shorecode/t5-efficient-tiny-summarizer-general-purpose-v3
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shorecode/t5-efficient-tiny-summarizer-general-purpose-v3 reacted to Banaxi-Tech's post with 🔥 3 days 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/kv1Organizations
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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
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
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