Instructions to use TobDeBer/myContainers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 new

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

TobDeBer/arco-Q4_K_M-GGUF

This model was converted to Big Endian Q4_K_M GGUF format from appvoid/arco using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Container Repository for CPU adaptations of Inference code

Variants for Inference

Slim container

run std binaries

CPUdiffusion

inference diffusion models on CPU
include CUDAonCPU stack

Diffusion container

run diffusion app.py variants
support CPU and CUDA
include Flux

Slim CUDA container

run CUDA binaries

Variants for Build

Llama.cpp build container

build llama-cli-static
build llama-server-static

sd build container

build sd
optional: build sd-server

CUDA build container

build cuda binaries
support sd_cuda

Downloads last month: 8

GGUF

Model size

0.5B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for TobDeBer/myContainers

Base model

appvoid/arco

Quantized

(7)

this model