Instructions to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp",
	filename="Wan2.2-TI2V-5B-Q2_K.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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
llama-cli -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
llama-cli -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

Use Docker

docker model run hf.co/isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

LM Studio
Jan
Ollama
How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Ollama:
```
ollama run hf.co/isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
```

Unsloth Studio new

How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp 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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp 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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp to start chatting

Docker Model Runner
How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Docker Model Runner:
```
docker model run hf.co/isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
```

Lemonade

How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

Run and chat with the model

lemonade run user.wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp-Q2_K

List all available models

lemonade list

wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp / stable-diffusion-cpp-nvidia /CMakeFiles /CMakeConfigureLog.yaml

isfs

Upload folder using huggingface_hub

a1663c1 verified 2 months ago

raw

history blame contribute delete

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	#$ _CUDART_=cudart
	#$ _HERE_=/usr/local/cuda/bin
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	#$ CUDAFE_FLAGS=
	#$ PTXAS_FLAGS=
	nvcc fatal : Don't know what to do with '__cmake_determine_cuda'


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	- "/usr/local/lib/python3.12/dist-packages/cmake/data/share/cmake-3.31/Modules/CMakeDetermineCUDACompiler.cmake:256 (cmake_nvcc_parse_implicit_info)"
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	- "/usr/local/lib/python3.12/dist-packages/cmake/data/share/cmake-3.31/Modules/CMakeDetermineCompilerABI.cmake:255 (cmake_determine_linker_id)"
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	- "ggml/src/ggml-cuda/CMakeLists.txt:58 (enable_language)"
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	Running the CUDA compiler's linker: "/usr/bin/ld" "-v"
	GNU ld (GNU Binutils for Ubuntu) 2.38
	...