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sumanthdΒ
updated
a
dataset
15 minutes ago
tahirjmΒ
updated
a
model
about 18 hours ago
tahirjmΒ
published
a
model
about 24 hours ago
svp19Β
updated
a
Space
1 day ago
tahirjmΒ
updated
a
collection
2 days ago
AshwinSankarΒ
updated
a
Space
2 days ago
albertvillanovaΒ
posted
an
update
9 days ago
Post
3554
π New smolagents update: Safer Local Python Execution! π¦Ύπ
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. π
Here's why this matters & what you need to know! π§΅π
1οΈβ£ Why is local execution risky? β οΈ
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.
2οΈβ£ New Safety Layer in smolagents π‘οΈ
We now inspect every return value during execution:
β Allowed: Safe built-in types (e.g., numbers, strings, lists)
β Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3οΈβ£ Immediate Benefits π‘
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities
4οΈβ£ Security Disclaimer β οΈ
π¨ Despite these improvements, local Python execution is NEVER 100% safe. π¨
If you need true isolation, use a remote sandboxed executor like Docker or E2B.
5οΈβ£ The Best Practice: Use Sandboxed Execution π
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.
6οΈβ£ Upgrade Now & Stay Safe! π
Check out the latest smolagents release and start building safer AI agents today.
π https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Letβs discuss! π
#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. π
Here's why this matters & what you need to know! π§΅π
1οΈβ£ Why is local execution risky? β οΈ
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.
2οΈβ£ New Safety Layer in smolagents π‘οΈ
We now inspect every return value during execution:
β Allowed: Safe built-in types (e.g., numbers, strings, lists)
β Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3οΈβ£ Immediate Benefits π‘
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities
4οΈβ£ Security Disclaimer β οΈ
π¨ Despite these improvements, local Python execution is NEVER 100% safe. π¨
If you need true isolation, use a remote sandboxed executor like Docker or E2B.
5οΈβ£ The Best Practice: Use Sandboxed Execution π
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.
6οΈβ£ Upgrade Now & Stay Safe! π
Check out the latest smolagents release and start building safer AI agents today.
π https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Letβs discuss! π
#AI #smolagents #Python #Security
- 2 replies
albertvillanovaΒ
posted
an
update
10 days ago
Post
3784
π Big news for AI agents! With the latest release of smolagents, you can now securely execute Python code in sandboxed Docker or E2B environments. π¦Ύπ
Here's why this is a game-changer for agent-based systems: π§΅π
1οΈβ£ Security First π
Running AI agents in unrestricted Python environments is risky! With sandboxing, your agents are isolated, preventing unintended file access, network abuse, or system modifications.
2οΈβ£ Deterministic & Reproducible Runs π¦
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable settingβno more environment mismatches or dependency issues!
3οΈβ£ Resource Control & Limits π¦
Docker and E2B allow you to enforce CPU, memory, and execution time limits, so rogue or inefficient agents donβt spiral out of control.
4οΈβ£ Safer Code Execution in Production π
Deploy AI agents confidently, knowing that any generated code runs in an ephemeral, isolated environment, protecting your host machine and infrastructure.
5οΈβ£ Easy to Integrate π οΈ
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backendβno need for complex security setups!
6οΈβ£ Perfect for Autonomous AI Agents π€
If your AI agents generate and execute code dynamically, this is a must-have to avoid security pitfalls while enabling advanced automation.
β‘ Get started now: https://github.com/huggingface/smolagents
What will you build with smolagents? Let us know! ππ‘
Here's why this is a game-changer for agent-based systems: π§΅π
1οΈβ£ Security First π
Running AI agents in unrestricted Python environments is risky! With sandboxing, your agents are isolated, preventing unintended file access, network abuse, or system modifications.
2οΈβ£ Deterministic & Reproducible Runs π¦
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable settingβno more environment mismatches or dependency issues!
3οΈβ£ Resource Control & Limits π¦
Docker and E2B allow you to enforce CPU, memory, and execution time limits, so rogue or inefficient agents donβt spiral out of control.
4οΈβ£ Safer Code Execution in Production π
Deploy AI agents confidently, knowing that any generated code runs in an ephemeral, isolated environment, protecting your host machine and infrastructure.
5οΈβ£ Easy to Integrate π οΈ
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backendβno need for complex security setups!
6οΈβ£ Perfect for Autonomous AI Agents π€
If your AI agents generate and execute code dynamically, this is a must-have to avoid security pitfalls while enabling advanced automation.
β‘ Get started now: https://github.com/huggingface/smolagents
What will you build with smolagents? Let us know! ππ‘
albertvillanovaΒ
posted
an
update
about 1 month ago
Post
3789
π Introducing
@huggingface
Open Deep-Researchπ₯
In just 24 hours, we built an open-source agent that:
β Autonomously browse the web
β Search, scroll & extract info
β Download & manipulate files
β Run calculations on data
55% on GAIA validation set! Help us improve it!π‘
https://huggingface.co/blog/open-deep-research
In just 24 hours, we built an open-source agent that:
β Autonomously browse the web
β Search, scroll & extract info
β Download & manipulate files
β Run calculations on data
55% on GAIA validation set! Help us improve it!π‘
https://huggingface.co/blog/open-deep-research
- 3 replies
albertvillanovaΒ
posted
an
update
2 months ago
Post
2105
Discover all the improvements in the new version of Lighteval: https://huggingface.co/docs/lighteval/
Umashanks99Β
authored
a
paper
3 months ago
AnanthZekeΒ
authored
a
paper
3 months ago
AshwinSankarΒ
authored
a
paper
3 months ago
albertvillanovaΒ
posted
an
update
4 months ago
Post
1815
π¨ How green is your model? π± Introducing a new feature in the Comparator tool: Environmental Impact for responsible #LLM research!
π open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!
π The Comparator calculates COβ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... π οΈ
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!
π open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!
π The Comparator calculates COβ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... π οΈ
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!
SshubamΒ
authored
2
papers
4 months ago
albertvillanovaΒ
posted
an
update
4 months ago
Post
1607
π New feature of the Comparator of the π€ 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!
π 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? π Try the π€ 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 π
π 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? π Try the π€ 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 π
DeovratΒ
authored
a
paper
5 months ago
albertvillanovaΒ
posted
an
update
5 months ago
Post
3192
π Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! π
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?
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
5 months ago
Post
1259
π¨ 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? π Compare models:
open-llm-leaderboard/comparator