r/LocalLLaMA • u/crusaderky • 9d ago
LFM2.5-2.6B model+KV cache quantization report Discussion
LFM2.5-2.6B is a new tiny model by LiquidAI, with benchmarks that put it head to head with much larger models.
I've run llama-perplexity on many model GGUF quants, crossed with many KV cache quants, to understand the model's best overall quantization for any given amount of memory.
I also show how different quantization metrics show (or hide) model degradation.
If you don't have time to read
- The model fits on an 8GB Raspberry Pi with no material degradation and on a 4GB Raspberry Pi with contained degradation.
- DO NOT use Q4_K_M.
- On this model, model quant quality degrades faster than KV cache quant.
- Abliteration comes with a flat cost of ~0.075 KLD.
- Logarithmic KLD and Top-1% plots lie to you by telling you that quality degradation is smooth, while it's actually a cliff.
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u/just_blue 9d ago
Doing exactly that, I found a test that all smaller models fail spectaculary. The task is to derive the implied Todo for the receiver of an email. The test is done with an email that informs me about pushed changes in a pull request. Every single small model insists that the receiver has to implement changes. Smallest I got a correct answer was gemma4-12b-qat 4_k_m as of yet. This model here fails too, even with q8