Ninja-v1-RP-WIP

This model declares a 4,096-token sliding window, but we could not establish which layers use it. Its architecture publishes the layout as a per-layer array inside the model file rather than as a period in config.json, and we have not yet ingested that array.

A flat context × layers × heads figure would be substantially too high, so we are not showing one. This is tracked as a known gap rather than filled with a guess.

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 3.79 GiB. The real file is 4.07 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

How much VRAM does Ninja-v1-RP-WIP need?

Q4_K_M is exactly 4,368,439,520 bytes (4.07 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.

Which quantization of Ninja-v1-RP-WIP should I use?

Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.