nvidia/Nemotron-Image-Training-v3
Viewer ā¢ Updated ā¢ 6.92M ā¢ 3.24k ā¢ 49
Cahlen Humphreys PRO
cahlen
AI & ML interests
ā ļøš»
Recent Activity
liked a dataset 1 day ago
nvidia/Nemotron-Image-Training-v3 liked a dataset 1 day ago
open-thoughts/AgentTrove liked a model 1 day ago
XiaomiMiMo/MiMo-V2.5-ProOrganizations
liked 2
datasets 1 day ago
liked 3
models 1 day ago
reacted to prithivMLmods's post with š„ 4 days ago
Post
3640
Multimodal-Edge Demo, a node-based inference canvas demo, is now live on Spaces. It features node-based Transformers for fast inference across 10+ edge-device multimodal models on the Hub, all within a single space. The series includes models from Qwen3.5, Qwen3-VL, Gemma 4, and the LFM 2.5 VL model series, with support for reasoning and grounding tasks.
š¤ Demo: prithivMLmods/Multimodal-Edge-Node
š GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
ā Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
š¤ > To learn more, visit the app page or the respective model pages.
š¤ Demo: prithivMLmods/Multimodal-Edge-Node
š GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
ā Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
š¤ > To learn more, visit the app page or the respective model pages.
liked a
model 5 days ago
reacted to SeaWolf-AI's post with š 6 days ago
Post
5017
š Introducing Model Galaxy ā a Living, Multimodal Fork of the HF Model Atlas
š Try it: FINAL-Bench/model-galaxy
This Space is a fork of the brilliant Eliahu/Model-Atlas, the official demo of "Charting and Navigating Hugging Face's Model Atlas" (Horwitz et al., arXiv 2503.10633). Their pre-computed HF model graph is the foundation of every node and edge you see, and we are deeply grateful for its open release.
The original atlas is a static snapshot of early 2025. Model Galaxy turns it into a living, multimodal map. We injected the 2026 trending originals that did not exist when the atlas was frozen ā DeepSeek-V4, Hy3-preview, GLM-5.1, Kimi-K2, gpt-oss, Nemotron-3 Super / Nano / Omni, Hermes-4.3, Qwen3-Coder-Next, Llama-3.3, Granite-4.1, plus the latest multimodal releases (FLUX.2, ERNIE-Image, HunyuanImage / Video, LTX-2.3, Wan2.2, Kokoro-82M, VoxCPM2, Voxtral-TTS, whisper-v3-turbo, Gemma-4, Qwen3-Omni, Phi-4-mm) ā each with proper base_model lineage edges.
We also added the complete VIDRAFT Darwin family ontology: 120 nodes covering Darwin Core, AETHER, every brand variant (Rogue, AWAXIS, TenOS, Warecube), NOESIS-Darwin multimodal extensions, and 40+ community quantizations ā the most complete Darwin lineage view anywhere.
The name "Galaxy" is now literal: our three injected clusters are re-laid out as logarithmic spiral galaxies, with bigger models near the bright cores and quantizations scattering to the outer arms ā just like real star mass distribution. A top-right toggle switches between Galaxy mode (deep-space gradient with 220 animated stars) and Atlas mode (clean white panels for reports). A 15-second progress bar narrates the render, and per-modality / per-company colors make every cluster legible at a glance.
Final scale: 22,480 nodes in the default Modalities atlas, 137,324 in the Large NLP atlas, and a 277-node compact Darwin + Trending view for instant exploration. Feedback and PRs welcome.
š Try it: FINAL-Bench/model-galaxy
This Space is a fork of the brilliant Eliahu/Model-Atlas, the official demo of "Charting and Navigating Hugging Face's Model Atlas" (Horwitz et al., arXiv 2503.10633). Their pre-computed HF model graph is the foundation of every node and edge you see, and we are deeply grateful for its open release.
The original atlas is a static snapshot of early 2025. Model Galaxy turns it into a living, multimodal map. We injected the 2026 trending originals that did not exist when the atlas was frozen ā DeepSeek-V4, Hy3-preview, GLM-5.1, Kimi-K2, gpt-oss, Nemotron-3 Super / Nano / Omni, Hermes-4.3, Qwen3-Coder-Next, Llama-3.3, Granite-4.1, plus the latest multimodal releases (FLUX.2, ERNIE-Image, HunyuanImage / Video, LTX-2.3, Wan2.2, Kokoro-82M, VoxCPM2, Voxtral-TTS, whisper-v3-turbo, Gemma-4, Qwen3-Omni, Phi-4-mm) ā each with proper base_model lineage edges.
We also added the complete VIDRAFT Darwin family ontology: 120 nodes covering Darwin Core, AETHER, every brand variant (Rogue, AWAXIS, TenOS, Warecube), NOESIS-Darwin multimodal extensions, and 40+ community quantizations ā the most complete Darwin lineage view anywhere.
The name "Galaxy" is now literal: our three injected clusters are re-laid out as logarithmic spiral galaxies, with bigger models near the bright cores and quantizations scattering to the outer arms ā just like real star mass distribution. A top-right toggle switches between Galaxy mode (deep-space gradient with 220 animated stars) and Atlas mode (clean white panels for reports). A 15-second progress bar narrates the render, and per-modality / per-company colors make every cluster legible at a glance.
Final scale: 22,480 nodes in the default Modalities atlas, 137,324 in the Large NLP atlas, and a 277-node compact Darwin + Trending view for instant exploration. Feedback and PRs welcome.
liked 2
datasets 11 days ago
Great quant!!
12
#6 opened 15 days ago
by 
tasticleeze
liked a
model 11 days ago
reacted to anakin87's post with ā¤ļøš„ 12 days ago
Post
3275
A small model that struggled against a random opponent now beats GPT-5-mini at tic-tac-toe
I took LiquidAI/LFM2-2.6B and trained it through play.
š§āš³ Here's how:
1ļøā£ Build a solid RL env with Verifiers (Prime Intellect)
2ļøā£ Generate synthetic data: <200 games sampled from GPT-5-mini playing in the env
3ļøā£ SFT warm-up to teach format
4ļøā£ Group-based RL (CISPO) against opponents making 20-70% random moves
5ļøā£ RL again with stronger opponents (0-25% random moves) + 1.25 temperature to push exploration and shake off suboptimal strategies
Done! Beats GPT-5-mini š
---
š® Play against the model: anakin87/LFM2-2.6B-mr-tictactoe
š¤ Model: anakin87/LFM2-2.6B-mr-tictactoe
š Walkthrough/course: https://github.com/anakin87/llm-rl-environments-lil-course
š¤ Dataset and checkpoints: https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
I took LiquidAI/LFM2-2.6B and trained it through play.
š§āš³ Here's how:
1ļøā£ Build a solid RL env with Verifiers (Prime Intellect)
2ļøā£ Generate synthetic data: <200 games sampled from GPT-5-mini playing in the env
3ļøā£ SFT warm-up to teach format
4ļøā£ Group-based RL (CISPO) against opponents making 20-70% random moves
5ļøā£ RL again with stronger opponents (0-25% random moves) + 1.25 temperature to push exploration and shake off suboptimal strategies
Done! Beats GPT-5-mini š
---
š® Play against the model: anakin87/LFM2-2.6B-mr-tictactoe
š¤ Model: anakin87/LFM2-2.6B-mr-tictactoe
š Walkthrough/course: https://github.com/anakin87/llm-rl-environments-lil-course
š¤ Dataset and checkpoints: https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
liked a
dataset 12 days ago
liked a
model 12 days ago
upvoted a collection 12 days ago
liked 3
models 12 days ago