---

license: apache-2.0

base_model:

• TheDrummer/Skyfall-31B-v4.2

library_name: ik_llama.cpp

pipeline_tag: text-generation

tags:

• gguf
• quantized
• ik_llama.cpp
• 3-bit
• IQ3_KT
• trellis
• roleplay

quantized_by: Koshkasa

base_model_relation: quantized

---

Model Card for Model ID

Skyfall is a heavy hitter for mainline consumer GPUs. I used to run it at IQ4_XS on 16 GB with partial offload, which was way too slow.

IQ3_M was tolerable, but restrictive of KVO. IQ3_XXS was permissive of KVO, but too dumbed down.

This is basically a ik-llama.cpp answer to "how do I fit this model into 16 gb with KVO without exacerbating brain damage"

The goals: at least not dumber than IQ3_M, at least 32k context in VRAM, at least at Q5_0 K and Q4_1 V cache, with system overhead accounted for.

The result: a mainly IQ3_KT quantization of TheDrummer/Skyfall-31B-v4.2

Quant Details

iq3_s: token_embd (1)

iq3_kt: attn_k, attn_q, ffn_up, ffn_gate, ffn_down (270)

iq4_k: attn_output, attn_v, output (109)

imatrix by bartowski

quantized with ik_llama.cpp build: 9d07d868

incompatible with mainline llama.cpp

only tested with ik_llama.cpp (though I expect croco.cpp to work as well)

Comparison:

IQ3_M by bartowski:

winogrande: 74.9013 ± 1.2186 (949/1267)

hellaswag (n=400, s=42): 82.25%

with K q5_0, V q4_1, b=512, amb=64, ngl=999, 5 runs:

pp512: 899.37 ± 36.62

pp32768: 658.53 ± 2.11

tg128: 25.09 0± 0.39

tg512: 22.00 ± 0.02

size: 14314095296 bytes

this IQ3_KT:

winogrande: 75.5328 ± 1.2082 (957/1267) (+0.84%)

hellaswag (n=400, s=42): 82.75% (+0.61%)

with K q5_0, V q4_1, b=512, amb=64, ngl=999, 5 runs:

pp512: 975.41 ± 32.69 (+8.45%)

pp32768: 741.10 ± 7.88 (+12.54%)

tg128: 23.16 ± 0.08 (-7.69%)

tg512: 22.53 ± 0.15 (+2.5%)

size: 12658394016 bytes (11.57% smaller) (-1578.99 MB)

In conclusion

-Is it smarter?

Not in a statistically significant way, but it's not dumber.

-Is it slower?

No. It's actually faster in attn, which is not surprising, given less quality on attn layers in comparison to IQ3_M by bartowski.

-Is it smaller?

Hell yeah. -1579 MB puts it in IQ3_XXS territory by size

-Is it optimal?

*No. I have a suspicion lowered attn quants will hurt it over long ctx (to be tested). Could try squeezing ffn_up in favor of k- and q- attn.

The primary candidate directly touching residuals is ffn_down which I can't quant up meaningfully (IQ3_KT is IQ_3M-ish in quality, going above 4bit bloats the gguf hard, failing the ctx goal.

One quantization takes me about 2.5 hrs on my CPU, so I can't freely test every hypothesis quickly.*

-Is it worth it?

No idea. I made it for personal use to squeeze some extra context in without losing brainpower. I already had to resort to 3 bits. WYSIWYG.

-Possible 12G VRAM full fit with KVO?

Unlikely. A mixed IQ2_KT ffn / IQ3_KT attn quant was around 8.8 GB, but it scored significantly worse on hellaswag and winogrande. I didn't go for testing it in RP, since it was overkill compression for my purpose.

-Why Q5_0/Q4_1?

Reasons

Cheers

MistralAI - the beloved base model(s).

ikawrakow and contributors of ik_llama.cpp - I probably misused your wonderful creation.

TheDrummer - you cooked a legendary one.

bartowski - for the imatrix + the myriad of quants we all benefit from.