is possible, and accessible AI tools that empower users to better understand how AI systems function. This isn't just a regulatory milestone; it’s an opportunity to foster a culture where AI literacy becomes foundational, enabling stakeholders to recognise biases, assess model limitations, and engage critically with technology.
Embedding these principles into daily practice, and eventually extending our learnings in AI literacy to the general public, is essential for building trustworthy AI that aligns with societal values.
-
- 2 replies
albertvillanova
posted
an
update
5 months ago
Post
2173
Discover all the improvements in the new version of Lighteval: https://huggingface.co/docs/lighteval/
albertvillanova
posted
an
update
7 months ago
Post
1880
🚨 How green is your model? 🌱 Introducing a new feature in the Comparator tool: Environmental Impact for responsible #LLM research!
👉 open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!
🌍 The Comparator calculates CO₂ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... 🛠️
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!
👉 open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!
🌍 The Comparator calculates CO₂ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... 🛠️
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!
Reverts timestamp data type to string
1
#10 opened 8 months ago
by
albertvillanova
Upload README.md
#14 opened 7 months ago
by
resquito-wmf