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ExLlamaV2

Arguments

Here are the arguments to convert.py:

  • -i / --in_dir directory: (required if not resuming) The source model to convert, in HF format (FP16). The directory should contain at least a config.json file, a tokenizer.model file and one or more .safetensors files containing weights. If there are multiple weights files, they will all be indexed and searched for the neccessary tensors, so sharded models are supported.

  • -o / --out_dir directory: (required) A working directory where the converter can store temporary files and deposit the final output unless -cf is also provided. If this directory is not empty when the conversion starts, the converter will attempt to resume whatever was being worked on in that directory. So if a job is interrupted, you can rerun the script with the same arguments to pick up from where it stopped. Note that parameters are read from the job.json file kept in this directory, so you won't be able to supply new parameters to a resumed job without editing that file. If you do, note that not all changes would leave the job in a valid state, so be careful with that.

  • -nr / --no_resume: If this flag is specified, the converter will not resume an interrupted job even if you point it to a non-empty working directory. If the working directory is not empty, all files within it will be deleted and the converter will start a new job.

  • -cf / --compile_full directory: By default the resulting .safetensors files are saved to the working directory. If you specify a directory with -cf, the quantized weights will be saved there instead. In addition, all files from the model (input) directory will be copied to this directory except for the original .safetensors files, resulting in a full model directory that can be used for inference by ExLlamaV2.

  • -om / --output_measurement file: After the first (measurement) pass is completed, a measurement.json file will be dropped in the working directory (-o). If you specify -om with a path, the measurement will be saved to that path after the measurement pass, and the script will exit immediately after.

  • -m / --measurement file: Skip the measurement pass and instead use the results from the provided file. This is particularly useful when quantizing the same model to multiple bitrates, since the measurement pass can take a long time to complete.

  • -c / --cal_dataset file: (optional) The calibration dataset in Parquet format. The quantizer concatenates all the data in this file into one long string and uses the first r * l tokens for calibration. If this is not specified, the default, built-in calibration dataset is used which contains a broad mix of different types of data. It's designed to prevent the quantized model from overfitting to any particular mode, language or style, and generally results in more robust, reliable outputs, especially at lower bitrates.

  • -l / --length int: Length, in tokens, of each calibration row. Default is 2048.

  • -r / --dataset_rows int: Number of rows in the calibration batch. Default is 100.

  • -ml / --measurement_length int: Length, in tokens, of each calibration row used for the measuring pass. Default is 2048.

  • -mr / --measurement_rows int: Number of rows in the calibration batch for the measuring pass. Default is 16.

  • -b / --bits float: Target average number of bits per weight.

  • -hb / --bits int: Number of bits for the lm_head (output) layer of the model. Default is 6, although that value actually results in a mixed-precision quantization of about 6.3 bits. Options are 2, 3, 4, 5, 6 and 8. (Only 6 and 8 appear to be useful.)

  • -ss / --shard_size float: Output shard size, in megabytes. Default is 8192. Set this to 0 to disable sharding. Note that writing a very large .safetensors file can require a lot of system RAM.

  • -ra / --rope_alpha float: RoPE (NTK) alpha to apply to base model for calibration.

  • -rs / --rope_scale float: RoPE scaling factor to apply to base model for calibration. This settings is not automatically read from the model's config, so it's strongly recommended that you check what setting the model was trained/finetuned with. E.g.: deepseek-coder uses a scaling factor of 4, so will be incorrectly calibrated if you convert it without -rs 4.

Notes

The converter works in two passes; first it measures how quantization impacts each module of the model, and then it actually quantizes the model, choosing quantization parameters for each layer that minimize the overall error while also achieving the desired overall (average) bitrate.

The first pass is slow, since it effectively quantizes the entire model about 12 times over (albeit with a less comprehensive sample of the calibration dataset), so make sure to save the measurement.json file so you can skip the measurement pass on subsequent quants of the same model.

Examples

Convert a model and create a directory containing the quantized version with all of its original files:

python convert.py \
    -i /mnt/models/llama2-7b-fp16/ \
    -o /mnt/temp/exl2/ \
    -cf /mnt/models/llama2-7b-exl2/3.0bpw/ \
    -b 3.0

Run just the measurement pass on a model, clearing the working directory first:

python convert.py \
    -i /mnt/models/llama2-7b-fp16/ \
    -o /mnt/temp/exl2/ \
    -nr \
    -om /mnt/models/llama2-7b-exl2/measurement.json

Use that measurement to quantize the model at two different bitrates:

python convert.py \
    -i /mnt/models/llama2-7b-fp16/ \
    -o /mnt/temp/exl2/ \
    -nr \
    -m /mnt/models/llama2-7b-exl2/measurement.json \
    -cf /mnt/models/llama2-7b-exl2/4.0bpw/ \
    -b 4.0
    
python convert.py \
    -i /mnt/models/llama2-7b-fp16/ \
    -o /mnt/temp/exl2/ \
    -nr \
    -m /mnt/models/llama2-7b-exl2/measurement.json \
    -cf /mnt/models/llama2-7b-exl2/4.5bpw/ \
    -b 4.5

If the working -o directory is not empty and you do not specify -nr, any existing job in that directory will be resumed. You can resume a job with no other arguments:

python convert.py -o /mnt/temp/exl2/

Notes

  • If the conversion script seems to stop on the "Solving..." step, give it a moment. It's attempting to find the combination of quantization parameters within the bits budget that minimizes the product of measured errors per individual layer, and the implementation is not very efficient.
  • During measurement and conversion of MoE models you may see a message like: !! Warning: w2.7 has less than 10% calibration for 77/115 rows. This happens when a particular expert isn't triggered enough during the reference forward passes to get a good amount of calibration data. It won't cause the conversion to fail, and it may not be a big deal at all, but GPTQ-style quantization of MoE models is very new so I'm not yet sure if it actually matters.
  • After conversion, the "calibration perplexity (quant)" is a perplexity calculation on a small sample of the calibration data as processed by the quantized model under construction. If it looks too high (30 or more), quantization likely didn't go well, and if it's unreasonably high (in the thousands, for instance) quantization failed catastrophically.

Hardware requirements

Roughly speaking, you'll need about 64 GB of RAM and 24 GB of VRAM to convert a 70B model, while 7B seems to require about 16 GB of RAM and about 8 GB of VRAM.

The deciding factor for the memory requirement is the width of the model rather than the depth, so 120B models that have the same hidden size as 70B models have the same hardware requirements. Mixtral 8x7B has much wider feed-forward layers so requires about 20 GB of VRAM.