NANI-Nithin/K2-Horizon-MoVA-36B-A4B-GGUF
Text Generation • 37B • Updated • 25.1k • 12
Christian
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We got gpt6 before gta6
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My (really ugly) thoughts on NVIDIA acquiring Hugging Face
I don’t care if you love me or hate me – something about one of the most open community efforts ever to achieve the tagline “The community building the future“ getting gobbled up by a company that arguably is the biggest hardware monopoly that has ever existed strikes me as deeply unsettling. I don’t like monopolies, and that is that. The whole appeal of HF for me personally was always having a neutral location where anyone could develop, deploy, and test a model on their silicon of choice without being pushed into a single “official“ proprietary infrastructure stack.
I am not going to pretend that I would believe NVIDIA “open and independent“ is ever going to happen – hell we have all heard the same lines dozens of times from every corporation that has ever uttered them before.
When the single biggest producer of compute also is one of the primary locations where all open weights live, it becomes very hard not to imagine where all of this is going to end up soon enough if we continue to let companies dictate the narrative. It might be the hyperbole but it is an absolute truth for me – open-sourced AI cannot be a slave to the whims of a trillion dollar company. It is high time we realize that open AI cannot live and breathe only on the goodwill of corporate entities.
I don’t care if you love me or hate me – something about one of the most open community efforts ever to achieve the tagline “The community building the future“ getting gobbled up by a company that arguably is the biggest hardware monopoly that has ever existed strikes me as deeply unsettling. I don’t like monopolies, and that is that. The whole appeal of HF for me personally was always having a neutral location where anyone could develop, deploy, and test a model on their silicon of choice without being pushed into a single “official“ proprietary infrastructure stack.
I am not going to pretend that I would believe NVIDIA “open and independent“ is ever going to happen – hell we have all heard the same lines dozens of times from every corporation that has ever uttered them before.
When the single biggest producer of compute also is one of the primary locations where all open weights live, it becomes very hard not to imagine where all of this is going to end up soon enough if we continue to let companies dictate the narrative. It might be the hyperbole but it is an absolute truth for me – open-sourced AI cannot be a slave to the whims of a trillion dollar company. It is high time we realize that open AI cannot live and breathe only on the goodwill of corporate entities.
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reacted to mrs83's post with 🔥 6 days ago
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Hi Hugging Face community! 👋 we just submitted our project, OpenAIRE Graph Evaluator, to the OpenAIRE AI Hackathon, and community voting is open until September 11th!
We developed a new classifier model and an LLM-as-judge pipeline to automatically classify millions of unlabelled research papers within the OpenAIRE Graph.
The goal is to save researchers hundreds of hours spent manually sorting datasets, methodologies, and reviews, while creating a self-improving pipeline to generate datasets for training future classifiers!
We would love your support: please vote for "02-OpenAIRE Graph Evaluator" (Theme B) here: https://innovation.openaire.eu/component/content/article/hackathon-community-vote.html?catid=8
We built a custom model, Echo-DSRN, a hybrid recurrent architecture designed for resource-constrained tasks. It features an internal surprise gate and decouples computation into a fast GRU, sliding-window attention, and a gated slow state.
It comes in 4 variants:
- Hybrid RNN: Lightweight generation and speculative drafting.
- Transformers Hybrid: DSRN memory blocks injected into backbones like Qwen 2.5.
- Embedding: Dense sentence embeddings compatible with sentence-transformers.
- Classifier: The specialized engine powering our hackathon submission!
Thanks for your support! Let us know if you have any questions about the architecture!
---
Collab Hub: https://openaire-2026.ethicalabs.ai/collab
GitHub: https://github.com/ethicalabs-ai/Echo-DSRN
HF Hackathon Collection: https://huggingface.co/collections/ethicalabs/openaire-ai-hackathon-2026
We developed a new classifier model and an LLM-as-judge pipeline to automatically classify millions of unlabelled research papers within the OpenAIRE Graph.
The goal is to save researchers hundreds of hours spent manually sorting datasets, methodologies, and reviews, while creating a self-improving pipeline to generate datasets for training future classifiers!
We would love your support: please vote for "02-OpenAIRE Graph Evaluator" (Theme B) here: https://innovation.openaire.eu/component/content/article/hackathon-community-vote.html?catid=8
We built a custom model, Echo-DSRN, a hybrid recurrent architecture designed for resource-constrained tasks. It features an internal surprise gate and decouples computation into a fast GRU, sliding-window attention, and a gated slow state.
It comes in 4 variants:
- Hybrid RNN: Lightweight generation and speculative drafting.
- Transformers Hybrid: DSRN memory blocks injected into backbones like Qwen 2.5.
- Embedding: Dense sentence embeddings compatible with sentence-transformers.
- Classifier: The specialized engine powering our hackathon submission!
Thanks for your support! Let us know if you have any questions about the architecture!
---
Collab Hub: https://openaire-2026.ethicalabs.ai/collab
GitHub: https://github.com/ethicalabs-ai/Echo-DSRN
HF Hackathon Collection: https://huggingface.co/collections/ethicalabs/openaire-ai-hackathon-2026
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Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original
MultiverseComputingCAI
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