How to use the gguf-split / Model sharding demo

[phymbert]

Context

Distributing and storing GGUFs is difficult for 70b+ models, especially on f16. Lot of issue can happen during file transfers, examples:

• temporary disk full
• network interruption

Typically, GGUFs need to be transferred from Hugging Face to an internal storage like s3, minio, git lfs, nexus or artifactory, then downloaded by the inference server and stored locally (or on a k8s PvC for example).

Storage solutions and filesystems poorly support large GGUF, typically HF does not support files larger than 50GB.
Such limits also exist on Artifactory.

Solution

We recently introduced gguf-split CLI and support the load of sharded GGUFs model in llama.cpp:

• gguf-split: split and merge gguf per batch of tensors #6135
• llama_model_loader: support multiple split/shard GGUFs #6187
• common: llama_load_model_from_url split support  #6192
• common : add HF arg helpers #6234
• split: allow --split-max-size option #6343

Download a model

from huggingface_hub import snapshot_download
snapshot_download(repo_id="keyfan/grok-1-hf")

Convert to GGUF F16

python -u convert-hf-to-gguf.py \
  ~/.cache/huggingface/hub/models--keyfan--grok-1-hf/snapshots/64e7373053c1bc7994ce427827b78ec11c181b3e/ \
  --outfile grok-1-f16.gguf \
  --outtype f16

NOTE: Follow llama.cpp build instructions to generate all tools/cli: make.

Quantize (optional)

quantize grok-1-f16.gguf grok-1-q4_0.gguf q4_0

Build model shards

It is possible to use different sharding strategy:

• Max tensors per file: --split-max-tensors 256
• Max file size: --split-max-size 48G

gguf-split --split --split-max-tensors 256 grok-1-q4_0.gguf grok-1-q4_0

It will produce 9 files with maximum 256 tensors in each.

You can then upload the sharded model to your HF Repo:

huggingface-cli upload [repo_id] [local_path] [path_in_repo]

Files produced by gguf-split are valid GGUFs, so you can visualize them in HF.

Load sharded model

llama_load_model_from_file will detect the number of files and will load additional tensors from the rest of files.

main --model grok-1-q4_0-00001-of-00009.gguf -ngl 64

You may notice:

llama_model_loader: additional 8 GGUFs metadata loaded.

Load sharded model from a remote URL

main --hf-repo ggml-org/models \
  --hf-file grok-1/grok-1-q4_0-00001-of-00009.gguf \
  --model   models/grok-1-q4_0-00001-of-00009.gguf \
  -ngl 64

[maziyarpanahi]

Maybe I am wrong, but I couldn't make --split-max-size 48G work.

[christianazinn]

@ngxson Here it is.

The really strange part is that it looks like changing the parameter very slightly results in drastically different outputs:

[christianazinn]

Ah, and I'm not so lucky as @maziyarpanahi with splitting Mixtral 8x22b. Only possible difference is that I quantized each model with an imatrix, but I'm not getting nicely sized splits sans flags.

[4cecoder]

please for the love of gpt how do you merge gguf files?

there isn't much docs on the net?

I tried compiling llama.cpp on windows and i've got that but now what?

Just want to merge 1-5 sharded gguf of command r+!

[christianazinn]

@4cecoder gguf-split --merge [path-to-first-shard] [path-to-outfile] should do the trick.

[dranger003]

On Windows you can compile llama.cpp by opening a VS native tools command prompt (i.e. x64 Native Tools Command Prompt for VS 2022) and running the commands below. Make sure your VS install has C++ dev libraries installed.

git clone --recursive https://github.com/ggerganov/llama.cpp
cmake -S . -B build && cmake --build build --config Release
build\bin\Release\gguf-split.exe -h

[taozhiyuai]

@dranger003 @phymbert may I ask how to compile gguf-split on MAC?

(llamacpp) taozhiyu@603e5f4a42f1 llama.cpp-master % gguf-split
zsh: command not found: gguf-split

[phymbert]

Hum, I am Mac less. @ggerganov, could you please help ?

Is not simply calling make not working ?

[ggerganov]

make
./gguf-split

[phymbert]

Summary updated ^)

[taozhiyuai]

Works on Mac.thanks.

'make
./gguf-split'

[bennmann]

Please also include more clear and specific instructions for --merge?

[phymbert]

Merging is not necessary anymore in most use cases.

[4cecoder]

Merging in LLM studio is no longer necessary so just use LLM studio on windows @bennmann