llama.cpp/examples/quantize
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quantize : improve type name parsing (#9570)
quantize : do not ignore invalid types in arg parsing

quantize : ignore case of type and ftype arguments
2024-09-20 20:55:36 +02:00
..
CMakeLists.txt cmake : fixed the order of linking libraries for llama-quantize (#9450) 2024-09-12 14:27:14 +03:00
quantize.cpp quantize : improve type name parsing (#9570) 2024-09-20 20:55:36 +02:00
README.md Arm AArch64: Documentation updates (#9321) 2024-09-09 10:02:45 +03:00
tests.sh build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809) 2024-06-13 00:41:52 +01:00

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

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