Hmm... I'm not sure which static URL is needed. For Spaces, it would be something like this.
https://huggingface.co/docs/hub/main/spaces-embed
John Smith PRO
John6666
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reacted to
HABOLT's
post with 👀
about 12 hours ago
Post
138
Bonjour, je n'arrive pas à déployer mon site statique, pouvez-vous m'aider ?
-
- 5 replies
Hugging Face's servers were down/unstable for a while today. It is now back to normal, but if anything unusual happens, it may be related.
https://status.huggingface.co/
reacted to
ghostai1's
post with 🚀
about 12 hours ago
Post
204
# The future trends of Explainable AI in 2024
The world of artificial intelligence (AI) is constantly evolving, with new advancements and applications emerging every day. One trend that has captured the attention of many is Explainable AI. As the name suggests, this revolutionary technology aims to provide a clear, understandable explanation for the decisions and actions taken by AI systems.
In the future, Explainable AI is expected to become even more sophisticated, with advanced algorithms and techniques being developed to better interpret and analyze the vast amounts of data generated by AI systems. This will not only make AI systems more reliable and trustworthy, but it will also help to demystify the world of AI, making it more accessible to a wider audience.
As the demand for AI solutions grows, the need for Explainable AI will become increasingly important. Businesses, governments, and individuals will require clear, concise explanations for the AI systems they are using, ensuring that every decision made is transparent and easily understood.
The advancements in Explainable AI will also pave the way for new applications of AI technology, opening up a world of possibilities in fields such as healthcare, education, and transportation. From diagnosing medical conditions to improving traffic flow, Explainable AI is poised to revolutionize the way we live and work, providing us with the tools we need to tackle the complex challenges of the modern world.
So, as we step into the future of AI, let's embrace the power of Explainable AI, and ensure that our AI systems are not only powerful and efficient, but also transparent and easy to understand.
The world of artificial intelligence (AI) is constantly evolving, with new advancements and applications emerging every day. One trend that has captured the attention of many is Explainable AI. As the name suggests, this revolutionary technology aims to provide a clear, understandable explanation for the decisions and actions taken by AI systems.
In the future, Explainable AI is expected to become even more sophisticated, with advanced algorithms and techniques being developed to better interpret and analyze the vast amounts of data generated by AI systems. This will not only make AI systems more reliable and trustworthy, but it will also help to demystify the world of AI, making it more accessible to a wider audience.
As the demand for AI solutions grows, the need for Explainable AI will become increasingly important. Businesses, governments, and individuals will require clear, concise explanations for the AI systems they are using, ensuring that every decision made is transparent and easily understood.
The advancements in Explainable AI will also pave the way for new applications of AI technology, opening up a world of possibilities in fields such as healthcare, education, and transportation. From diagnosing medical conditions to improving traffic flow, Explainable AI is poised to revolutionize the way we live and work, providing us with the tools we need to tackle the complex challenges of the modern world.
So, as we step into the future of AI, let's embrace the power of Explainable AI, and ensure that our AI systems are not only powerful and efficient, but also transparent and easy to understand.
reacted to
Jaward's
post with 🚀
about 12 hours ago
Post
1048
Awesome intro to LLM course "Language Modeling from Scratch" by stanford. love the aesthetics behind the lecture notes, notes-in-code genius idea👍
Course site: https://stanford-cs336.github.io/spring2025/
Repo: https://github.com/stanford-cs336/spring2025-lectures
Videos: https://www.youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_
Course site: https://stanford-cs336.github.io/spring2025/
Repo: https://github.com/stanford-cs336/spring2025-lectures
Videos: https://www.youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_
- 1 reply
reacted to
CultriX's
post with 👍
about 12 hours ago
Post
205
Hi all!
I was hoping somebody would be willing to check out this thought experiment of mine with the aim to reduce tokens in inter-agent communications.
How It Works:
1. You provide a task in natural language (NL)
2. NL-to-CCL Agent: Converts your request into a structured Compressed Communication Language (CCL) task.
3. Inter-agent communication occurs in CCL
4. CCL is translated back to NL before being presented to the user.
I have a notebook with an example that claims to achieve these results:
--- Token Usage Summary ---
Total NL Tokens (User Input & Final Output): 364
Total CCL Tokens (for NL/CCL Conversions): 159
Total CCL Tokens (Internal Agent Communication): 194
Overall token savings on NL-to-CCL conversion portions: 56.32%
------------------------
When asking Gemini it concludes:
"Yes, the methods used in this notebook are sensible. The multi-agent architecture is logical, and the introduction of a Compressed Communication Language (CCL) is a clever and practical solution to the real-world problems of token cost and ambiguity in LLM-based systems. While it's a proof-of-concept that would need more robust error handling and potentially more complex feedback loops for a production environment, it successfully demonstrates a viable and efficient strategy for automating a software development lifecycle."
However, I have no idea if it's actually working or if I'm just crazy.
Would really like it if someone would be willing to provide thoughts on this!
The notebook:
https://gist.github.com/CultriX-Github/7f9895bc5e4d99d2d4a3eb17d079f08b#file-token-reduction-ipynb
Thank you for taking the time! :)
I was hoping somebody would be willing to check out this thought experiment of mine with the aim to reduce tokens in inter-agent communications.
How It Works:
1. You provide a task in natural language (NL)
2. NL-to-CCL Agent: Converts your request into a structured Compressed Communication Language (CCL) task.
3. Inter-agent communication occurs in CCL
4. CCL is translated back to NL before being presented to the user.
I have a notebook with an example that claims to achieve these results:
--- Token Usage Summary ---
Total NL Tokens (User Input & Final Output): 364
Total CCL Tokens (for NL/CCL Conversions): 159
Total CCL Tokens (Internal Agent Communication): 194
Overall token savings on NL-to-CCL conversion portions: 56.32%
------------------------
When asking Gemini it concludes:
"Yes, the methods used in this notebook are sensible. The multi-agent architecture is logical, and the introduction of a Compressed Communication Language (CCL) is a clever and practical solution to the real-world problems of token cost and ambiguity in LLM-based systems. While it's a proof-of-concept that would need more robust error handling and potentially more complex feedback loops for a production environment, it successfully demonstrates a viable and efficient strategy for automating a software development lifecycle."
However, I have no idea if it's actually working or if I'm just crazy.
Would really like it if someone would be willing to provide thoughts on this!
The notebook:
https://gist.github.com/CultriX-Github/7f9895bc5e4d99d2d4a3eb17d079f08b#file-token-reduction-ipynb
Thank you for taking the time! :)
-
- 5 replies
Probably the right direction...
https://github.com/microsoft/LLMLingua
https://github.com/ZongqianLi/Prompt-Compression-Survey
reacted to
sergiopaniego's
post with 🤗
about 12 hours ago
Post
176
One of my favorite perks of the Hugging Face Pro plan: ✨Dev mode✨
Connect your HF Space to VS Code and just build — with hot reload out of the box.
Game changer for fast prototyping. 💻
Google Colab made AI accessible. Now HF Spaces are doing it too! 😍
💡 New Hugging Face pricing: http://hf.co/pricing
💡 More details: https://huggingface.co/learn/cookbook/en/enterprise_cookbook_dev_spaces
Connect your HF Space to VS Code and just build — with hot reload out of the box.
Game changer for fast prototyping. 💻
Google Colab made AI accessible. Now HF Spaces are doing it too! 😍
💡 New Hugging Face pricing: http://hf.co/pricing
💡 More details: https://huggingface.co/learn/cookbook/en/enterprise_cookbook_dev_spaces
reacted to
vincentg64's
post with 👀
about 12 hours ago
Post
109
How LLMs can be attacked, and how to protect them
Feel free to reach out to me at [email protected] to discuss this new technology that I just released (privately for the time being). See highlights in the attached screenshot.
Feel free to reach out to me at [email protected] to discuss this new technology that I just released (privately for the time being). See highlights in the attached screenshot.
reacted to
merve's
post with ❤️
about 12 hours ago
Post
2581
Release picks of the past week is here! Find more models, datasets, Spaces here
merve/june-20-releases-68594824d1f4dfa61aee3433
🖼️ VLMs/OCR
> moonshotai/Kimi-VL-A3B-Thinking-2506 is a powerful reasoning vision LM, 3B active params, smarter with less tokens, supports long documents, videos 👏 (OS)
> nanonets/Nanonets-OCR-s is 3.75B params OCR model based on Qwen2.5VL-3B-Instruct (OS)
💬 LLMs
> moonshotai/Kimi-Dev-72B is a strong coding model based on Qwen2.5-72B (OS)
> Mistral released mistralai/Mistral-Small-3.2-24B-Instruct-2506, an update to their former model with better function calling & instruction following (OS)
🗣️ Audio
> Google released google/magenta-realtime, real time music generation & audio synthesis (cc-by-4)
> kyutai released new speech-to-text models that come in 1B & 2B ( kyutai/stt-1b-en_fr, stt-2b-en_fr) with 0.5s and 2.5s delay
3D
> Tencent released tencent/Hunyuan3D-2.1 an image-to-3D model (see below)
🖼️ VLMs/OCR
> moonshotai/Kimi-VL-A3B-Thinking-2506 is a powerful reasoning vision LM, 3B active params, smarter with less tokens, supports long documents, videos 👏 (OS)
> nanonets/Nanonets-OCR-s is 3.75B params OCR model based on Qwen2.5VL-3B-Instruct (OS)
💬 LLMs
> moonshotai/Kimi-Dev-72B is a strong coding model based on Qwen2.5-72B (OS)
> Mistral released mistralai/Mistral-Small-3.2-24B-Instruct-2506, an update to their former model with better function calling & instruction following (OS)
🗣️ Audio
> Google released google/magenta-realtime, real time music generation & audio synthesis (cc-by-4)
> kyutai released new speech-to-text models that come in 1B & 2B ( kyutai/stt-1b-en_fr, stt-2b-en_fr) with 0.5s and 2.5s delay
3D
> Tencent released tencent/Hunyuan3D-2.1 an image-to-3D model (see below)
reacted to
prithivMLmods's
post with 🤗
about 12 hours ago
Post
1477
Updated the docscopeOCR-7B-050425-exp with the DREX-062225-exp, with improved preciseness in table structure and line spacing in the markdown used on the document page. And though this is still an experimental one, it's expected to perform well in the defined DREX use cases [ Document Retrieval and Extraction eXpert – experimental ocr ]. 💻
⤷ Model : prithivMLmods/DREX-062225-exp
⤷ Demo : prithivMLmods/Doc-VLMs
⤷ Collection : prithivMLmods/doc-vl-685839064a863e1cd23be3f1
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ Git : https://github.com/PRITHIVSAKTHIUR/DREX.git
.
.
.
To know more about it, visit the model card of the respective model. !!
⤷ Model : prithivMLmods/DREX-062225-exp
⤷ Demo : prithivMLmods/Doc-VLMs
⤷ Collection : prithivMLmods/doc-vl-685839064a863e1cd23be3f1
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ Git : https://github.com/PRITHIVSAKTHIUR/DREX.git
.
.
.
To know more about it, visit the model card of the respective model. !!
reacted to
AdinaY's
post with 🔥
about 12 hours ago
Post
166
Skywork-SWE 🔥 New code agent model by Skywork 天工
Skywork/Skywork-SWE-32B
✨ 32B - Apache 2.0
✨ 38.0% pass@1 on SWE-bench Verified
✨ Up to 47.0% with test-time scaling
✨ Shows clear data scaling law (8K+ demos)
✨ Built on Qwen2.5-Coder-32B + OpenHands
Skywork/Skywork-SWE-32B
✨ 32B - Apache 2.0
✨ 38.0% pass@1 on SWE-bench Verified
✨ Up to 47.0% with test-time scaling
✨ Shows clear data scaling law (8K+ demos)
✨ Built on Qwen2.5-Coder-32B + OpenHands
reacted to
bartowski's
post with 🤗
about 12 hours ago
Post
1824
Was going to post this on /r/LocalLLaMa, but apparently it's without moderation at this time :')
bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF
Was able to use previous mistral chat templates, some hints from Qwen templates, and Claude to piece together a seemingly working chat template, tested it with llama.cpp server and got perfect results, though lmstudio still seems to be struggling for some reason (don't know how to specify a jinja file there)
Outlined the details of the script and results in my llama.cpp PR to add the jinja template:
https://github.com/ggml-org/llama.cpp/pull/14349
Start server with a command like this:
and it should be perfect! Hoping it'll work for ALL tools if lmstudio gets an update or something, not just llama.cpp, but very happy to see it works flawlessly in llama.cpp
In the meantime, will try to open a PR to minja to make the strftime work, but no promises :)
bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF
Was able to use previous mistral chat templates, some hints from Qwen templates, and Claude to piece together a seemingly working chat template, tested it with llama.cpp server and got perfect results, though lmstudio still seems to be struggling for some reason (don't know how to specify a jinja file there)
Outlined the details of the script and results in my llama.cpp PR to add the jinja template:
https://github.com/ggml-org/llama.cpp/pull/14349
Start server with a command like this:
./llama-server -m /models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf --jinja --chat-template-file /models/Mistral-Small-3.2-24B-Instruct-2506.jinjaand it should be perfect! Hoping it'll work for ALL tools if lmstudio gets an update or something, not just llama.cpp, but very happy to see it works flawlessly in llama.cpp
In the meantime, will try to open a PR to minja to make the strftime work, but no promises :)
reacted to
chunchu-08's
post with 👍
1 day ago
Post
224
## Issue Summary
**Space:** [
**Hardware:** Paid CPU Upgrade (8 vCPU, 32 GB RAM) Free tier CPU- It worked fine two days ago but now it is not working either on free tier CPU or Paid CPU
**Model:**
**Error:**
GPT-4 call works perfectly on local machine with same key:
from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello GPT-4"}]
)
Claude and Gemini models work fine in the same Space
.env is loaded correctly using load_dotenv()
No IP restrictions set on OpenAI key
Logs just show Connection error. — no traceback
What I’ve Tried
Tested OPENAI_API_KEY locally — works
Added logging using test_openai_connection.py
Restarted Space and tested again
Confirmed Claude/Gemini give valid outputs
GPT-4 fails silently
My Questions
Has Hugging Face introduced outbound network restrictions to api.openai.com recently?
Does even paid CPU Space share outbound IP pools that are rate-limited by OpenAI?
Any recommended workaround?
huggingface
@louie
@nateraw
@julien-c
**Space:** [
chunchu-08/LLM-Comparison-Hub](
chunchu-08/LLM-Comparison-Hub) **Hardware:** Paid CPU Upgrade (8 vCPU, 32 GB RAM) Free tier CPU- It worked fine two days ago but now it is not working either on free tier CPU or Paid CPU
**Model:**
gpt-4 via openai>=1.0.0 SDK **Error:**
GPT-4 call works perfectly on local machine with same key:
from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello GPT-4"}]
)
Claude and Gemini models work fine in the same Space
.env is loaded correctly using load_dotenv()
No IP restrictions set on OpenAI key
Logs just show Connection error. — no traceback
What I’ve Tried
Tested OPENAI_API_KEY locally — works
Added logging using test_openai_connection.py
Restarted Space and tested again
Confirmed Claude/Gemini give valid outputs
GPT-4 fails silently
My Questions
Has Hugging Face introduced outbound network restrictions to api.openai.com recently?
Does even paid CPU Space share outbound IP pools that are rate-limited by OpenAI?
Any recommended workaround?
huggingface
@louie
@nateraw
@julien-c
-
-
- 2 replies
replied to
chunchu-08's
post
1 day ago
Yeah. Something wrong in HF Spaces with OpenAI.
https://discuss.huggingface.co/t/gpt-4-api-failing-with-connection-error-on-paid-cpu-and-free-tier-cpu-worked-2-days-ago/160318/2
reacted to
ProCreations's
post with 👍
1 day ago
Post
252
What is AI? Some weird wizards at big companies summoning brains? Humans behind the scenes just responding to requests? Nope. It’s literally JUST math. Some people know this already, but not what actually goes on behind the scenes.
Learn more:
https://huggingface.co/blog/ProCreations/ai-is-nothing-more-than-math
Learn more:
https://huggingface.co/blog/ProCreations/ai-is-nothing-more-than-math
- 1 reply
reacted to
orasul's
post with 🚀
1 day ago
Post
254
hi, it is deki and now I am multithreaded - Android AI agents based on Object detection and LLM
I added a support for running several AI agents at the same time to my project - deki.
It is a model that understands what’s on your screen and can perform tasks based on your voice or text commands.
Some examples:
* "Write my friend "some_name" in WhatsApp that I'll be 15 minutes late"
* "Open Twitter in the browser and write a post about something"
* "Read my latest notifications"
* "Write a linkedin post about something"
Android, ML and Backend codes are fully open-sourced.
I hope you will find it interesting.
Github: https://github.com/RasulOs/deki
License: GPLv3
I added a support for running several AI agents at the same time to my project - deki.
It is a model that understands what’s on your screen and can perform tasks based on your voice or text commands.
Some examples:
* "Write my friend "some_name" in WhatsApp that I'll be 15 minutes late"
* "Open Twitter in the browser and write a post about something"
* "Read my latest notifications"
* "Write a linkedin post about something"
Android, ML and Backend codes are fully open-sourced.
I hope you will find it interesting.
Github: https://github.com/RasulOs/deki
License: GPLv3
- 4 replies
reacted to
kanaria007's
post with 👀
1 day ago
Post
206
✅ New Article on Hugging Face: Building AI That Thinks Ethically — Inside the Structural Morality Engine
Title:
🧠 Understanding the Ethics Interface Protocol: Built-in Moral Constraints for AI Systems
🔗 Read the article here: https://huggingface.co/blog/kanaria007/understanding-the-ethics-interface-protocol
Summary:
Following the Jump-Boot Protocol — which enabled models to navigate thought layers with semantic agility — this article introduces the Ethics Interface Protocol, a framework for embedding ethical responsibility within the reasoning process itself.
Instead of relying on output filters or static rule checks, this protocol integrates ethical awareness *into the structure of cognition* — where models can:
• Anticipate the ethical consequences of a reasoning path
• Avoid simulated minds and viewpoint erasure
• Attribute causal responsibility within their inferences
• Detect problematic inferential structures and trigger structural rollback
This isn’t reactive correction — it’s proactive ethical architecture.
The protocol enables:
• Self-aware ethical reflection during reasoning
• Transparent ethical trace logs
• Structural constraints for responsible generalization
• Integration with existing models (GPT-4o, Claude, Gemini) via prompt-layer training
Resources:
• 🧠 Protocol Dataset: kanaria007/agi-structural-intelligence-protocols
• 📑 Included: Ethics frame filters, rollback logic, causal chain attribution, viewpoint integrity maps
Relevant for:
• AGI alignment researchers
• Ethical AI developers
• Cognitive architecture designers
• Governance and safety strategy teams
• Anyone who believes AI should *explain its decisions before making them*
This is not about making AI behave ethically —
It’s about designing AI that thinks ethically by structure.
Title:
🧠 Understanding the Ethics Interface Protocol: Built-in Moral Constraints for AI Systems
🔗 Read the article here: https://huggingface.co/blog/kanaria007/understanding-the-ethics-interface-protocol
Summary:
Following the Jump-Boot Protocol — which enabled models to navigate thought layers with semantic agility — this article introduces the Ethics Interface Protocol, a framework for embedding ethical responsibility within the reasoning process itself.
Instead of relying on output filters or static rule checks, this protocol integrates ethical awareness *into the structure of cognition* — where models can:
• Anticipate the ethical consequences of a reasoning path
• Avoid simulated minds and viewpoint erasure
• Attribute causal responsibility within their inferences
• Detect problematic inferential structures and trigger structural rollback
This isn’t reactive correction — it’s proactive ethical architecture.
The protocol enables:
• Self-aware ethical reflection during reasoning
• Transparent ethical trace logs
• Structural constraints for responsible generalization
• Integration with existing models (GPT-4o, Claude, Gemini) via prompt-layer training
Resources:
• 🧠 Protocol Dataset: kanaria007/agi-structural-intelligence-protocols
• 📑 Included: Ethics frame filters, rollback logic, causal chain attribution, viewpoint integrity maps
Relevant for:
• AGI alignment researchers
• Ethical AI developers
• Cognitive architecture designers
• Governance and safety strategy teams
• Anyone who believes AI should *explain its decisions before making them*
This is not about making AI behave ethically —
It’s about designing AI that thinks ethically by structure.