Post
2313
We got gpt6 before gta6
- 13 replies
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Post
1698
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.
- 10 replies
Post
2924
Canceling My Pro Subscription
I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.
Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?
I paid for openness. Not for a corporate takeover.
🤗 was about community.
I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.
Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?
I paid for openness. Not for a corporate takeover.
🤗 was about community.
- 12 replies
tegridydev
posted an update 1 day ago
Post
1754
Been playing around with Astra the last few days and gave it my usual dumb Minecraft test lol
Started with a super vague oneshot prompt in Work on Astra Max, got a surprisingly complete voxel game back, then pushed the same project through one more Max revision and finally into Codex CLI with Astra xHigh for
Whole run was about 145 mins from first prompt to the final top-down sim version, and the overall dev experience was noticeably smoother than my similar Sol 5.6 runs.
Wrote up the process, timings, screenshots and linked the original one-shot Wildblock source here:
https://huggingface.co/blog/tegridydev/minecraft-time-with-astra-tegridydev
Source / one-shot:
https://github.com/tegridydev/tegridy/tree/main/blog/minecraft-time-with-astra
[td] tegridydev
Started with a super vague oneshot prompt in Work on Astra Max, got a surprisingly complete voxel game back, then pushed the same project through one more Max revision and finally into Codex CLI with Astra xHigh for
/plan + Astra Low for /goal.Whole run was about 145 mins from first prompt to the final top-down sim version, and the overall dev experience was noticeably smoother than my similar Sol 5.6 runs.
Wrote up the process, timings, screenshots and linked the original one-shot Wildblock source here:
https://huggingface.co/blog/tegridydev/minecraft-time-with-astra-tegridydev
Source / one-shot:
https://github.com/tegridydev/tegridy/tree/main/blog/minecraft-time-with-astra
[td] tegridydev
- 1 reply
Post
612
Hello HuggingFace!
I tested the current architect of FWKV/Myosotis-1-base (that beinng FWKV) @ different sized and sequence lengths among RWKV and Transformer architecture. I did not include Mamba since that would require a costume kernel.
Note: the evaluation might not be accurate.
CSV avalible @ FlameF0X/evals
I tested the current architect of FWKV/Myosotis-1-base (that beinng FWKV) @ different sized and sequence lengths among RWKV and Transformer architecture. I did not include Mamba since that would require a costume kernel.
Note: the evaluation might not be accurate.
CSV avalible @ FlameF0X/evals
- 2 replies
Post
641
Big shoutout to Abliterlitics.
These benchmark writeups are consistently some of the most thoughtful, detailed, and genuinely useful posts I read on open-weight model behavior and abliteration. The latest Qwen3.8-27B comparison is another great one.
I look forward to pretty much every post he puts out on the subject. If you're interested in uncensoring, abliteration, model behavior, or comparing techniques across models, definitely check his work out:
https://abliterlitics.dev
These benchmark writeups are consistently some of the most thoughtful, detailed, and genuinely useful posts I read on open-weight model behavior and abliteration. The latest Qwen3.8-27B comparison is another great one.
I look forward to pretty much every post he puts out on the subject. If you're interested in uncensoring, abliteration, model behavior, or comparing techniques across models, definitely check his work out:
https://abliterlitics.dev
Post
2400
I added recurrent depth, a gated memory, and Squeeze-and-Excitation to a chess network with no search. Same one forward pass, no tree — a deeper claim about what three architectural changes buy together.
Confirmed on three independent seeds at 1.38M games. Then, extending it, I found two bugs in my own protocol: cudnn.benchmark=True was silently undoing my determinism seed, and a comparison's random seed was derived from the model path being tested — two checkpoints played different openings against the same opponent. Both fixed, nine already-run matches discarded and rerun.
Only then did I extend one seed across 8.8x more data. Four instruments, and they don't agree: puzzles ahead 8/8, Maia ahead 23/24, a 91-pair round-robin (27,300 games) ahead 7/7 — all single-pass, no search. Minimax, the one opponent that actually searches: ahead on only 4/8, no stable direction, every swing confirmed on a second draw before I believed it.
The lesson isn't "it works." It's that an opponent that searches gives a different verdict than one that doesn't — and the two bugs that could have hidden that were caught by rules written before this run, not by anything clever during it.
Every number, including the ones minimax contradicts 👇
🤗 RDTvlokip/RDTChess
💻 https://github.com/RDTvlokip/RDTChess
♟️ https://lichess.org/@/RDTChessBot
Confirmed on three independent seeds at 1.38M games. Then, extending it, I found two bugs in my own protocol: cudnn.benchmark=True was silently undoing my determinism seed, and a comparison's random seed was derived from the model path being tested — two checkpoints played different openings against the same opponent. Both fixed, nine already-run matches discarded and rerun.
Only then did I extend one seed across 8.8x more data. Four instruments, and they don't agree: puzzles ahead 8/8, Maia ahead 23/24, a 91-pair round-robin (27,300 games) ahead 7/7 — all single-pass, no search. Minimax, the one opponent that actually searches: ahead on only 4/8, no stable direction, every swing confirmed on a second draw before I believed it.
The lesson isn't "it works." It's that an opponent that searches gives a different verdict than one that doesn't — and the two bugs that could have hidden that were caught by rules written before this run, not by anything clever during it.
Every number, including the ones minimax contradicts 👇
🤗 RDTvlokip/RDTChess
💻 https://github.com/RDTvlokip/RDTChess
♟️ https://lichess.org/@/RDTChessBot
- 2 replies
Post
5234
Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored
This is the first fine tune to exceed 730 "arc-c" ("735": 144 pts higher than Qwen 3.8 27B) AND 880 ARC-E (The OpenAI, Claude and Gemini "zone of intelligence") in 8 bit and over 718 arc-c in 4 bit.
This version is called TURBO because it drastically reduces thinking tokens (by 1/2 to as high as 1/10), yet maintains output detail and quality.
In other words while "reg" Qwen3.8 27B is thinking about "formatting" for a few 1000 tokens, this model is already done and waiting for more.
This repo contains both "regular" and "MTP" Neo-CODER MAX DI-MATRIX (duel imatrix) GGUF quants.
DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF
PS: There are 29 additional quant repos as of this writing too, as well NVFP4 and many more as well.
This is one of 10+ Qwen 3.8 27B at or above ARC-C of 717 (all 10 exceed all core benchmarks of Qwen 3.8, 3.6 and 3.5 27B and 35B-A3B versions) - you can see the complete project and some of the training here :
DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU
This is the first fine tune to exceed 730 "arc-c" ("735": 144 pts higher than Qwen 3.8 27B) AND 880 ARC-E (The OpenAI, Claude and Gemini "zone of intelligence") in 8 bit and over 718 arc-c in 4 bit.
This version is called TURBO because it drastically reduces thinking tokens (by 1/2 to as high as 1/10), yet maintains output detail and quality.
In other words while "reg" Qwen3.8 27B is thinking about "formatting" for a few 1000 tokens, this model is already done and waiting for more.
This repo contains both "regular" and "MTP" Neo-CODER MAX DI-MATRIX (duel imatrix) GGUF quants.
DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF
PS: There are 29 additional quant repos as of this writing too, as well NVFP4 and many more as well.
This is one of 10+ Qwen 3.8 27B at or above ARC-C of 717 (all 10 exceed all core benchmarks of Qwen 3.8, 3.6 and 3.5 27B and 35B-A3B versions) - you can see the complete project and some of the training here :
DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU
- 3 replies
lucifertrj
posted an update about 7 hours ago
Post
141
Published a guide to TurboQuant quantization: how the algorithm works and what Qdrant adds on top of it.
It also includes a benchmark comparing float32, scalar, binary and TurboQuant across BEIR's SciFact, ArguAna and NFCorpus, measured with recall@10, precision@10 and nDCG@10.
🔗 HF article: https://huggingface.co/blog/lucifertrj/turboquant-quantization-explained
It also includes a benchmark comparing float32, scalar, binary and TurboQuant across BEIR's SciFact, ArguAna and NFCorpus, measured with recall@10, precision@10 and nDCG@10.
🔗 HF article: https://huggingface.co/blog/lucifertrj/turboquant-quantization-explained
prithivMLmods
posted an update 2 days ago
Post
3119
VisionGuardrail, a multimodal content-safety classifier based on Qwen3.5, is now available on Hugging Face in 4B and 9B variants. It is a direct upgrade to ImageShield-MMCF, providing improved parental controls through conservative visual content-safety filtering.
More About:
➠ hf.co/blog — https://huggingface.co/blog/prithivMLmods/vision-guardrail-mini-blog
➠ Models:
✦ VisionGuardrail-4B: prithivMLmods/VisionGuardrail-4B
✦ VisionGuardrail-9B: prithivMLmods/VisionGuardrail-9B
➠ Dataset:
✦ ImageShield-Guardrail-Pro: prithivMLmods/ImageShield-Guardrail-Pro
⤷ To learn more, visit the app page or the respective model pages.
More About:
➠ hf.co/blog — https://huggingface.co/blog/prithivMLmods/vision-guardrail-mini-blog
➠ Models:
✦ VisionGuardrail-4B: prithivMLmods/VisionGuardrail-4B
✦ VisionGuardrail-9B: prithivMLmods/VisionGuardrail-9B
➠ Dataset:
✦ ImageShield-Guardrail-Pro: prithivMLmods/ImageShield-Guardrail-Pro
⤷ To learn more, visit the app page or the respective model pages.
