alkinun

AtAndDev

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alkinun

AI & ML interests

decentralize

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upvoted an article about 10 hours ago

Security incident disclosure — July 2026

reacted to appvoid's post with 👀 about 14 hours ago

small reasoning models are overrated, these little ones just doom loop a lot by default. good data will always be the moat when training or finetuning small models and latest sota models like fable 5 and gpt 5.6 are increasingly making this a lot easier to do.

liked a model about 15 hours ago

InternScience/Agents-A1

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reacted to appvoid's post with 👀 about 14 hours ago

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197

2 replies

reacted to ProCreations's post with 👍 about 18 hours ago

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2344

grug-v2 here. grug v1 good but lost tool use brain. grug v2 RL fix. grug v2 more SFT to make more grug.

Try grug v2 here:
ProCreations/grug-v2-9b-gguf
ProCreations/grug-v2-9b
Demo space here:
ProCreations/grug-v2-9b-demo

reacted to danielhanchen's post with 🤗😎👍🚀 about 23 hours ago

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3371

Gemma 4 is now faster and much more accurate! 🚀

Google made huge improvements to tool-calling and chat accuracy, reliability + speed.
To get fixes, re-download our updated GGUF, MLX, NVFP4 quants!

Unsloth quants: https://huggingface.co/collections/unsloth/gemma-4
Gemma 4 Guide: https://unsloth.ai/docs/models/gemma-4

6 replies

reacted to s3nh's post with 👀❤️ 2 days ago

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335

Existing methods — GPTQ, AWQ, llama.cpp's k-quants — minimize empirical loss heuristically. None of them prove they are optimal in any information-theoretic sense. ICRB-Q builds a quantization scheme that is provably optimal via the Cramér-Rao lower bound (CRB): no unbiased estimator of a weight can have lower variance than [F(θ)]⁻¹, where F is the Fisher information matrix.

1 reply

replied to ProCreations's post 2 days ago

man.. someone get this man a sponsor

reacted to ProCreations's post with 😔🔥🤝 2 days ago

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3151

sad grug news:
I have burnt a lot of GPU credits already just making grug and it’s variants (I do not own a workstation yet) and grug 35b has a pretty bad issue but if I continue burning GPU credits to fix it then it will take even longer to get the workstation I am working for… so the fix may take a few days or never happen.

The error:
I tested it in opencode after benchmarks were good and after release (my mistake) and it had pretty bad repetition failure and simply didn’t work. so.. yea.

4 replies

reacted to s3nh's post with 👍 7 days ago

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267

Uncensoring Mistral,

give it a try
s3nh/Ministral-3-14B-Instruct-2512-BF16-abliterated

reacted to ProCreations's post with 🤗😎 7 days ago

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526

I won 4th place on the huggingface hackathon, genuinely so happy. Congrats to everyone that won, this has been super fun

reacted to Banaxi-Tech's post with 🤗👀🔥 14 days ago

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4242

Today we are releasing BananaMind-KV1-8M-2Bit-Experimental, a KV-cache-aware trained model that stores its generation KV cache in 2-bit precision instead of the usual 16-bit precision.

Result: 5.33x smaller KV cache vs FP16, with 0.0916 mean KLD against a 16-bit KV cache reference on WikiText-2.

Model: BananaMind/BananaMind-KV1-8M-2Bit-Experimental

The important part: this is not just post-training KV cache quantization.
Instead we take the BitNet approach.

KV1 is trained with a 2-bit-aware K/V path. Instead of training a normal model and quantizing the cache afterwards, the model learns during training to operate under the low-bit KV constraint, closer in spirit to the BitNet idea of training for the low-bit regime.

During generation, each K/V vector is quantized into 4 affine levels and packed into uint8 tensors, with four 2-bit values stored per byte.

WikiText-2 eval vs 16-bit KV cache reference:

Mean KLD: 0.0916 nats/token
Mean KLD: 0.1322 bits/token
Average KV cache shrink vs FP16: 5.33x
Evaluated positions: 372,675

If this actually gets used in models like Qwen or Gemma, then it may be possible to run 128K or even 256K Context on a Normal Machine!
Try it here: BananaMind/BananaMind-KV1-8M-2Bit-Experimental

Code: https://github.com/Banaxi-Tech/kv1

4 replies

reacted to aufklarer's post with 🔥 16 days ago

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1345

Voice cloning models measured across five languages: OmniVoice, Chatterbox, VoxCPM2, Fish Audio

I published a new Soniqo benchmark post for local voice cloning models across five languages:

https://www.soniqo.audio/blog/voice-cloning-benchmarks

Models:

- OmniVoice int8
- Chatterbox Multilingual fp16
- VoxCPM2 bf16
- Fish Audio S2 Pro fp16

Languages:

- English
- German
- Modern Standard Arabic
- Spanish
- Mandarin Chinese

The benchmark uses Google FLEURS test clips as dataset references. Each row includes the reference audio, generated audio, speaker similarity, WER/CER, generated audio length, and RTF.

Main result in this run: OmniVoice was the strongest all-around row set, with 0.707 mean speaker cosine across all five languages, 0.0% ASR error, and mean RTF 0.45. VoxCPM2 bf16 was especially strong on Arabic speaker match. Fish Audio S2 Pro showed strong German/Arabic similarity but slower RTF. Chatterbox Multilingual was competitive on Arabic and Spanish.

This is an engineering benchmark, not a human MOS study. The speaker-similarity values should be compared within this table because every row uses the same local speaker-embedding pipeline.

Try the stack locally with Speech Studio:

https://www.soniqo.audio/speech-studio
https://github.com/soniqo/speech-studio

Underlying Swift library/CLI:

https://github.com/soniqo/speech-swift

Soniqo models and exports:

soniqo @aufklarer

What model or language should I add next?

reacted to prithivMLmods's post with 🔥 about 2 months ago

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5612

I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached “Space of the Week”! A few Spaces are still topping the list even after many months.

Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.

Thanks for all the community support! 🤗❤️

🔗 Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

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alkinun

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