The hybrid quant employs different quantization levels on a per layer basis to enable both high performance and small file size at the same time. The quants employed are all K to avoid slow CPU or older GPU processing of IQ quants. For this file the Q6_K_H layer quants are as follows:

Q5_K_L : attn_v = q8_0 attn_o = q6_k ffn_d = q6_k
Q6_K_S : Q6_K
Q6_K_M : attn_v = q8_0 ffn_d = q8_0
Q6_K_L : attn_v = q8_0 attn_o = q8_0 ffn_d = q8_0

   LAYER_TYPES='[
   [0 ,"Q6_K_S"],[1 ,"Q6_K_S"],[2 ,"Q5_K_L"],[3 ,"Q5_K_L"],[4 ,"Q5_K_M"],[5 ,"Q5_K_M"],[6 ,"Q5_K_M"],[7 ,"Q5_K_M"],
   [8 ,"Q5_K_M"],[9 ,"Q5_K_M"],[10,"Q5_K_M"],[11,"Q5_K_M"],[12,"Q5_K_L"],[13,"Q5_K_L"],[14,"Q5_K_L"],[15,"Q5_K_L"],
   [16,"Q6_K_S"],[17,"Q6_K_S"],[18,"Q6_K_S"],[19,"Q6_K_S"],[20,"Q6_K_M"],[21,"Q6_K_M"],[22,"Q6_K_M"],[23,"Q6_K_M"],
   [24,"Q6_K_L"],[25,"Q6_K_L"],[26,"Q6_K_L"],[27,"Q6_K_L"],[28,"Q8_0"  ],[29,"Q8_0"  ],[30,"Q8_0"  ],[31,"Q8_0"  ]
   ]'
   FLAGS="--token-embedding-type Q6_K --output-tensor-type Q6_K --layer-types-high"

This quant is sized at ~Q6_K efficiency.

A smaller Q4_K_H quant is also available:

Q4_K_L : Q4_K_M + attn_o = q6_k
Q5_K_L : attn_v = q8_0 attn_o = q6_k ffn_d = q6_k
Q6_K_S : Q6_K

   LAYER_TYPES='[
   [0 ,"Q5_K_L"],[1 ,"Q5_K_M"],[2 ,"Q5_K_S"],[3 ,"Q4_K_L"],[4 ,"Q4_K_M"],[5 ,"Q4_K_S"],[6 ,"Q4_K_S"],[7 ,"Q4_K_S"],
   [8 ,"Q4_K_S"],[9 ,"Q4_K_S"],[10,"Q4_K_S"],[11,"Q4_K_S"],[12,"Q4_K_S"],[13,"Q4_K_S"],[14,"Q4_K_S"],[15,"Q4_K_S"],
   [16,"Q4_K_M"],[17,"Q4_K_S"],[18,"Q4_K_M"],[19,"Q4_K_S"],[20,"Q4_K_M"],[21,"Q4_K_S"],[22,"Q4_K_M"],[23,"Q4_K_S"],
   [24,"Q4_K_M"],[25,"Q4_K_M"],[26,"Q4_K_M"],[27,"Q4_K_L"],[28,"Q5_K_S"],[29,"Q5_K_M"],[30,"Q5_K_L"],[31,"Q6_K_S"]
   ]'
   FLAGS="--token-embedding-type Q4_K --output-tensor-type Q6_K --layer-types-high"

This quant is sized at ~Q4_K_M efficiency.

Both layer quants were updated on 12/19/2025 optimized for performance across a small set of curated test prompts.

Comparison:

Quant	size	PPL	Comment
Q4_K_M	4.9e9	7.3	-
Q4_K_H	5.1e9	7.3	Hybrid quant with Q4_K embedding Q6_K output
Q6_K	6.6e9	7.2	Q6_K with default embedding and output
Q6_K_H	6.6e9	7.2	Hybrid quant with Q6_K embedding Q6_K output

Usage:

This model may be used together with fixie-ai ultravox-v0_5-llama-3_1-8b to enable it to process audio (.mp3 and .wav files) and text inputs and generate text outputs. The mmproj file is made available here: https://huggingface.co/steampunque/ultravox-v0_5-llama-3_1-8b-MP-GGUF More information about running multimedia may be found in the docs in the mtmd readme in the tools directory of the llama.cpp source tree https://github.com/ggml-org/llama.cpp/blob/master/tools/mtmd/README.md.

The model can be speculated using Llama 3.2 1B Instruct. Approximate performance on a 4070 with context and weights in VRAM using a custom downstream greedy speculator with fixed spec block length ND :

Prompt	ND	Gen TPS	Comment
goldcoin	0	68	non code
goldcoin	4	134	non code
humaneval	0	68	code
humaneval	8	162	code

goldcoin:

I have 10 apples. I find 3 gold coins in the bottom of a river. The river runs near a big city that has something to do with what I can spend the coins on. I then lose 4 apples but gain a gold coin. Three birds run into my path and drop 6 apples each. I play an online game and win 6 gold coins but I have to share them equally with my 2 teammates. I buy apples for all the coins I have. The price of an apple is 0.5 coins. How many apples do I have? And where is the river? Use step-by-step reasoning to solve this problem.

humaneval:

generate python code for the described function header:

from typing import List

def has_close_elements(numbers: List[float], threshold: float) -> bool:
    """ Check if in given list of numbers, are any two numbers closer to each other than
    given threshold.
    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)
    False
    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
    True
    """

Benchmarks:

A full set of benchmarks for the model will eventually be given here: https://huggingface.co/spaces/steampunque/benchlm

Download the file from below:

Link	Type	Size/e9 B	Notes
Llama-3.1-8B-Instruct.Q4_K_H.gguf	Q4_K_H	5.1e9 B	~ Q4_K_M size
Llama-3.1-8B-Instruct.Q6_K_H.gguf	Q6_K_H	6.6e9 B	~ Q6_K size
ultravox-v0_5-llama-3_1-8b.mmproj.gguf	F16	1.38e9 B	multimedia projector

A discussion thread about the hybrid layer quant approach can be found here on the llama.cpp git repository:

https://github.com/ggml-org/llama.cpp/discussions/13040

steampunque/Llama-3.1-8B-Instruct-Hybrid-GGUF

Mixed Precision GGUF layer quantization of Llama 3.1 8B Instruct by meta-llama

Download the file from below:

steampunque/
Llama-3.1-8B-Instruct-Hybrid-GGUF