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 commited on Sep 11, 2024

Commit

aca3f5d

verified ·

1 Parent(s): a653b79

Update README.md

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README.md +11 -5

README.md CHANGED Viewed

@@ -1,8 +1,14 @@
-# Model Repository for Big Endian models
-## Variants
-### Arco 500M Q4 BE
 # Container Repository for CPU adaptations of Inference code

+---
+base_model: appvoid/arco
+license: apache-2.0
+tags:
+- llama-cpp
+- gguf-my-repo
+---
+# TobDeBer/arco-Q4_K_M-GGUF
+This model was converted to Big Endian Q4_K_M GGUF format from [`appvoid/arco`](https://huggingface.co/appvoid/arco) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
+Refer to the [original model card](https://huggingface.co/appvoid/arco) for more details on the model.
 # Container Repository for CPU adaptations of Inference code