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https://nlp.uit.edu.vn/
uitnlp
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Natural Language Processing, Computational Linguistics

Recent Activity

ThuanPhong  new activity 15 days ago
uitnlp/CafeBERT:Update README.md
kietnv  authored a paper 2 months ago
ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation
ngwgsang  authored a paper 2 months ago
A Large-Scale Benchmark for Vietnamese Sentence Paraphrases
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albertvillanova 
posted an update about 2 hours ago
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21
🚀 smolagents v1.21.0 is here!
Now with improved safety in the local Python executor: dunder calls are blocked!
⚠️ Still, not fully isolated: for untrusted code, use a remote executor instead: Docker, E2B, Wasm.
✨ Many bug fixes: more reliable code.
👉 https://github.com/huggingface/smolagents/releases/tag/v1.21.0
ThuanPhong 
in uitnlp/CafeBERT 15 days ago

Update README.md
#2 opened 15 days ago by
ngwgsang
albertvillanova 
posted an update about 1 month ago
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588
🚀 New in smolagents v1.20.0: Remote Python Execution via WebAssembly (Wasm)

We've just merged a major new capability into the smolagents framework: the CodeAgent can now execute Python code remotely in a secure, sandboxed WebAssembly environment!

🔧 Powered by Pyodide and Deno, this new WasmExecutor lets your agent-generated Python code run safely: without relying on Docker or local execution.

Why this matters:
✅ Isolated execution = no host access
✅ No need for Python on the user's machine
✅ Safer evaluation of arbitrary code
✅ Compatible with serverless / edge agent workloads
✅ Ideal for constrained or untrusted environments

This is just the beginning: a focused initial implementation with known limitations. A solid MVP designed for secure, sandboxed use cases. 💡

💡 We're inviting the open-source community to help evolve this executor:
• Tackle more advanced Python features
• Expand compatibility
• Add test coverage
• Shape the next-gen secure agent runtime

🔗 Check out the PR: https://github.com/huggingface/smolagents/pull/1261

Let's reimagine what agent-driven Python execution can look like: remote-first, wasm-secure, and community-built.

This feature is live in smolagents v1.20.0!
Try it out.
Break things. Extend it. Give us feedback.
Let's build safer, smarter agents; together 🧠⚙️

👉 https://github.com/huggingface/smolagents/releases/tag/v1.20.0

#smolagents #WebAssembly #Python #AIagents #Pyodide #Deno #OpenSource #HuggingFace #AgenticAI
albertvillanova 
posted an update about 2 months ago
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1661
🚀 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:

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

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

🔄 Streaming Refactor
- Streaming event aggregation moved off the Model class
- ➡️ Better architecture & maintainability

📦 Output Tracking
- CodeAgent outputs are now stored in ActionStep
- ✅ More visibility and structure to agent decisions

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

📚 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

🔗 Full release notes:
https://github.com/huggingface/smolagents/releases/tag/v1.19.0

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

#smolagents #opensource #AIagents #LLM #HuggingFace
kietnv 
authored a paper 2 months ago
ngwgsang 
authored a paper 2 months ago
albertvillanova 
posted an update 3 months ago
albertvillanova 
posted an update 3 months ago
sonlam1102 
authored a paper 3 months ago
kietnv 
authored a paper 3 months ago
albertvillanova 
posted an update 4 months ago
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2801
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
albertvillanova 
posted an update 5 months ago
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4147
🚀 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
  • 2 replies
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albertvillanova 
posted an update 5 months ago
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4053
🚀 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! 🚀💡
albertvillanova 
posted an update 6 months ago
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🚀 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
  • 3 replies
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albertvillanova 
posted an update 7 months ago
ThuanPhong 
in uitnlp/CafeBERT 8 months ago

Adding `safetensors` variant of this model
#1 opened 9 months ago by
SFconvertbot
albertvillanova 
posted an update 9 months ago
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1903
🚨 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!
albertvillanova 
posted an update 9 months ago
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🚀 New feature of the Comparator of the 🤗 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!

🛠️ 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? 🏆 Try the 🤗 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 🌐
albertvillanova 
posted an update 10 months ago
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3271
🚀 Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! 📊

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?
albertvillanova 
posted an update 10 months ago
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1302
🚨 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? 📊 Compare models: open-llm-leaderboard/comparator