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Alexander

AlexanderKyng
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AI & ML interests

Deeply passionate in all things AI. Junior Data Scientist & AI Engineer.

Recent Activity

reacted to Banaxi-Tech's post with 🔥 about 8 hours ago
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: https://huggingface.co/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: https://huggingface.co/BananaMind/BananaMind-KV1-8M-2Bit-Experimental Code: https://github.com/Banaxi-Tech/kv1
liked a model 3 days ago
nvidia/Qwen3.6-27B-NVFP4
updated a model 4 days ago
AlexanderKyng/Qwopus-27B-Coder-Mixed-Q5
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