Post
1884
You can explore and try my Agentic Space Factory:
fffiloni/agentic-space-factory-UI-public
Be kind ๐ค
fffiloni/agentic-space-factory-UI-public
Be kind ๐ค
AI & ML interests
A central place for all AI creators wanting to use the different AI Tools that provides HuggingFace for their film creations
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Post
1765
I made a Hugging Face Space for SCAIL-2 ๐ค
Reference character + driving motion โ animated result.
A simple demo to explore the paperโs core workflow with curated examples.
๐ fffiloni/SCAIL-2
Reference character + driving motion โ animated result.
A simple demo to explore the paperโs core workflow with curated examples.
๐ fffiloni/SCAIL-2
- 1 reply
Post
795
โฑ๏ธ Built a small Space for Visual Chronometer / Pulse of Motion.
Upload a video and estimate its Physical FPS: the frame rate implied by visual motion, independent of metadata.
Useful to inspect โchronometric hallucinationโ in generated videos: clips that look smooth, but move with the wrong physical time scale.
Try it here: fffiloni/Pulse-of-Motion
Upload a video and estimate its Physical FPS: the frame rate implied by visual motion, independent of metadata.
Useful to inspect โchronometric hallucinationโ in generated videos: clips that look smooth, but move with the wrong physical time scale.
Try it here: fffiloni/Pulse-of-Motion
Shrijanagainย
posted an update 13 days ago
Post
162
Welcome Researcher and Developers!
SKT AI Labs, we are pushing the boundaries of AI architecture and researchโand today, we are thrilled to open our doors to the global research community!
โWe warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.
โ๐งช What You Can Explore:
We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.
---------- CHECK OUT:
SPACE : SKT-NRS/RD
EXPERIMENT : sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1
โ๐ค Your Feedback Shapes the Future :
โIf it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.
โIf it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.
โWe firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!
You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.
โLet's innovate and build together! ๐ก
SKT AI Labs, we are pushing the boundaries of AI architecture and researchโand today, we are thrilled to open our doors to the global research community!
โWe warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.
โ๐งช What You Can Explore:
We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.
---------- CHECK OUT:
SPACE : SKT-NRS/RD
EXPERIMENT : sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1
โ๐ค Your Feedback Shapes the Future :
โIf it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.
โIf it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.
โWe firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!
You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.
โLet's innovate and build together! ๐ก
Shrijanagainย
posted an update 16 days ago
Post
190
๐ Big News for the AI Community! ๐ฅ
Weโre excited to release NRS_QWEN_MYTHOS_1M โ a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, weโve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.
๐ฅ Why This Model is a Game-Changer:
โ 100x Reasoning Capacity โ Exceptional deep logical thinking and complex problem-solving
โ 1 Million Token Context โ Perfect for massive codebases, long documents, and multi-turn agentic workflows
โ Advanced Thinking Mode โ Native <think> tags for true step-by-step Chain-of-Thought reasoning
โ Tool-Use Ready โ Optimized for Python execution, Web Search, and self-correction
โ Blazing Fast โ Runs smoothly on consumer GPUs like RTX 3090/4090
Technical Highlights:
Base: Qwen 3.5 9B
Tuning: NRS-specific high-quality reasoning data
Context: 1M Tokens (YaRN Scaling)
License: NRS DOCS
Whether youโre a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning โ this model is built for you.
๐ Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M
Drop a comment: What will you build with it first? ๐
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity
Weโre excited to release NRS_QWEN_MYTHOS_1M โ a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, weโve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.
๐ฅ Why This Model is a Game-Changer:
โ 100x Reasoning Capacity โ Exceptional deep logical thinking and complex problem-solving
โ 1 Million Token Context โ Perfect for massive codebases, long documents, and multi-turn agentic workflows
โ Advanced Thinking Mode โ Native <think> tags for true step-by-step Chain-of-Thought reasoning
โ Tool-Use Ready โ Optimized for Python execution, Web Search, and self-correction
โ Blazing Fast โ Runs smoothly on consumer GPUs like RTX 3090/4090
Technical Highlights:
Base: Qwen 3.5 9B
Tuning: NRS-specific high-quality reasoning data
Context: 1M Tokens (YaRN Scaling)
License: NRS DOCS
Whether youโre a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning โ this model is built for you.
๐ Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M
Drop a comment: What will you build with it first? ๐
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity
Post
1551
A few weeks ago, @victor opened the door: coding agents can now ship Hugging Face Spaces autonomously.
I pulled on that thread.
As someone who builds and ships Gradio demos regularly, I didnโt just want to reproduce the loop. I wanted to see what happens when that loop is plugged into the whole Hugging Face stack.
The interesting part is not only that an agent can ship a Space.
Itโs what happens when Space generation becomes a first-class Hugging Face workflow.
That became Agentic Space Factory.
More soon. ๐ค
I pulled on that thread.
As someone who builds and ships Gradio demos regularly, I didnโt just want to reproduce the loop. I wanted to see what happens when that loop is plugged into the whole Hugging Face stack.
The interesting part is not only that an agent can ship a Space.
Itโs what happens when Space generation becomes a first-class Hugging Face workflow.
That became Agentic Space Factory.
More soon. ๐ค
- 1 reply
eienmojikiย
posted an update 17 days ago
Post
166
Hi everyone,
I've created a Gradio space for embedding and extracting invisible watermarks in images:
๐ eienmojiki/blind-watermark-studio
It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.
Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei
:).
I've created a Gradio space for embedding and extracting invisible watermarks in images:
๐ eienmojiki/blind-watermark-studio
It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.
Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei
:).
ajibawa-2023ย
posted an update 23 days ago
Post
6870
Shell-Code-Large
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
Post
264
Released lafzyn , built over Qwen, an Urdu language model that converts Urdu text into IPA phonetic transcription, with GGUF builds for local inference.
Release contents:
- mahwizzzz/lafzyn: full weights
- mahwizzzz/lafzyn-gguf: quantized builds
Try it out ๐ค
Demo: https://huggingface.co/spaces/mahwizzzz/Lafzyn
Release contents:
- mahwizzzz/lafzyn: full weights
- mahwizzzz/lafzyn-gguf: quantized builds
Try it out ๐ค
Demo: https://huggingface.co/spaces/mahwizzzz/Lafzyn
Shrijanagainย
posted an update about 2 months ago
Post
2622
We are pleased to announce that the W-IMG Vision Dataset infrastructure is officially live.
The complete asset infrastructure is now accessible on Hugging Face for internal validation and architecture scaling targets.
Dataset Endpoint - sKT-Ai-Labs/W-IMG
#SovereignAI #ComputerVision #MachineLearning #OpenSource
The complete asset infrastructure is now accessible on Hugging Face for internal validation and architecture scaling targets.
Dataset Endpoint - sKT-Ai-Labs/W-IMG
#SovereignAI #ComputerVision #MachineLearning #OpenSource
Post
3844
I built HF Radio on Hugging Face Spaces ๐ป
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itโs not just a playlist.
Itโs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
โ> fffiloni/HF-Radio
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itโs not just a playlist.
Itโs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
โ> fffiloni/HF-Radio
iproskurinaย
authored 3
papers about 2 months ago
Fair-GPTQ: Bias-Aware Quantization for Large Language Models
Paper โข 2509.15206 โข Published โข 1
HatePrototypes: Interpretable and Transferable Representations for Implicit and Explicit Hate Speech Detection
Paper โข 2511.06391 โข Published โข 1
RILEC: Detection and Generation of L1 Russian Interference Errors in English Learner Texts
Paper โข 2603.07366 โข Published
Post
506
Great technical guide by Nico Martin on the Hugging Face blog, showing how to use Transformers.js inside a Chrome extension and run ONNX models from the Hub locally with WebGPU inside a Manifest V3 extension.
The interesting part: this is not just a chatbot in a side panel.
The article walks through the architecture behind a browser agent that can read open tabs, query webpages, search history, and highlight elements directly on the page โ with models downloaded from the Hugging Face Hub, cached under the extension origin, and executed locally instead of being called through a remote API for every prompt.
A strong blueprint for building local-first web copilots, reading assistants, and AI-powered browsing workflows.
Article: https://huggingface.co/blog/transformersjs-chrome-extension
The interesting part: this is not just a chatbot in a side panel.
The article walks through the architecture behind a browser agent that can read open tabs, query webpages, search history, and highlight elements directly on the page โ with models downloaded from the Hugging Face Hub, cached under the extension origin, and executed locally instead of being called through a remote API for every prompt.
A strong blueprint for building local-first web copilots, reading assistants, and AI-powered browsing workflows.
Article: https://huggingface.co/blog/transformersjs-chrome-extension
Post
347
Iโve been reading โWhat if AI systems werenโt chatbots?โ
What if AI systems weren't chatbots? (2605.07896) ๐
The paper asks a simple but important question: what if the chatbot interface is not just a neutral wrapper around AI models, but part of the problem?
A chatbot can make a system feel more capable, more certain, and more โhumanโ than it really is. That matters, because interfaces shape how we trust, use, and delegate to AI systems.
When everything becomes: ask โ answer
we can lose sight of the actual workflow:
- parameters
- alternatives
- uncertainty
- intermediate steps
- failure modes
- human control
For creative AI especially โ image, video, editing, animation โ Iโm not sure โchatโ should always be the default interface.
Sometimes we need a conversation.
But often we need a canvas, a timeline, sliders, masks, previews, comparisons, and visible pipelines.
This is also why I find many open ML demos interesting: Spaces, Gradio apps, visual tools, small focused interfaces.
They often explore another direction โ not just better assistants, but better tools. ๐ค
What if AI systems weren't chatbots? (2605.07896) ๐
The paper asks a simple but important question: what if the chatbot interface is not just a neutral wrapper around AI models, but part of the problem?
A chatbot can make a system feel more capable, more certain, and more โhumanโ than it really is. That matters, because interfaces shape how we trust, use, and delegate to AI systems.
When everything becomes: ask โ answer
we can lose sight of the actual workflow:
- parameters
- alternatives
- uncertainty
- intermediate steps
- failure modes
- human control
For creative AI especially โ image, video, editing, animation โ Iโm not sure โchatโ should always be the default interface.
Sometimes we need a conversation.
But often we need a canvas, a timeline, sliders, masks, previews, comparisons, and visible pipelines.
This is also why I find many open ML demos interesting: Spaces, Gradio apps, visual tools, small focused interfaces.
They often explore another direction โ not just better assistants, but better tools. ๐ค
- 2 replies
ajibawa-2023ย
posted an update 2 months ago
Post
2220
Stitched-Reasoning-Trajectories-7M
Dataset: ajibawa-2023/Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate "topic drift," every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5).
The resulting dataset consists of 709 .jsonl files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.
Dataset: ajibawa-2023/Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate "topic drift," every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5).
The resulting dataset consists of 709 .jsonl files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.
Ujjwal-Tyagiย
posted an update 2 months ago
Post
446
6 Open-Source Libraries to FineTune LLMs
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
โ Fastest way to fine-tune LLMs locally
โ Optimized for low VRAM (even laptops)
โ Plug-and-play with Hugging Face models
2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
โ Flexible LLM fine-tuning configs
โ Supports LoRA, QLoRA, multi-GPU
โ Great for custom training pipelines
3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
โ RLHF, DPO, PPO for LLM alignment
โ Built on Hugging Face ecosystem
โ Essential for post-training optimization
4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
โ Train massive models efficiently
โ Memory + speed optimization
โ Industry standard for scaling
5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
โ All-in-one fine-tuning UI + CLI
โ Supports multiple models (LLaMA, Qwen, etc.)
โ Beginner-friendly + powerful
6. PEFT
GitHub: https://github.com/huggingface/peft
โ Fine-tune with minimal compute
โ LoRA, adapters, prefix tuning
โ Best for cost-efficient training
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
โ Fastest way to fine-tune LLMs locally
โ Optimized for low VRAM (even laptops)
โ Plug-and-play with Hugging Face models
2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
โ Flexible LLM fine-tuning configs
โ Supports LoRA, QLoRA, multi-GPU
โ Great for custom training pipelines
3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
โ RLHF, DPO, PPO for LLM alignment
โ Built on Hugging Face ecosystem
โ Essential for post-training optimization
4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
โ Train massive models efficiently
โ Memory + speed optimization
โ Industry standard for scaling
5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
โ All-in-one fine-tuning UI + CLI
โ Supports multiple models (LLaMA, Qwen, etc.)
โ Beginner-friendly + powerful
6. PEFT
GitHub: https://github.com/huggingface/peft
โ Fine-tune with minimal compute
โ LoRA, adapters, prefix tuning
โ Best for cost-efficient training
- 1 reply
Sri-Vigneshwar-DJย
posted an update 2 months ago
Post
164
We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) โ 500 questions across real multi-session conversations, up to 115K tokens each.
**Score: 0.693** ยท GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**
Per-axis breakdown:
โ Info-extraction: **0.942**
โ Knowledge-update: **0.714**
โ Multi-session: **0.606**
โ Temporal: **0.477** โ the hard one, Phase 9 addresses this
Architecture: Hybrid BM25+dense ยท adaptive temporal decay ยท embedded (no server) ยท p50 = 0.19ms ยท MIT
Raw results + audit JSONs: Hawky-ai/longmemeval-results
We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) โ 500 questions across real multi-session conversations, up to 115K tokens each.
**Score: 0.693** ยท GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**
Per-axis breakdown:
โ Info-extraction: **0.942**
โ Knowledge-update: **0.714**
โ Multi-session: **0.606**
โ Temporal: **0.477** โ the hard one, Phase 9 addresses this
Architecture: Hybrid BM25+dense ยท adaptive temporal decay ยท embedded (no server) ยท p50 = 0.19ms ยท MIT
pip install feather-dbRaw results + audit JSONs: Hawky-ai/longmemeval-results
Ujjwal-Tyagiย
posted an update 3 months ago
Post
391
This is the best set of AI and ML books and a full guide to learning machine learning from the ground up. This is my study material that I used, so I thought it would be helpful to share it with others. Like, share, and add it to your collection at Ujjwal-Tyagi/ai-ml-foundations-book-collection.