AlekseyCalvin/Lyrical_ru2en_Gemma4_SFT_3960
Text Generation • 8B • Updated • 1
Jack Voide
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Aratako/gemma-4-E4B-it-audio-encoder
Feature Extraction • Updated • 157 • 13
Qwen/Qwen3-Embedding-0.6B
Feature Extraction • 0.6B • Updated • 5.78M • • 962
Hcompany/Holo3-35B-A3B
Image-Text-to-Text • 35B • Updated • 1.53k • 223
HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive
Image-Text-to-Text • 8B • Updated • 57.6k • 107
HauhauCS/Gemma-4-E2B-Uncensored-HauhauCS-Aggressive
Image-Text-to-Text • 5B • Updated • 14.7k • 30
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reactedto DedeProGames's post with 👍 3 days ago
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2881
🔥 GRM2 - The small one that surpasses the big ones.
What if a 3-parameter model can beat a 32-parameter model in every benchmark? We prove that it can.
GRM2 is a 3b params model based on the llama architecture, trained for long reasoning and high performance in complex tasks - the first 3b params model to outperform qwen3-32b in ALL benchmarks, and outperform o3-mini in almost all benchmarks.
🤗 Model: OrionLLM/GRM2-3b
The first 3b params model to generate over 1000 lines of code and achieve a score of 39.0 in xBench-DeepSearch-2510.
🚀 Chat with GRM:
DedeProGames/GRM2-Chat
🏆 Download official GGUFs: OrionLLM/GRM2-3b-GGUF
What if a 3-parameter model can beat a 32-parameter model in every benchmark? We prove that it can.
GRM2 is a 3b params model based on the llama architecture, trained for long reasoning and high performance in complex tasks - the first 3b params model to outperform qwen3-32b in ALL benchmarks, and outperform o3-mini in almost all benchmarks.
🤗 Model: OrionLLM/GRM2-3b
The first 3b params model to generate over 1000 lines of code and achieve a score of 39.0 in xBench-DeepSearch-2510.
🚀 Chat with GRM:
DedeProGames/GRM2-Chat
🏆 Download official GGUFs: OrionLLM/GRM2-3b-GGUF
reactedto reaperdoesntknow's post with 👍 5 days ago
Post
1955
Your Loss Function Has Singularities. Classical Calculus Can't See Them.
Introducing Discrepancy Calculus (DISC) — treating training singularities as structure, not noise.
Loss plateaus, mode collapse, catastrophic forgetting, distilled models that know things the teacher never taught — we engineer around these. But what if those singularities are the actual structure of the learning problem?
The core insight: Every BV function decomposes into smooth (what classical calculus handles), jump (capability emergence, loss plateaus breaking), and Cantor (ghost imprinting — knowledge transferring through weight-space topology, not gradient signal). Classical analysis sees only the first. DISC sees all three.
The paper proves this isn't alternative notation — it's strictly larger. The Meta-Discrepancy Theorem: where singularities exist, the classical FTC/MVT/chain-rule package is provably impossible.
What it explains:
TopologicalQwen exhibited literary reasoning from physics-only data — the Cantor part explains how. DualMind's Explore→Examine→Response loop operationalizes DISC as inference dynamics. 50 models, 35K+ downloads, all built on this framework.
Paper: Discrepancy Calculus: Foundations and Core Theory (DOI: 10.57967/hf/8194) — 8 axioms, proofs, computational recipes.
Series: Structure Over Scale (DOI: 10.57967/hf/8165) → Three Teachers to Dual Cognition (DOI: 10.57967/hf/8184) → DISC Foundations
— Roy S. Colca Jr., Convergent Intelligence LLC: Research Division
Introducing Discrepancy Calculus (DISC) — treating training singularities as structure, not noise.
Loss plateaus, mode collapse, catastrophic forgetting, distilled models that know things the teacher never taught — we engineer around these. But what if those singularities are the actual structure of the learning problem?
The core insight: Every BV function decomposes into smooth (what classical calculus handles), jump (capability emergence, loss plateaus breaking), and Cantor (ghost imprinting — knowledge transferring through weight-space topology, not gradient signal). Classical analysis sees only the first. DISC sees all three.
The paper proves this isn't alternative notation — it's strictly larger. The Meta-Discrepancy Theorem: where singularities exist, the classical FTC/MVT/chain-rule package is provably impossible.
What it explains:
TopologicalQwen exhibited literary reasoning from physics-only data — the Cantor part explains how. DualMind's Explore→Examine→Response loop operationalizes DISC as inference dynamics. 50 models, 35K+ downloads, all built on this framework.
Paper: Discrepancy Calculus: Foundations and Core Theory (DOI: 10.57967/hf/8194) — 8 axioms, proofs, computational recipes.
Series: Structure Over Scale (DOI: 10.57967/hf/8165) → Three Teachers to Dual Cognition (DOI: 10.57967/hf/8184) → DISC Foundations
— Roy S. Colca Jr., Convergent Intelligence LLC: Research Division
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UnstableLlama/DavidAU_Qwen3.5-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-4.00bpw-exl3
11B • Updated • 61 • 3
ibm-granite/granite-4.0-3b-vision
Image-Text-to-Text • 4B • Updated • 4.99k • 79
UnstableLlama/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-4.05bpw-exl3
Text Generation • 10B • Updated • 216 • 1
reactedto MaziyarPanahi's post with 👍👍 10 days ago
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2139
We annotated 119K medical images with two frontier VLMs (Qwen 3.5, Kimi K2.5), cross-validated at 93% agreement, and produced 110K training records, all for under $500. Fine-tuning 3 small models (2-3B params) improved all benchmarks: best model reaches +15.0% average exact match.
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
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