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

Libraries

How to use jazeelmohd/mobileo_android with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jazeelmohd/mobileo_android",
	filename="text_encoder.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jazeelmohd/mobileo_android with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jazeelmohd/mobileo_android
# Run inference directly in the terminal:
llama-cli -hf jazeelmohd/mobileo_android

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jazeelmohd/mobileo_android
# Run inference directly in the terminal:
llama-cli -hf jazeelmohd/mobileo_android

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 jazeelmohd/mobileo_android
# Run inference directly in the terminal:
./llama-cli -hf jazeelmohd/mobileo_android

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 jazeelmohd/mobileo_android
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jazeelmohd/mobileo_android

Use Docker

docker model run hf.co/jazeelmohd/mobileo_android

LM Studio
Jan
Ollama
How to use jazeelmohd/mobileo_android with Ollama:
```
ollama run hf.co/jazeelmohd/mobileo_android
```

Unsloth Studio

How to use jazeelmohd/mobileo_android 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 jazeelmohd/mobileo_android 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 jazeelmohd/mobileo_android to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jazeelmohd/mobileo_android to start chatting

Docker Model Runner
How to use jazeelmohd/mobileo_android with Docker Model Runner:
```
docker model run hf.co/jazeelmohd/mobileo_android
```

Lemonade

How to use jazeelmohd/mobileo_android with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jazeelmohd/mobileo_android

Run and chat with the model

lemonade run user.mobileo_android-{{QUANT_TAG}}

List all available models

lemonade list

mobileo_android / vae_decoder.mnn.weight

jazeelmohd

v2: Update MNN models from Post-Train-Edit checkpoint

e2d7128 verified about 22 hours ago

download

history blame contribute delete

637 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: bd2137eac2b47bef5b1b6c542b1f1dcbb635939144cb89e191c9a7cff3d447fa
Size of remote file:: 637 MB
SHA256:: cbe768e78dcaef693365803fd384e8ba7006b4a37b161642c48cb3444bf39451

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.