Instructions to use TobDeBer/myContainers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use TobDeBer/myContainers with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TobDeBer/myContainers", filename="arco_BE8.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 TobDeBer/myContainers with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TobDeBer/myContainers # Run inference directly in the terminal: llama-cli -hf TobDeBer/myContainers
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TobDeBer/myContainers # Run inference directly in the terminal: llama-cli -hf TobDeBer/myContainers
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 TobDeBer/myContainers # Run inference directly in the terminal: ./llama-cli -hf TobDeBer/myContainers
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 TobDeBer/myContainers # Run inference directly in the terminal: ./build/bin/llama-cli -hf TobDeBer/myContainers
Use Docker
docker model run hf.co/TobDeBer/myContainers
- LM Studio
- Jan
- Ollama
How to use TobDeBer/myContainers with Ollama:
ollama run hf.co/TobDeBer/myContainers
- Unsloth Studio
How to use TobDeBer/myContainers 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 TobDeBer/myContainers 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 TobDeBer/myContainers to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TobDeBer/myContainers to start chatting
- Docker Model Runner
How to use TobDeBer/myContainers with Docker Model Runner:
docker model run hf.co/TobDeBer/myContainers
- Lemonade
How to use TobDeBer/myContainers with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TobDeBer/myContainers
Run and chat with the model
lemonade run user.myContainers-{{QUANT_TAG}}List all available models
lemonade list
update README
Browse files- README.md +41 -4
- serve-from-url.sh +10 -0
README.md
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#
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## Variants
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### CPUdiffusion
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- inference diffusion models on CPU
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- include CUDAonCPU stack
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- inference gguf models on CPU
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- include GUI libraries
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# Model Repository for Big Endian models
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## Variants
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### Arco 500M Q4 BE
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# Container Repository for CPU adaptations of Inference code
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## Variants for Inference
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### Slim container
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- run std binaries
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### CPUdiffusion
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- inference diffusion models on CPU
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- include CUDAonCPU stack
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### Diffusion container
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- run diffusion app.py variants
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- support CPU and CUDA
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- include Flux
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### Slim CUDA container
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- run CUDA binaries
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## Variants for Build
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### Llama.cpp build container
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- build llama-cli-static
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- build llama-server-static
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### sd build container
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- build sd
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- optional: build sd-server
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### CUDA build container
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- build cuda binaries
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- support sd_cuda
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serve-from-url.sh
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1) wget the model
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2) save hostip: ip route | grep 'default' | awk '{print $9}' >hostip
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3a) calls llama-server in container
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3b) calls sed + llama-server in container
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opodman run -d --net=host -v ~/funstreams:/models localhost/bookworm:server ./models/llama-server-static -m /models/qwen2-500.gguf
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