llama.cpp/examples/quantize

quantize

You can also use the GGUF-my-repo space on Hugging Face to build your own quants without any setup.

Note: It is synced from llama.cpp main every 6 hours.

Example usage:

# obtain the official LLaMA model weights and place them in ./models
ls ./models
llama-2-7b tokenizer_checklist.chk tokenizer.model
# [Optional] for models using BPE tokenizers
ls ./models
<folder containing weights and tokenizer json> vocab.json
# [Optional] for PyTorch .bin models like Mistral-7B
ls ./models
<folder containing weights and tokenizer json>

# install Python dependencies
python3 -m pip install -r requirements.txt

# convert the model to ggml FP16 format
python3 convert_hf_to_gguf.py models/mymodel/

# quantize the model to 4-bits (using Q4_K_M method)
./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M

# update the gguf filetype to current version if older version is now unsupported
./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY

Run the quantized model:

# start inference on a gguf model
./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -cnv -p "You are a helpful assistant"

When running the larger models, make sure you have enough disk space to store all the intermediate files.

Memory/Disk Requirements

As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.

Model	Original size	Quantized size (Q4_0)
7B	13 GB	3.9 GB
13B	24 GB	7.8 GB
30B	60 GB	19.5 GB
65B	120 GB	38.5 GB

Quantization

Several quantization methods are supported. They differ in the resulting model disk size and inference speed.

The quantization formats Q4_0_4_4, Q4_0_4_8 and Q4_0_8_8 are block interleaved variants of the Q4_0 format, providing a data layout that is better suited for specific implementations of optimized mulmat kernels. Since these formats differ only in data layout, they have the same quantized size as the Q4_0 format.

(outdated)

Model	Measure	F16	Q4_0	Q4_1	Q5_0	Q5_1	Q8_0
7B	perplexity	5.9066	6.1565	6.0912	5.9862	5.9481	5.9070
7B	file size	13.0G	3.5G	3.9G	4.3G	4.7G	6.7G
7B	ms/tok @ 4th	127	55	54	76	83	72
7B	ms/tok @ 8th	122	43	45	52	56	67
7B	bits/weight	16.0	4.5	5.0	5.5	6.0	8.5
13B	perplexity	5.2543	5.3860	5.3608	5.2856	5.2706	5.2548
13B	file size	25.0G	6.8G	7.6G	8.3G	9.1G	13G
13B	ms/tok @ 4th	-	103	105	148	160	131
13B	ms/tok @ 8th	-	73	82	98	105	128
13B	bits/weight	16.0	4.5	5.0	5.5	6.0	8.5

• k-quants
• recent k-quants improvements and new i-quants
  • #2707
  • #2807
  • #4773 - 2-bit i-quants (inference)
  • #4856 - 2-bit i-quants (inference)
  • #4861 - importance matrix
  • #4872 - MoE models
  • #4897 - 2-bit quantization
  • #4930 - imatrix for all k-quants
  • #4951 - imatrix on the GPU
  • #4969 - imatrix for legacy quants
  • #4996 - k-qunats tuning
  • #5060 - Q3_K_XS
  • #5196 - 3-bit i-quants
  • quantization tuning, another one, and another one

Llama 2 7B

Quantization	Bits per Weight (BPW)
Q2_K	3.35
Q3_K_S	3.50
Q3_K_M	3.91
Q3_K_L	4.27
Q4_K_S	4.58
Q4_K_M	4.84
Q5_K_S	5.52
Q5_K_M	5.68
Q6_K	6.56

Llama 2 13B

Quantization	Bits per Weight (BPW)
Q2_K	3.34
Q3_K_S	3.48
Q3_K_M	3.89
Q3_K_L	4.26
Q4_K_S	4.56
Q4_K_M	4.83
Q5_K_S	5.51
Q5_K_M	5.67
Q6_K	6.56

Llama 2 70B

Quantization	Bits per Weight (BPW)
Q2_K	3.40
Q3_K_S	3.47
Q3_K_M	3.85
Q3_K_L	4.19
Q4_K_S	4.53
Q4_K_M	4.80
Q5_K_S	5.50
Q5_K_M	5.65
Q6_K	6.56