Post
4103
Join fast we will soon published tokens and all join and get started because we will soon off join request button if you want you can join fast guys
- 1 reply
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Shrijanagain
posted an update 8 days ago
Join fast we will soon published tokens and all join and get started because we will soon off join request button if you want you can join fast guys
Shrijanagain
posted an update 13 days ago
Post
2555
🚀 Bharat AI Revolution ka Hissa Banein! 🇮🇳
Kya aap Bharat ko AI ki duniya mein ek nayi pehchan dilana chahte hain ?
SKT AI Labs sirf ek naam nahi, ek mission hai—desh ko digital shakti dene ka aur "Viksit Bharat" ke sapne ko sach karne ka.
Humse Kyun Judein?
1. Desh ka Apna AI: Hum aise models bana rahe hain jo khas taur par Bharat ki zarooraton aur bhashaon ke liye hain.
2. Open Collaboration: Hamare Hugging Face repository par hamare kaam ko dekhein, test karein aur apna yogdan dein.
3. Technological Growth: Agar aap student hain, developer hain ya tech enthusiast hain, toh hamare saath naya seekhne aur grow karne ka yeh behtareen mauka hai.
Join here
sKT-Ai-Labs
🔗 sKT-Ai-Labs
Aaiye, saath milkar Bharat AI Revolution ko aage badhate hain! 💻🔥
#SKTAILabs #DigitalIndia #AIRevolution #ViksitBharat #TechInnovation #JoinTheMission
Kya aap Bharat ko AI ki duniya mein ek nayi pehchan dilana chahte hain ?
SKT AI Labs sirf ek naam nahi, ek mission hai—desh ko digital shakti dene ka aur "Viksit Bharat" ke sapne ko sach karne ka.
Humse Kyun Judein?
1. Desh ka Apna AI: Hum aise models bana rahe hain jo khas taur par Bharat ki zarooraton aur bhashaon ke liye hain.
2. Open Collaboration: Hamare Hugging Face repository par hamare kaam ko dekhein, test karein aur apna yogdan dein.
3. Technological Growth: Agar aap student hain, developer hain ya tech enthusiast hain, toh hamare saath naya seekhne aur grow karne ka yeh behtareen mauka hai.
Join here
🔗
Aaiye, saath milkar Bharat AI Revolution ko aage badhate hain! 💻🔥
#SKTAILabs #DigitalIndia #AIRevolution #ViksitBharat #TechInnovation #JoinTheMission
Shrijanagain
posted an update 14 days ago
Post
6819
SOME NEW HINDI + ENGLISH DATASETS
🔗
- sKT-Ai-Labs/HIN
- sKT-Ai-Labs/SKT-MIX
- sKT-Ai-Labs/ST-H
Download and Use And Train Models
You Can Alsoo Use ST-x-LIGHTING Module For Faster Training
pip install ST-x-LIGHT-V11
🔗
- sKT-Ai-Labs/HIN
- sKT-Ai-Labs/SKT-MIX
- sKT-Ai-Labs/ST-H
Download and Use And Train Models
You Can Alsoo Use ST-x-LIGHTING Module For Faster Training
pip install ST-x-LIGHT-V11
- 2 replies
Shrijanagain
posted an update 20 days ago
Post
5571
We are thrilled to announce the launch of SKT-OMNI-CORPUS-146T-V1, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch.
Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge.
💎 Key Highlights:
•• Massive Scale: Targeting a multi-terabyte architecture for 146T-level tokenization.
•• Pure Quality: Curated from 500+ Elite Sources
•• Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training.
🤝 Open for Collaboration!
We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together.
Explore the Dataset on Hugging Face:
🔗 https://huggingface.co/datasets/Shrijanagain/SKT-OMNI-CORPUS-146T-V1
DSR -- 🔗 https://huggingface.co/datasets/Shrijanagain/SKT-DSRx10000
#AI #MachineLearning #OpenSource #IndicAI #SKTAILABS #LLM #BigData #HuggingFace #InnovationIndia
Shrijanagain
posted an update 25 days ago
Post
5461
Surya-1.1T: Scaling Beyond Human-Level Reasoning via 146 Trillion Token Pre-training
Author: SKT AI LABS
Affiliation: SKT AI Labs / Project Surya
Model Architecture: Optimized Dense Transformer
Parameters: 1.1 Trillion
Training Tokens: 146 Trillion
Wanna collaborate us Friends let's Start Journey we have Collected 146 trillon tokens and done pre training but we need to made more powerfull
Whitepaper - https://github.com/SHRIJANAGAIN/PROFF
Author: SKT AI LABS
Affiliation: SKT AI Labs / Project Surya
Model Architecture: Optimized Dense Transformer
Parameters: 1.1 Trillion
Training Tokens: 146 Trillion
Wanna collaborate us Friends let's Start Journey we have Collected 146 trillon tokens and done pre training but we need to made more powerfull
Whitepaper - https://github.com/SHRIJANAGAIN/PROFF
- 57 replies
Post
6521
We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.
Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
- 3 replies
[bot] Conversion to Parquet
#3 opened 2 months ago
by 
parquet-converter
not-lain
updated a
dataset 2 months ago
IlyasMoutawwakil
posted an update 3 months ago
Post
3174
Transformers v5 just landed! 🚀
It significantly unifies and reduces modeling code across architectures, while opening the door to a whole new class of performance optimizations.
My favorite new feature? 🤔
The new dynamic weight loader + converter. Here’s why 👇
Over the last few months, the core Transformers maintainers built an incredibly fast weight loader, capable of converting tensors on the fly while loading them in parallel threads. This means we’re no longer constrained by how parameters are laid out inside the safetensors weight files.
In practice, this unlocks two big things:
- Much more modular modeling code. You can now clearly see how architectures build on top of each other (DeepSeek v2 → v3, Qwen v2 → v3 → MoE, etc.). This makes shared bottlenecks obvious and lets us optimize the right building blocks once, for all model families.
- Performance optimizations beyond what torch.compile can do alone. torch.compile operates on the computation graph, but it can’t change parameter layouts. With the new loader, we can restructure weights at load time: fusing MoE expert projections, merging attention QKV projections, and enabling more compute-dense kernels that simply weren’t possible before.
Personally, I'm honored to have contributed in this direction, including the work on optimizing MoE implementations and making modeling code more torch-exportable, so these optimizations can be ported cleanly across runtimes.
Overall, Transformers v5 is a strong signal of where the community and industry are converging: Modularity and Performance, without sacrificing Flexibility.
Transformers v5 makes its signature from_pretrained an entrypoint where you can mix and match:
- Parallelism
- Quantization
- Custom kernels
- Flash/Paged attention
- Continuous batching
- ...
Kudos to everyone involved! I highly recommend the:
Release notes: https://github.com/huggingface/transformers/releases/tag/v5.0.0
Blog post: https://huggingface.co/blog/transformers-v5
It significantly unifies and reduces modeling code across architectures, while opening the door to a whole new class of performance optimizations.
My favorite new feature? 🤔
The new dynamic weight loader + converter. Here’s why 👇
Over the last few months, the core Transformers maintainers built an incredibly fast weight loader, capable of converting tensors on the fly while loading them in parallel threads. This means we’re no longer constrained by how parameters are laid out inside the safetensors weight files.
In practice, this unlocks two big things:
- Much more modular modeling code. You can now clearly see how architectures build on top of each other (DeepSeek v2 → v3, Qwen v2 → v3 → MoE, etc.). This makes shared bottlenecks obvious and lets us optimize the right building blocks once, for all model families.
- Performance optimizations beyond what torch.compile can do alone. torch.compile operates on the computation graph, but it can’t change parameter layouts. With the new loader, we can restructure weights at load time: fusing MoE expert projections, merging attention QKV projections, and enabling more compute-dense kernels that simply weren’t possible before.
Personally, I'm honored to have contributed in this direction, including the work on optimizing MoE implementations and making modeling code more torch-exportable, so these optimizations can be ported cleanly across runtimes.
Overall, Transformers v5 is a strong signal of where the community and industry are converging: Modularity and Performance, without sacrificing Flexibility.
Transformers v5 makes its signature from_pretrained an entrypoint where you can mix and match:
- Parallelism
- Quantization
- Custom kernels
- Flash/Paged attention
- Continuous batching
- ...
Kudos to everyone involved! I highly recommend the:
Release notes: https://github.com/huggingface/transformers/releases/tag/v5.0.0
Blog post: https://huggingface.co/blog/transformers-v5
- 3 replies
IlyasMoutawwakil
posted an update 3 months ago
Post
2439
After 2 months of refinement, I'm happy to announce that a lot of Transformers' modeling code is now significantly more torch-compile & export-friendly 🔥
Why it had to be done 👇
PyTorch's Dynamo compiler is increasingly becoming the default interoperability layer for ML systems. Anything that relies on torch.export or torch.compile, from model optimization to cross-framework integrations, benefits directly when models can be captured as a single dynamo-traced graph !
Transformers models are now easier to:
⚙️ Compile end-to-end with torch.compile backends
📦 Export reliably via torch.export and torch.onnx.export
🚀 Deploy to ONNX / ONNX Runtime, Intel Corporation's OpenVINO, NVIDIA AutoDeploy (TRT-LLM), AMD's Quark, Meta's Executorch and more hardware-specific runtimes.
This work aims at unblocking entire TorchDynamo-based toolchains that rely on exporting Transformers across runtimes and accelerators.
We are doubling down on Transformers commitment to be a first-class citizen of the PyTorch ecosystem, more exportable, more optimizable, and easier to deploy everywhere.
There are definitely some edge-cases that we still haven't addressed so don't hesitate to try compiling / exporting your favorite transformers and to open issues / PRs.
PR in the comments ! More updates coming coming soon !
Why it had to be done 👇
PyTorch's Dynamo compiler is increasingly becoming the default interoperability layer for ML systems. Anything that relies on torch.export or torch.compile, from model optimization to cross-framework integrations, benefits directly when models can be captured as a single dynamo-traced graph !
Transformers models are now easier to:
⚙️ Compile end-to-end with torch.compile backends
📦 Export reliably via torch.export and torch.onnx.export
🚀 Deploy to ONNX / ONNX Runtime, Intel Corporation's OpenVINO, NVIDIA AutoDeploy (TRT-LLM), AMD's Quark, Meta's Executorch and more hardware-specific runtimes.
This work aims at unblocking entire TorchDynamo-based toolchains that rely on exporting Transformers across runtimes and accelerators.
We are doubling down on Transformers commitment to be a first-class citizen of the PyTorch ecosystem, more exportable, more optimizable, and easier to deploy everywhere.
There are definitely some edge-cases that we still haven't addressed so don't hesitate to try compiling / exporting your favorite transformers and to open issues / PRs.
PR in the comments ! More updates coming coming soon !
- 1 reply
ntaquan0125
authored a
paper 3 months ago
Post
2790
Genuine recommendation: You should really use this AutoHotKey macro. Save the file as
In my experience the past few months,
macros.ahk and run it. Before sending a prompt to your coding agent, press Ctrl + Alt + 1 and paste your prompt to any regular chatbot. Then send the output to the agent. This is the actual, boring, real way to "10x your prompting". Use the other number keys to avoid repeating yourself over and over again. I use this macro prolly 100-200 times per day. AutoHotKey isn't as new or hype as a lot of other workflows, but there's a reason it's still widely used after 17 years. Don't overcomplicate it.; Requires AutoHotkey v1.1+
; All macros are `Ctrl + Alt + <variable>`
^!1::
Send, Please help me more clearly articulate what I mean with this message (write the message in a code block):
return
^!2::
Send, Please make the following changes:
return
^!3::
Send, It seems you got cut off by the maximum response limit. Please continue by picking up where you left off.
returnIn my experience the past few months,
Ctrl + Alt + 1 works best with Instruct models (non-thinking). Reasoning causes some models to ramble and miss the point. I've just been using GPT-5.x for this. Post
2812
🚨 New tool for the Nymbo/Tools MCP server: The new
How it works: The tool exposes the standard discover/info/resources/validate actions. Skills live in
I've included a
Caveat: On HF Spaces,
Try it out ~ https://www.nymbo.net/nymbot
Agent_Skills tool provides full support for Agent Skills (Claude Skills but open-source).How it works: The tool exposes the standard discover/info/resources/validate actions. Skills live in
/Skills under the same File_System root, and any bundled scripts run through Shell_Command, no new infrastructure required.Agent_Skills(action="discover") # List all available skills
Agent_Skills(action="info", skill_name="music-downloader") # Full SKILL.md
Agent_Skills(action="resources", skill_name="music-downloader") # Scripts, refs, assetsI've included a
music-downloader skill as a working demo, it wraps yt-dlp for YouTube/SoundCloud audio extraction.Caveat: On HF Spaces,
Shell_Command works for most tasks, but some operations (like YouTube downloads) are restricted due to the container environment. For full functionality, run the server locally on your machine.Try it out ~ https://www.nymbo.net/nymbot
potato4code
updated a
collection 5 months ago
Post
5290
🚀 I've just shipped a major update to the Nymbo/Tools MCP server: the
Anthropic found 98.7% context savings using code execution with MCP, Cloudflare published similar findings. This is my open-source implementation of the same idea.
# The Problem
Traditional MCP exposes every tool definition directly to the model. With 12 tools, that's thousands of tokens consumed *before the conversation even starts*. Each tool call also passes intermediate results through the context window — a 10,000-row spreadsheet? That's all going into context just to sum a column.
# The Solution: One Tool to Rule Them All
Instead of the model making individual tool calls, it writes Python code that orchestrates the tools directly:
Don't know what tools are available? The agent can discover them at runtime:
The individual direct tool calls are all still there, but they can be disabled if using the
Agent_Terminal, a single "master tool" that cuts token usage by over 90%!Anthropic found 98.7% context savings using code execution with MCP, Cloudflare published similar findings. This is my open-source implementation of the same idea.
# The Problem
Traditional MCP exposes every tool definition directly to the model. With 12 tools, that's thousands of tokens consumed *before the conversation even starts*. Each tool call also passes intermediate results through the context window — a 10,000-row spreadsheet? That's all going into context just to sum a column.
# The Solution: One Tool to Rule Them All
Agent_Terminal wraps all 12 tools (Web_Search, Web_Fetch, File_System, Generate_Image, Generate_Speech, Generate_Video, Deep_Research, Memory_Manager, Obsidian_Vault, Shell_Command, Code_Interpreter) into a single Python code execution gateway.Instead of the model making individual tool calls, it writes Python code that orchestrates the tools directly:
# Search for Bitcoin price
result = Web_Search("current price of bitcoin", max_results=3)
print(result)Don't know what tools are available? The agent can discover them at runtime:
print(search_tools('image')) # Find tools by keyword
print(usage('Generate_Image')) # Get full docs for a specific toolThe individual direct tool calls are all still there, but they can be disabled if using the
Agent_Terminal. Try it now - https://www.nymbo.net/nymbot 
- 1 reply
Post
1269
The #1 trending AI/ML dataset today 🏆
Massive scale, diversity and end-to-end potential from nvidia !
nvidia/PhysicalAI-Autonomous-Vehicles
Massive scale, diversity and end-to-end potential from nvidia !
nvidia/PhysicalAI-Autonomous-Vehicles
Post
793
The new King 👑has arrived!
Moonshot AI now the top model on Hugging Face 🔥
moonshotai/Kimi-K2-Thinking
Moonshot AI now the top model on Hugging Face 🔥
moonshotai/Kimi-K2-Thinking
Post
2857
💸🤑You don’t need 100 GPUs to train something amazing!
Our Smol Training Playbook teaches you a better path to world-class LLMs, for free!
Check out the #1 trending space on 🤗 :
HuggingFaceTB/smol-training-playbook
Our Smol Training Playbook teaches you a better path to world-class LLMs, for free!
Check out the #1 trending space on 🤗 :
HuggingFaceTB/smol-training-playbook
Post
1172
I've added an 11th tool to the Nymbo/Tools MCP server, it's for your
# Obsidian_Vault Tool
It's basically a read-only version of the
Run the server locally and set the
# Web_Search Improvements
The
There's also a new date filter to limit results to those created in the past day, week, month, or year. Oh, and uhh, SafeSearch is now
# Web_Fetch Improvements
As context-efficient as the Markdown mode is for web browsing, sometimes it does lose important context in the conversion from HTML to Markdown. So I've added a new HTML mode to the
# A Note on Claude Skills
I've been having fun with the new
Happy building ~
Obsidian_Vault. I'd argue it's far more context-efficient than any other Obsidian MCP I've seen, and doesn't require any plugins. Also some big improvements to the Web_Search and Web_Fetch tools.# Obsidian_Vault Tool
It's basically a read-only version of the
File_System tool, but it works so well for navigating Obsidian without unnecessary context. It supports recursive (full-text) search across the entire vault, and supports offset so the agent can "scroll" through a document without re-consuming tokens.Run the server locally and set the
OBSIDIAN_VAULT_ROOT environment variable to your vault's root path. If you don't use Obsidian, this is perfectly usable as simply a read-only filesystem.# Web_Search Improvements
The
Web_Search tool previously just used DuckDuckGo as a backend search engine, but now it also supports Bing, Brave, Yahoo, and Wikipedia. Default engine is auto which provides results from all backends in recommended order. Still doesn't require any kind of API or auth for Web_Search.There's also a new date filter to limit results to those created in the past day, week, month, or year. Oh, and uhh, SafeSearch is now
off by default :)# Web_Fetch Improvements
As context-efficient as the Markdown mode is for web browsing, sometimes it does lose important context in the conversion from HTML to Markdown. So I've added a new HTML mode to the
Web_Fetch tool that basically executes a cURL request on the URL, returning the full HTML page if necessary.# A Note on Claude Skills
I've been having fun with the new
File_System and Shell_Command tools. Using Claude Skills doesn't currently work in the public HF space because of environment restrictions, but using Skills works perfectly well running locally.Happy building ~
Post
2045
Two new tools added to the Nymbo/Tools MCP server,
GPT-5-Codex proves that for many cases, shell commands really are all you need, and Claude Skills seem to lean into this. The thing is, nothing about the design of Claude Skills actually restricts them to proprietary models!
# File_System
There's a new directory inside the repo called
Files created in the public HF space live in the space's running container, and gets cleared when the space is restarted. When running the server locally, files are actually stored on disk.
# Shell_Exec
What good is a filesystem if you can't execute commands in that filesystem? This tool automatically detects if the server is running on Windows or Linux, and suggests using the appropriate shell (PowerShell/Bash). Both of these new tools require that the agent uses relative paths, rather than absolute paths. I could be convinced to back pedal on this.
# Closing Thoughts
The
File_System and Shell_Exec. You can theoretically do basically anything with these two tools, and it should enable support for many Claude Skills. GPT-5-Codex proves that for many cases, shell commands really are all you need, and Claude Skills seem to lean into this. The thing is, nothing about the design of Claude Skills actually restricts them to proprietary models!
# File_System
There's a new directory inside the repo called
Filesystem, that's the agent's "root". It can perform the following actions : list, read, write, append, mkdir, move, copy, delete, info, help. It's able to keep this all within the scope of one tool call by making the Action field required and all other fields optional. Using a filesystem shouldn't require 15 different tools.Files created in the public HF space live in the space's running container, and gets cleared when the space is restarted. When running the server locally, files are actually stored on disk.
# Shell_Exec
What good is a filesystem if you can't execute commands in that filesystem? This tool automatically detects if the server is running on Windows or Linux, and suggests using the appropriate shell (PowerShell/Bash). Both of these new tools require that the agent uses relative paths, rather than absolute paths. I could be convinced to back pedal on this.
# Closing Thoughts
The
File_System and Shell_Exec tools aren't super polished yet, I'll continue to improve the agent's instructions and UX of using the new tools. Most of my testing was done with gpt-oss-20b and if it messes up, it gets the gist after one failed tool call. It should work perfectly fine for the GPU poor. 
- 1 reply