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LLAMA-QUANTIZE(1) User Commands LLAMA-QUANTIZE(1)

NAME

llama-quantize - llama-quantize

DESCRIPTION

usage: obj-x86_64-linux-gnu/bin/llama-quantize [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]

[--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--prune-layers] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]

--allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit

--leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing

--pure: Disable k-quant mixtures and quantize all tensors to the same type

--imatrix file_name: use data in file_name as importance matrix for quant optimizations

--include-weights tensor_name: use importance matrix for this/these tensor(s)

--exclude-weights tensor_name: use importance matrix for this/these tensor(s)

--output-tensor-type ggml_type: use this ggml_type for the output.weight tensor

--token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor

--tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0

Advanced option to selectively quantize tensors. May be specified multiple times.

--prune-layers L0,L1,L2...comma-separated list of layer numbers to prune from the model

Advanced option to remove all tensors from the given layers

--keep-split: will generate quantized model in the same shards as input

--override-kv KEY=TYPE:VALUE

Advanced option to override model metadata by key in the quantized model. May be specified multiple times.

Note: --include-weights and --exclude-weights cannot be used together

Allowed quantization types:

2
or Q4_0 : 4.34G, +0.4685 ppl @ Llama-3-8B
3
or Q4_1 : 4.78G, +0.4511 ppl @ Llama-3-8B
8
or Q5_0 : 5.21G, +0.1316 ppl @ Llama-3-8B
9
or Q5_1 : 5.65G, +0.1062 ppl @ Llama-3-8B
19
or IQ2_XXS : 2.06 bpw quantization
20
or IQ2_XS : 2.31 bpw quantization
28
or IQ2_S : 2.5 bpw quantization
29
or IQ2_M : 2.7 bpw quantization
24
or IQ1_S : 1.56 bpw quantization
31
or IQ1_M : 1.75 bpw quantization
36
or TQ1_0 : 1.69 bpw ternarization
37
or TQ2_0 : 2.06 bpw ternarization
10
or Q2_K : 2.96G, +3.5199 ppl @ Llama-3-8B
21
or Q2_K_S : 2.96G, +3.1836 ppl @ Llama-3-8B
23
or IQ3_XXS : 3.06 bpw quantization
26
or IQ3_S : 3.44 bpw quantization
27
or IQ3_M : 3.66 bpw quantization mix
12
or Q3_K : alias for Q3_K_M
22
or IQ3_XS : 3.3 bpw quantization
11
or Q3_K_S : 3.41G, +1.6321 ppl @ Llama-3-8B
12
or Q3_K_M : 3.74G, +0.6569 ppl @ Llama-3-8B
13
or Q3_K_L : 4.03G, +0.5562 ppl @ Llama-3-8B
25
or IQ4_NL : 4.50 bpw non-linear quantization
30
or IQ4_XS : 4.25 bpw non-linear quantization
15
or Q4_K : alias for Q4_K_M
14
or Q4_K_S : 4.37G, +0.2689 ppl @ Llama-3-8B
15
or Q4_K_M : 4.58G, +0.1754 ppl @ Llama-3-8B
17
or Q5_K : alias for Q5_K_M
16
or Q5_K_S : 5.21G, +0.1049 ppl @ Llama-3-8B
17
or Q5_K_M : 5.33G, +0.0569 ppl @ Llama-3-8B
18
or Q6_K : 6.14G, +0.0217 ppl @ Llama-3-8B
7
or Q8_0 : 7.96G, +0.0026 ppl @ Llama-3-8B
1
or F16 : 14.00G, +0.0020 ppl @ Mistral-7B
32
or BF16 : 14.00G, -0.0050 ppl @ Mistral-7B
0
or F32 : 26.00G @ 7B
: only copy tensors, no quantizing
August 2025 debian