Instructions to use city96/t5-v1_1-xxl-encoder-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use city96/t5-v1_1-xxl-encoder-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="city96/t5-v1_1-xxl-encoder-gguf",
	filename="t5-v1_1-xxl-encoder-Q3_K_L.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use city96/t5-v1_1-xxl-encoder-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M

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/t5-v1_1-xxl-encoder-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M

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/t5-v1_1-xxl-encoder-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M

Use Docker

docker model run hf.co/city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use city96/t5-v1_1-xxl-encoder-gguf with Ollama:
```
ollama run hf.co/city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M
```

Unsloth Studio new

How to use city96/t5-v1_1-xxl-encoder-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for city96/t5-v1_1-xxl-encoder-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for city96/t5-v1_1-xxl-encoder-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for city96/t5-v1_1-xxl-encoder-gguf to start chatting

Docker Model Runner
How to use city96/t5-v1_1-xxl-encoder-gguf with Docker Model Runner:
```
docker model run hf.co/city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M
```

Lemonade

How to use city96/t5-v1_1-xxl-encoder-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull city96/t5-v1_1-xxl-encoder-gguf:Q4_K_M

Run and chat with the model

lemonade run user.t5-v1_1-xxl-encoder-gguf-Q4_K_M

List all available models

lemonade list

The load dual clip GGUF can use this encoder, what what about clip_i?

by witchercher - opened Oct 17, 2024

Discussion

witchercher

Oct 17, 2024

Hi.
We usually use 2 clips with the dual clip loader:

clip_I
t5 xxl
Ok with the GGUF version we can use the t5 xxl, but what about clip I ? Is there a gguf version if it? Thanks

city96

Owner Oct 18, 2024

•

edited Oct 18, 2024

You're supposed to just use the normal one. Clip is small so there's no real point in quantizing it.

city96 changed discussion status to closed Oct 18, 2024

witchercher

Oct 18, 2024

Thanks!

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