Model Galaxy
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17
Darwin family + 2026 trending models on the HF galaxy
Jinki Jeong PRO
Anserwise
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liked a Space 3 days ago
FINAL-Bench/model-galaxy reacted to SeaWolf-AI's post with 👍 3 days ago
🌌 Introducing Model Galaxy — a Living, Multimodal Fork of the HF Model Atlas
👉 Try it: https://huggingface.co/spaces/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 — 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) — 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 — 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 — 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 a model 3 days ago
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🌌 Introducing Model Galaxy — a Living, Multimodal Fork of the HF Model Atlas
👉 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 — 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) — 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 — 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 — 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.
👉 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 — 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) — 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 — 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 — 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.
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reacted to SeaWolf-AI's post with 🚀🤗❤️🔥 6 days ago
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🧬 Introducing Darwin-9B-NEG — the first model with Native Entropy Gating (NEG)
🔗 Try it now: FINAL-Bench/Darwin-9B-NEG
🔗 Q4 bit : FINAL-Bench/Darwin-9B-MFP4
We're thrilled to release Darwin-9B-NEG, a 9B-parameter reasoning model
that embeds an architecturally-internalised sense of self-confidence directly
into the transformer — our proprietary Native Entropy Gating (NEG) technology.
📊 GPQA Diamond (198 PhD-level questions):
▸ Baseline Darwin-9B (no NEG) → 51.01 %
▸ Pure NEG (greedy · 1× cost) → 63.64 % 🔥 +12.63 %p
▸ + Permutation (4× cost) → 76.26 %
▸ + Ensemble Refinement (~20×) → 84.34 % 🏆
With only 9 billion parameters and 1× inference cost, Pure NEG jumps
+12.63 %p over the same model without NEG. Going all-in with ensemble
refinement pushes it to 84.34 % — surpassing the published Qwen3.5-9B
leaderboard score (81.7 %) by +2.64 %p.
🔬 What makes NEG different from Multi-Turn Iteration (MTI)?
Classical MTI needs 3-8× extra inference passes. NEG instead lives
INSIDE the single decoding loop. Two tiny modules ride with the
transformer: NEG-Head predicts per-token entropy from the last hidden
state, and NEG-Gate conditionally restricts the top-k choice when
confidence is low. The gate activates in only 4.36 % of tokens —
essentially free at inference time.
✨ Key differentiators
• Architecturally internalised — model file *is* the feature
• 1× inference cost (vs. 3-8× for MTI)
• Drop-in with vLLM / SGLang / TGI / transformers — no extra engine
• +12.63 %p reasoning at zero latency overhead
• Single-file deployment, Apache 2.0 licensed
🧬 Lineage
Qwen/Qwen3.5-9B → Darwin-9B-Opus (V7 evolutionary merge) → Darwin-9B-NEG (V8 + NEG training)
#Darwin #NEG #NativeEntropyGating #GPQA #Reasoning #LLM #OpenSource #Apache2
🔗 Try it now: FINAL-Bench/Darwin-9B-NEG
🔗 Q4 bit : FINAL-Bench/Darwin-9B-MFP4
We're thrilled to release Darwin-9B-NEG, a 9B-parameter reasoning model
that embeds an architecturally-internalised sense of self-confidence directly
into the transformer — our proprietary Native Entropy Gating (NEG) technology.
📊 GPQA Diamond (198 PhD-level questions):
▸ Baseline Darwin-9B (no NEG) → 51.01 %
▸ Pure NEG (greedy · 1× cost) → 63.64 % 🔥 +12.63 %p
▸ + Permutation (4× cost) → 76.26 %
▸ + Ensemble Refinement (~20×) → 84.34 % 🏆
With only 9 billion parameters and 1× inference cost, Pure NEG jumps
+12.63 %p over the same model without NEG. Going all-in with ensemble
refinement pushes it to 84.34 % — surpassing the published Qwen3.5-9B
leaderboard score (81.7 %) by +2.64 %p.
🔬 What makes NEG different from Multi-Turn Iteration (MTI)?
Classical MTI needs 3-8× extra inference passes. NEG instead lives
INSIDE the single decoding loop. Two tiny modules ride with the
transformer: NEG-Head predicts per-token entropy from the last hidden
state, and NEG-Gate conditionally restricts the top-k choice when
confidence is low. The gate activates in only 4.36 % of tokens —
essentially free at inference time.
✨ Key differentiators
• Architecturally internalised — model file *is* the feature
• 1× inference cost (vs. 3-8× for MTI)
• Drop-in with vLLM / SGLang / TGI / transformers — no extra engine
• +12.63 %p reasoning at zero latency overhead
• Single-file deployment, Apache 2.0 licensed
🧬 Lineage
Qwen/Qwen3.5-9B → Darwin-9B-Opus (V7 evolutionary merge) → Darwin-9B-NEG (V8 + NEG training)
#Darwin #NEG #NativeEntropyGating #GPQA #Reasoning #LLM #OpenSource #Apache2
liked 8
models 7 days ago
FINAL-Bench/Darwin-28B-KR
Text Generation • 27B • Updated • 269 • 10
mradermacher/Darwin-4B-Genesis-GGUF
8B • Updated • 2.14k • 10
Cheuv/Darwin-4B-Genesis-Q8_0-GGUF
Text Generation • 8B • Updated • 82 • 10
AMAImedia/Qwen3.5-35B-A3B-Darwin-Opus-NOESIS-AWQ-INT8
Text Generation • 37B • Updated • 209 • 10
AMAImedia/Qwen3.5-35B-A3B-Darwin-Opus-NOESIS-AWQ-INT4
Text Generation • 20B • Updated • 332 • 11
AMAImedia/Qwen3-1.7B-TTS-Cross-Darwin-NOESIS-AWQ-INT4
Text-to-Speech • 2B • Updated • 79 • 14
AMAImedia/Qwen3.5-9B-Darwin-Opus-NOESIS-AWQ-INT4
Image-Text-to-Text • 9B • Updated • 842 • 11
TheCluster/Darwin-35B-A3B-Opus-MLX-mixed-9bit
Image-Text-to-Text • 35B • Updated • 155 • 10