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Croc-Prog-HF
Croc-Prog-HF
AI & ML interests
High-temperature Text-Generation("Creativity" of the model) and Etical AI Alignment.
Democratizing local inference: reducing hardware requirements. Against the oligopoly of huge and polluting LLMs.
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Introducing WM Bench: A Benchmark for Cognitive Intelligence in World Models updated a collection 1 day ago
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Introducing WM Bench: A Benchmark for Cognitive Intelligence in World Models
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reactedto SeaWolf-AI's post with ❤️ 1 day ago
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🌍 World Model Bench — does your world model actually think?
FID measures realism. FVD measures smoothness. But neither tells you whether the model understood the scene.
We just released WM Bench — the first benchmark for cognitive intelligence in world models. The core question: when a beast charges from 3 meters away, does the model know to sprint — not walk? Does it respond differently to a human vs an animal? Does it remember the left corridor was blocked two steps ago?
Those are cognitive questions. No existing benchmark asks them. So we built one.
3 Pillars · 10 Categories · 100 Scenarios · 1,000-point scale
- 👁 P1 Perception (25%) — Can it read the scene?
- 🧠 P2 Cognition (45%) — Does it predict threats, escalate emotions, utilize memory?
- 🔥 P3 Embodiment (30%) — Does the body respond with the right motion?
All evaluation is via simple JSON I/O — no 3D engine, no special hardware. Any model with an API can participate.
We also built PROMETHEUS as a live reference implementation — runs in your browser on a T4, no install needed. Combines FloodDiffusion motion generation with a LLM cognitive brain (Perceive → Predict → Decide → Act). Scored 726/1000 (Grade B) on Track C — the only directly verified model so far. Submissions from other teams very welcome.
---
🗂 Dataset → FINAL-Bench/World-Model
🌍 Demo → FINAL-Bench/World-Model
🏆 Leaderboard → FINAL-Bench/worldmodel-bench
📝 Article → https://huggingface.co/blog/FINAL-Bench/world-model
Part of the FINAL Bench Family — alongside FINAL Bench (Feb 2026). Feedback on rubrics and missing models always welcome!
FID measures realism. FVD measures smoothness. But neither tells you whether the model understood the scene.
We just released WM Bench — the first benchmark for cognitive intelligence in world models. The core question: when a beast charges from 3 meters away, does the model know to sprint — not walk? Does it respond differently to a human vs an animal? Does it remember the left corridor was blocked two steps ago?
Those are cognitive questions. No existing benchmark asks them. So we built one.
3 Pillars · 10 Categories · 100 Scenarios · 1,000-point scale
- 👁 P1 Perception (25%) — Can it read the scene?
- 🧠 P2 Cognition (45%) — Does it predict threats, escalate emotions, utilize memory?
- 🔥 P3 Embodiment (30%) — Does the body respond with the right motion?
All evaluation is via simple JSON I/O — no 3D engine, no special hardware. Any model with an API can participate.
We also built PROMETHEUS as a live reference implementation — runs in your browser on a T4, no install needed. Combines FloodDiffusion motion generation with a LLM cognitive brain (Perceive → Predict → Decide → Act). Scored 726/1000 (Grade B) on Track C — the only directly verified model so far. Submissions from other teams very welcome.
---
🗂 Dataset → FINAL-Bench/World-Model
🌍 Demo → FINAL-Bench/World-Model
🏆 Leaderboard → FINAL-Bench/worldmodel-bench
📝 Article → https://huggingface.co/blog/FINAL-Bench/world-model
Part of the FINAL Bench Family — alongside FINAL Bench (Feb 2026). Feedback on rubrics and missing models always welcome!
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reactedto reaperdoesntknow's post with 👍 3 days ago
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We present a methodology for training small language models on CPU at FP32 precision
that achieves capability-per-dollar efficiency orders of magnitude beyond GPU-based training.
Across15modelsspanningfournovelarchitecturefamilies—MixtureofAttentions(MoA),cross-
architecture fusion (Qemma), swarm intelligence (SAGI), and metric-space causal language
models (DiscoverLM)—total compute cost was $24 on a single AMD EPYC 9454P proces-
sor. We introduce seven methodological pillars: (1) FP32 precision preservation, with exper-
iments demonstrating 5,810×single-operation error and 23,225×compounding error ratio for
FP16 at network depth; (2) sparse cognitive architectures where 0.02–7% of parameters activate
per token, matching CPU branching rather than GPU SIMD; (3) developmental curriculum
training progressing from language to logic to transfer to depth; (4) continuous belt-fed data
ingestion eliminating truncation waste; (5) hardware-native optimization for AMD Zen 4 via
AOCL/OpenMP/NUMA-aware allocation; (6) self-regulating thermodynamic governance with
emergent temperature measurement grounded in L2-star discrepancy; and (7) open-standard
compute (AVX2 SIMD at FP32) free of proprietary vendor dependency. We argue that transformers were designed for GPU hardware rather than mathematical optimality, and that architecture designed for geometric correctness—metric-space attention, triangle inequality enforcement, sparse expert routing—naturally favor CPU execution. For sub-2B parameter models, CPU training produces more capable models at a fraction of the cost.
that achieves capability-per-dollar efficiency orders of magnitude beyond GPU-based training.
Across15modelsspanningfournovelarchitecturefamilies—MixtureofAttentions(MoA),cross-
architecture fusion (Qemma), swarm intelligence (SAGI), and metric-space causal language
models (DiscoverLM)—total compute cost was $24 on a single AMD EPYC 9454P proces-
sor. We introduce seven methodological pillars: (1) FP32 precision preservation, with exper-
iments demonstrating 5,810×single-operation error and 23,225×compounding error ratio for
FP16 at network depth; (2) sparse cognitive architectures where 0.02–7% of parameters activate
per token, matching CPU branching rather than GPU SIMD; (3) developmental curriculum
training progressing from language to logic to transfer to depth; (4) continuous belt-fed data
ingestion eliminating truncation waste; (5) hardware-native optimization for AMD Zen 4 via
AOCL/OpenMP/NUMA-aware allocation; (6) self-regulating thermodynamic governance with
emergent temperature measurement grounded in L2-star discrepancy; and (7) open-standard
compute (AVX2 SIMD at FP32) free of proprietary vendor dependency. We argue that transformers were designed for GPU hardware rather than mathematical optimality, and that architecture designed for geometric correctness—metric-space attention, triangle inequality enforcement, sparse expert routing—naturally favor CPU execution. For sub-2B parameter models, CPU training produces more capable models at a fraction of the cost.
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reactedto lakj7's post with 🔥 4 days ago
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Neural Gas is a classical unsupervised learning algorithm for vector quantization and topology learning, introduced in the early 1990s. It maintains a set of prototype vectors that move through the data space and gradually approximate the underlying distribution by ranking samples and adapting all units accordingly.
While the original formulation is algorithmically elegant, most existing implementations remain procedural and non-differentiable, which limits their integration with modern deep learning systems.
This project introduces a **differentiable** implementation of Neural Gas in PyTorch:
https://github.com/francesco-p/ngas-pytorch
The key idea is to reinterpret the update rules in a way that is compatible with autograd, allowing the algorithm to be embedded inside end-to-end trainable pipelines.
This shift enables several directions that are difficult or impossible with standard implementations:
- joint optimization of Neural Gas with neural networks
- inclusion of topology-learning modules inside differentiable models
- gradient-based tuning of algorithm parameters
- hybrid architectures combining representation learning and vector quantization
The repository provides a clean PyTorch implementation and focuses on making the core mechanism usable as a first-class differentiable component, rather than a standalone preprocessing step.
In parallel, an interactive playground was built to visualize the behavior of Neural Gas during training and better understand how prototypes adapt to the data distribution:
https://francesco-p.github.io/res/neural-gas/playground.html
The goal is to revisit a well-known algorithm and make it compatible with current machine learning workflows, where differentiability is a central constraint rather than an afterthought.
While the original formulation is algorithmically elegant, most existing implementations remain procedural and non-differentiable, which limits their integration with modern deep learning systems.
This project introduces a **differentiable** implementation of Neural Gas in PyTorch:
https://github.com/francesco-p/ngas-pytorch
The key idea is to reinterpret the update rules in a way that is compatible with autograd, allowing the algorithm to be embedded inside end-to-end trainable pipelines.
This shift enables several directions that are difficult or impossible with standard implementations:
- joint optimization of Neural Gas with neural networks
- inclusion of topology-learning modules inside differentiable models
- gradient-based tuning of algorithm parameters
- hybrid architectures combining representation learning and vector quantization
The repository provides a clean PyTorch implementation and focuses on making the core mechanism usable as a first-class differentiable component, rather than a standalone preprocessing step.
In parallel, an interactive playground was built to visualize the behavior of Neural Gas during training and better understand how prototypes adapt to the data distribution:
https://francesco-p.github.io/res/neural-gas/playground.html
The goal is to revisit a well-known algorithm and make it compatible with current machine learning workflows, where differentiability is a central constraint rather than an afterthought.
reactedto prithivMLmods's post with 👍 4 days ago
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4368
Flux-Klein-KV-Edit-Consistency demo is now available on Spaces. It preserves character identity and delivers high-quality, realistic results after edits. No need for any special prompts, just upload the image, type your prompt, and get the resulting image blazing fast.
🔥 Demo Space: prithivMLmods/flux-klein-kv-edit-consistency
🤗 Model: black-forest-labs/FLUX.2-klein-9b-kv
🤗 Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
🔗 Gradio Server Mode: https://www.gradio.app/main/guides/server-mode
➔ Built with Headless Gradio, an alternative to using gr.Blocks for creating the frontend and triggering events, powered by FastAPI + Gradio. You can now design the frontend however you want, with continued support for APIs, MCP, and ZeroGPU.
➔ Gradio Server Mode is now available from gradio@v6.10.0.
To learn more, visit the app page or the respective model pages.
🔥 Demo Space: prithivMLmods/flux-klein-kv-edit-consistency
🤗 Model: black-forest-labs/FLUX.2-klein-9b-kv
🤗 Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
🔗 Gradio Server Mode: https://www.gradio.app/main/guides/server-mode
➔ Built with Headless Gradio, an alternative to using gr.Blocks for creating the frontend and triggering events, powered by FastAPI + Gradio. You can now design the frontend however you want, with continued support for APIs, MCP, and ZeroGPU.
➔ Gradio Server Mode is now available from gradio@v6.10.0.
To learn more, visit the app page or the respective model pages.