i actually used this invitation code and using it now to do my weekend jobs , it's very sharp ๐๐ป
Joseph [open/acc] Pollack
Tonic
AI & ML interests
๐คMaking robots to help people learn things quicker ๐ฉ๐ปโ๐๐
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liked
a model
about 8 hours ago
BlinkDL/rwkv7-g1
updated
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about 12 hours ago
cool datasets
liked
a dataset
about 12 hours ago
HuggingFaceTB/dclm-edu
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Tonic's activity
reacted to
jsulz's
post with ๐
1 day ago
Post
1989
If you haven't already, I strong recommend reading
@chiphuyen
's AI Engineering https://www.goodreads.com/en/book/show/216848047-ai-engineering
It comes complete with a section on open source AI (of obvious interest to the crowd here) and more than one mention of the Hugging Face community ๐ค
In my opinion, one of the best parts is that it is a compendium for seminal and cutting-edge AI resources, with nearly 250 arXiv papers cited. I've done my best to collect them all in a single place, organized by chapter and by order in which they appear in the book:
jsulz/ai-engineering-67c5abe02c8596b5c089934c
Happy reading ๐ค
It comes complete with a section on open source AI (of obvious interest to the crowd here) and more than one mention of the Hugging Face community ๐ค
In my opinion, one of the best parts is that it is a compendium for seminal and cutting-edge AI resources, with nearly 250 arXiv papers cited. I've done my best to collect them all in a single place, organized by chapter and by order in which they appear in the book:
jsulz/ai-engineering-67c5abe02c8596b5c089934c
Happy reading ๐ค
reacted to
albertvillanova's
post with ๐โค๏ธ
1 day ago
Post
3592
๐ Big news for AI agents! With the latest release of smolagents, you can now securely execute Python code in sandboxed Docker or E2B environments. ๐ฆพ๐
Here's why this is a game-changer for agent-based systems: ๐งต๐
1๏ธโฃ Security First ๐
Running AI agents in unrestricted Python environments is risky! With sandboxing, your agents are isolated, preventing unintended file access, network abuse, or system modifications.
2๏ธโฃ Deterministic & Reproducible Runs ๐ฆ
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable settingโno more environment mismatches or dependency issues!
3๏ธโฃ Resource Control & Limits ๐ฆ
Docker and E2B allow you to enforce CPU, memory, and execution time limits, so rogue or inefficient agents donโt spiral out of control.
4๏ธโฃ Safer Code Execution in Production ๐ญ
Deploy AI agents confidently, knowing that any generated code runs in an ephemeral, isolated environment, protecting your host machine and infrastructure.
5๏ธโฃ Easy to Integrate ๐ ๏ธ
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backendโno need for complex security setups!
6๏ธโฃ Perfect for Autonomous AI Agents ๐ค
If your AI agents generate and execute code dynamically, this is a must-have to avoid security pitfalls while enabling advanced automation.
โก Get started now: https://github.com/huggingface/smolagents
What will you build with smolagents? Let us know! ๐๐ก
Here's why this is a game-changer for agent-based systems: ๐งต๐
1๏ธโฃ Security First ๐
Running AI agents in unrestricted Python environments is risky! With sandboxing, your agents are isolated, preventing unintended file access, network abuse, or system modifications.
2๏ธโฃ Deterministic & Reproducible Runs ๐ฆ
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable settingโno more environment mismatches or dependency issues!
3๏ธโฃ Resource Control & Limits ๐ฆ
Docker and E2B allow you to enforce CPU, memory, and execution time limits, so rogue or inefficient agents donโt spiral out of control.
4๏ธโฃ Safer Code Execution in Production ๐ญ
Deploy AI agents confidently, knowing that any generated code runs in an ephemeral, isolated environment, protecting your host machine and infrastructure.
5๏ธโฃ Easy to Integrate ๐ ๏ธ
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backendโno need for complex security setups!
6๏ธโฃ Perfect for Autonomous AI Agents ๐ค
If your AI agents generate and execute code dynamically, this is a must-have to avoid security pitfalls while enabling advanced automation.
โก Get started now: https://github.com/huggingface/smolagents
What will you build with smolagents? Let us know! ๐๐ก
reacted to
as-cle-bert's
post with โค๏ธ
1 day ago
Post
2452
I just released a fully automated evaluation framework for your RAG applications!๐
GitHub ๐ https://github.com/AstraBert/diRAGnosis
PyPi ๐ https://pypi.org/project/diragnosis/
It's called ๐๐ข๐๐๐๐ง๐จ๐ฌ๐ข๐ฌ and is a lightweight framework that helps you ๐ฑ๐ถ๐ฎ๐ด๐ป๐ผ๐๐ฒ ๐๐ต๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ผ๐ณ ๐๐๐ ๐ ๐ฎ๐ป๐ฑ ๐ฟ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ถ๐ป ๐ฅ๐๐ ๐ฎ๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐.
You can launch it as an application locally (it's Docker-ready!๐) or, if you want more flexibility, you can integrate it in your code as a python package๐ฆ
The workflow is simple:
๐ง You choose your favorite LLM provider and model (supported, for now, are Mistral AI, Groq, Anthropic, OpenAI and Cohere)
๐ง You pick the embedding models provider and the embedding model you prefer (supported, for now, are Mistral AI, Hugging Face, Cohere and OpenAI)
๐ You prepare and provide your documents
โ๏ธ Documents are ingested into a Qdrant vector database and transformed into a synthetic question dataset with the help of LlamaIndex
๐ The LLM is evaluated for the faithfulness and relevancy of its retrieval-augmented answer to the questions
๐ The embedding model is evaluated for hit rate and mean reciprocal ranking (MRR) of the retrieved documents
And the cool thing is that all of this is ๐ถ๐ป๐๐๐ถ๐๐ถ๐๐ฒ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ๐น๐ ๐ฎ๐๐๐ผ๐บ๐ฎ๐๐ฒ๐ฑ: you plug it in, and it works!๐โก
Even cooler? This is all built on top of LlamaIndex and its integrations: no need for tons of dependencies or fancy workarounds๐ฆ
And if you're a UI lover, Gradio and FastAPI are there to provide you a seamless backend-to-frontend experience๐ถ๏ธ
So now it's your turn: you can either get diRAGnosis from GitHub ๐ https://github.com/AstraBert/diRAGnosis
or just run a quick and painless:
To get the package installed (lightning-fast) in your environment๐โโ๏ธ
Have fun and feel free to leave feedback and feature/integrations requests on GitHub issuesโจ
GitHub ๐ https://github.com/AstraBert/diRAGnosis
PyPi ๐ https://pypi.org/project/diragnosis/
It's called ๐๐ข๐๐๐๐ง๐จ๐ฌ๐ข๐ฌ and is a lightweight framework that helps you ๐ฑ๐ถ๐ฎ๐ด๐ป๐ผ๐๐ฒ ๐๐ต๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ผ๐ณ ๐๐๐ ๐ ๐ฎ๐ป๐ฑ ๐ฟ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ถ๐ป ๐ฅ๐๐ ๐ฎ๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐.
You can launch it as an application locally (it's Docker-ready!๐) or, if you want more flexibility, you can integrate it in your code as a python package๐ฆ
The workflow is simple:
๐ง You choose your favorite LLM provider and model (supported, for now, are Mistral AI, Groq, Anthropic, OpenAI and Cohere)
๐ง You pick the embedding models provider and the embedding model you prefer (supported, for now, are Mistral AI, Hugging Face, Cohere and OpenAI)
๐ You prepare and provide your documents
โ๏ธ Documents are ingested into a Qdrant vector database and transformed into a synthetic question dataset with the help of LlamaIndex
๐ The LLM is evaluated for the faithfulness and relevancy of its retrieval-augmented answer to the questions
๐ The embedding model is evaluated for hit rate and mean reciprocal ranking (MRR) of the retrieved documents
And the cool thing is that all of this is ๐ถ๐ป๐๐๐ถ๐๐ถ๐๐ฒ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ๐น๐ ๐ฎ๐๐๐ผ๐บ๐ฎ๐๐ฒ๐ฑ: you plug it in, and it works!๐โก
Even cooler? This is all built on top of LlamaIndex and its integrations: no need for tons of dependencies or fancy workarounds๐ฆ
And if you're a UI lover, Gradio and FastAPI are there to provide you a seamless backend-to-frontend experience๐ถ๏ธ
So now it's your turn: you can either get diRAGnosis from GitHub ๐ https://github.com/AstraBert/diRAGnosis
or just run a quick and painless:
uv pip install diragnosisTo get the package installed (lightning-fast) in your environment๐โโ๏ธ
Have fun and feel free to leave feedback and feature/integrations requests on GitHub issuesโจ
reacted to
daavoo's
post with โค๏ธ๐
1 day ago
Post
1822
Hi there ๐! Check this project for mapping features in OpenStreetMap with Computer Vision:
โญ-> https://github.com/mozilla-ai/osm-ai-helper
And a live demo showing how to map new swimming pools ๐:
๐บ๏ธ -> mozilla-ai/osm-ai-helper
โญ-> https://github.com/mozilla-ai/osm-ai-helper
And a live demo showing how to map new swimming pools ๐:
๐บ๏ธ -> mozilla-ai/osm-ai-helper
reacted to
albertvillanova's
post with ๐
1 day ago
Post
3130
๐ New smolagents update: Safer Local Python Execution! ๐ฆพ๐
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. ๐
Here's why this matters & what you need to know! ๐งต๐
1๏ธโฃ Why is local execution risky? โ ๏ธ
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.
2๏ธโฃ New Safety Layer in smolagents ๐ก๏ธ
We now inspect every return value during execution:
โ Allowed: Safe built-in types (e.g., numbers, strings, lists)
โ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3๏ธโฃ Immediate Benefits ๐ก
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities
4๏ธโฃ Security Disclaimer โ ๏ธ
๐จ Despite these improvements, local Python execution is NEVER 100% safe. ๐จ
If you need true isolation, use a remote sandboxed executor like Docker or E2B.
5๏ธโฃ The Best Practice: Use Sandboxed Execution ๐
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.
6๏ธโฃ Upgrade Now & Stay Safe! ๐
Check out the latest smolagents release and start building safer AI agents today.
๐ https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Letโs discuss! ๐
#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. ๐
Here's why this matters & what you need to know! ๐งต๐
1๏ธโฃ Why is local execution risky? โ ๏ธ
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.
2๏ธโฃ New Safety Layer in smolagents ๐ก๏ธ
We now inspect every return value during execution:
โ Allowed: Safe built-in types (e.g., numbers, strings, lists)
โ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3๏ธโฃ Immediate Benefits ๐ก
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities
4๏ธโฃ Security Disclaimer โ ๏ธ
๐จ Despite these improvements, local Python execution is NEVER 100% safe. ๐จ
If you need true isolation, use a remote sandboxed executor like Docker or E2B.
5๏ธโฃ The Best Practice: Use Sandboxed Execution ๐
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.
6๏ธโฃ Upgrade Now & Stay Safe! ๐
Check out the latest smolagents release and start building safer AI agents today.
๐ https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Letโs discuss! ๐
#AI #smolagents #Python #Security
- 2 replies
reacted to
clem's
post with โค๏ธ
1 day ago
Post
3200
10,000+ models based on Deepseek R1 have been publicly shared on Hugging Face! Which ones are your favorite ones: https://huggingface.co./models?sort=trending&search=r1. Truly game-changer!
replied to
their
post
2 days ago
sure you can , check the links in the demo and the codebase that is available with each :-)
posted
an
update
3 days ago
Post
744
๐๐ปโโ๏ธHey there folks,
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
๐๐ปif you like this topic please contribute code upstream ๐
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
๐๐ปif you like this topic please contribute code upstream ๐
-
- 2 replies
posted
an
update
4 days ago
Post
570
Powered by
KRLabsOrg/lettucedect-large-modernbert-en-v1 from KRLabsOrg.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](https://huggingface.co./KRLabsOrg)
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](https://huggingface.co./KRLabsOrg)
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
reacted to
Locutusque's
post with ๐๐๐ฅ๐คโค๏ธ
12 days ago
Post
2457
๐ Exciting news, everyone! I've just released **Thespis-Llama-3.1-8B**, a new language model designed for enhanced roleplaying! โจ๏ธ
It's built on Llama-3.1 and fine-tuned with a focus on Theory of Mind reasoning to create more believable and engaging characters. It even learned a few tricks on its own, like adding in-character thought processes! ๐ง
Check it out here: Locutusque/Thespis-Llama-3.1-8B
Give it a try and let me know what you think! I'm especially interested in feedback on how well the characters stay in role and if the responses feel natural. Looking forward to seeing what amazing stories you create! โ๏ธ
It's built on Llama-3.1 and fine-tuned with a focus on Theory of Mind reasoning to create more believable and engaging characters. It even learned a few tricks on its own, like adding in-character thought processes! ๐ง
Check it out here: Locutusque/Thespis-Llama-3.1-8B
Give it a try and let me know what you think! I'm especially interested in feedback on how well the characters stay in role and if the responses feel natural. Looking forward to seeing what amazing stories you create! โ๏ธ
reacted to
freddyaboulton's
post with ๐๐ค๐ง
12 days ago
Post
3105
Getting WebRTC and Websockets right in python is very tricky. If you've tried to wrap an LLM in a real-time audio layer then you know what I'm talking about.
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
Check out our org: hf.co/fastrtc
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
Check out our org: hf.co/fastrtc