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codelion  authored a paper about 1 month ago
An Empirical Study of Path Feasibility Queries
codelion  authored a paper about 1 month ago
The Dynamics of Software Composition Analysis
codelion  authored a paper about 1 month ago
End to End Verification and Validation with SPIN
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prithivMLmods 
posted an update 1 day ago
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ImageShield-MMCF — Multimodal Content Filter is a multimodal content-safety classifier built on top of Qwen3.5 and is now available on Hugging Face!

This is the preview initial version (v1.0) of the model, designed to classify visual content as Safe or Unsafe, with a particular focus on detecting Non-Consensual Intimate Imagery (NCII) and other potentially sensitive visual content.

The demo is implemented in the prithivMLmods/opencaption-4b-vl-sft Space, which serves as an active content-safety layer for computer vision tasks. It helps block NCII content generation and paves the way for more meaningful and responsible creativity.

⊹ ImageShield-MMCF-0.8B: prithivMLmods/ImageShield-MMCF-0.8B
⊹ ImageShield-MMCF-2B: prithivMLmods/ImageShield-MMCF-2B

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prithivMLmods 
posted an update 10 days ago
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The Qwen3.8 27B demo for object grounding is now available on Hugging Face Spaces.

It features three tasks: Object Detection (Bounding Boxes), Point Localization (Keypoints), and Spatial Guidance (Path Mapping).

Try it now: prithivMLmods/Qwen3.8-27B-Object-Detection
Nymbo 
posted an update 18 days ago
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Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you
Anthropic for supporting open source.

So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.

https://github.com/Nymbo/Markdown-Minimap — issues and PRs welcome.
prithivMLmods 
posted an update 29 days ago
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5522
Made a demo for Text/Image-to-3D Video and Image-to-3D Video asset generation using TRELLIS.2. It is paired with Z-Image-Turbo to accelerate the input image preprocessing pipeline, streamlining the Image-to-3D workflow. The generated GLB (GL Transmission Format) files are converted into MP4 (MPEG-4) videos, making them easy to preview and share. Try it now on Hugging Face Spaces.🤗

➠ Image-to-3D-Video-Asset-Generator: prithivMLmods/Image-to-3D-Video-Asset-Generator
➠ collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
➠ github: https://github.com/PRITHIVSAKTHIUR/Image-to-3D-Video-Asset-Generator

⤷ To learn more, visit the app page or the respective model pages.
Nymbo 
posted an update about 1 month ago
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Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
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codelion 
authored 4 papers about 1 month ago
pankajpandey-dev 
posted an update about 2 months ago
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🇮🇳 Qwen3.5-9B Hindi Instruct — it stops thinking in English
Ask base Qwen3.5-9B a question in Hindi and it burns hundreds of tokens thinking in English inside its think block before a single Devanagari word appears — then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi.
✅ Model (16-bit): pankajpandey-dev/qwen3.5-9b-hindi-instruct
✅ GGUF (Q4/Q5/Q8): pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF
✅ Try it in the browser: pankajpandey-dev/qwen3.5-9b-hindi-demo
Recipe: Unsloth + LoRA (r=16, response-only loss) on 12.9k Hindi pairs — AI4Bharat anudesh + dolly-hi + wikiHow-hi + Aya Hindi (human-written). The Q4_K_M is 5.4 GB and runs on a plain laptop CPU.
New in this run vs my earlier models: mixed in long-form native sources (wikiHow) after my last eval showed the fine-tune traded detail for conciseness — this one keeps answers detailed and native.
Part of my weekly 🇮🇳 Hindi LLM Series. Feedback welcome 🙏
#Hindi #IndicNLP #Qwen #GGUF #LocalLLM #Unsloth
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Shrijanagain 
posted an update about 2 months ago
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Welcome Researcher and Developers!

SKT AI Labs, we are pushing the boundaries of AI architecture and research—and today, we are thrilled to open our doors to the global research community!

​We warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.

​🧪 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 : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1

​🤝 Your Feedback Shapes the Future :

​If it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.

​If 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.

​We 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.

​Let's innovate and build together! 💡
codelion 
posted an update 2 months ago
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SPROG-9M — a 9.37M parameter model trained from scratch to solve GSM8K-style math without using an LLM at inference.

The model, codelion/sprog-9m, predicts symbolic programs over number slots, then a deterministic executor does the arithmetic. With a simple verifier, it reaches ~11.8% on GSM8K test.

We also released the dataset: codelion/gsm8k-synth, 117K validated synthetic GSM8K-style problems.

Tiny model, no pretraining, no LLM at inference, runs on a laptop.
pankajpandey-dev 
posted an update 2 months ago
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🇮🇳 New in my Hindi LLM Series: Gemma-4 E4B, fine-tuned for Hindi — and it runs on your laptop's CPU.
I fine-tuned Google's new Gemma-4 E4B on ~10k Hindi instruction pairs (AI4Bharat: anudesh + dolly) using Unsloth + LoRA, on a single L4 GPU.
Then I ran an honest side-by-side eval: base Gemma-4 vs my fine-tune, across 25 Hindi prompts. The results were interesting 👇
✅ My fine-tune is more concise — ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay.

✅ Pure native Hindi — base keeps slipping into English ("संतुलित आहार (Eat a Balanced Diet)", "तारा (Star)"). My fine-tune stays in clean Hindi.

✅ Tighter instruction-following — ask for a "short message" and it gives one, not a menu of options.
⚖️ And to be honest: base Gemma-4 is more detailed and comprehensive. I didn't build a "smarter" model — I built a focused, Hindi-native, edge-friendly one that runs as a 5GB GGUF (Q4) on CPU.
🔗 Try it:

Live demo (CPU): pankajpandey-dev/gemma-4-e4b-hindi-demo
GGUF (Ollama/llama.cpp): pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF
16-bit model: pankajpandey-dev/gemma-4-e4b-hindi-instruct

Built with @unsloth · Data by @ai4bharat 🙏
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
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Shrijanagain 
posted an update 2 months ago
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🚀 Big News for the AI Community! 🔥

We’re excited to release NRS_QWEN_MYTHOS_1M — a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, we’ve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.

🔥 Why This Model is a Game-Changer:
✅ 100x Reasoning Capacity — Exceptional deep logical thinking and complex problem-solving
✅ 1 Million Token Context — Perfect for massive codebases, long documents, and multi-turn agentic workflows
✅ Advanced Thinking Mode — Native <think> tags for true step-by-step Chain-of-Thought reasoning
✅ Tool-Use Ready — Optimized for Python execution, Web Search, and self-correction
✅ Blazing Fast — 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 you’re a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning — this model is built for you.

👉 Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M

Drop a comment: What will you build with it first? 👇
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity
eienmojiki 
posted an update 2 months ago
Abhaykoul 
posted an update 2 months ago
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Shipped v0.1.2 of vtx — a minimalist coding agent for the terminal.

Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.

Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.

What ships out of the box:

→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist

This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.

Apache 2.0. uv tool install vtx-coding-agent and you're running.

GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent

Built in the open. Feedback, extensions, and PRs welcome.
prithivMLmods 
posted an update 2 months ago
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Wan2.2-I2V-Fast with highly upscaled sequential frame sampling is now available as a Spaces demo, built using Wan2.2-I2V and FLUX.2-Klein. Try the demo using the links below.👇

➠ wan2.2-i2v-fast : prithivMLmods/Wan2.2-Fast
➠ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
➠ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

⤷ To learn more, visit the app page or the respective model pages.
OzTianlu 
posted an update 3 months ago
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ResNet is Explicit Euler. GPT is Implicit Euler. What Else is Hiding in Plain Sight?

Read online: https://datawhalechina.github.io/learning-terrain/

I wrote an open-source monograph on learning dynamics — The Terrain of Learning. Bilingual (Chinese/English), 4 volumes, 12 chapters, 30+ print-grade figures. Completely free (CC BY-NC-SA 4.0).

The core argument: gradient descent is not optimization. It's terrain motion. The loss function is a landscape. The gradient is the direction of slope. The optimizer is how you choose each step. Once you see it this way, everything clicks:

ResNet = explicit Euler integration on a vector field. The residual branch is the vector field. Each layer takes one Euler step.

GPT autoregression = implicit-state Euler iteration. Stable where explicit Euler explodes. That's why transformers handle long-range dependencies.

DEQ = the Banach fixed-point theorem in production. The forward pass is root-finding. There are no layers to backprop through.

KL divergence = a Bregman divergence on the entropy landscape. Your belief space is curved, not flat.

Chain-of-thought reasoning = hidden states flowing along a reasoning field toward an attractor basin. Correct answers have wide basins. The number of reasoning steps is determined by the terrain, not by the problem.

Diffusion models = systems flowing downhill along a score vector field, from noise to structure, from high energy to low energy.

The book traces one idea across 337 years — from F=ma (Newton, 1687) to H=T+V (Hamilton, 1833) to loss landscape + gradient field (2020s). Hamilton replaced a catalog of forces with one geometric object. This book does the same for deep learning.

GitHub: https://github.com/datawhalechina/learning-terrain
Discussion: https://github.com/datawhalechina/learning-terrain/discussions/2

Convergence is not hope. Convergence is geometry. You see.
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OzTianlu 
authored a paper 3 months ago
OzTianlu 
submitted a paper to Daily Papers 3 months ago