Converted text models
Collection
Text models in various precisions/formats, many specific to image models. • 6 items • Updated • 7
How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf city96/umt5-xxl-encoder-gguf:# Run inference directly in the terminal:
llama-cli -hf city96/umt5-xxl-encoder-gguf:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf city96/umt5-xxl-encoder-gguf:# Run inference directly in the terminal:
./llama-cli -hf city96/umt5-xxl-encoder-gguf:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf city96/umt5-xxl-encoder-gguf:# Run inference directly in the terminal:
./build/bin/llama-cli -hf city96/umt5-xxl-encoder-gguf:Use Docker
docker model run hf.co/city96/umt5-xxl-encoder-gguf:Quick Links
This is a GGUF conversion of Google's UMT5 xxl model, specifically the encoder part.
The weights can be used with ./llama-embedding or with the ComfyUI-GGUF custom node together with image/video generation models.
This is a non imatrix quant as llama.cpp doesn't support imatrix creation for T5 models at the time of writing. It's therefore recommended to use Q5_K_M or larger for the best results, although smaller models may also still provide decent results in resource constrained scenarios.
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Model tree for city96/umt5-xxl-encoder-gguf
Base model
google/umt5-xxl
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf city96/umt5-xxl-encoder-gguf:# Run inference directly in the terminal: llama-cli -hf city96/umt5-xxl-encoder-gguf: