Instructions to use zmarcusss/ggml-model-f32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use zmarcusss/ggml-model-f32 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="zmarcusss/ggml-model-f32", filename="MobileVLM-1.7B-F32.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use zmarcusss/ggml-model-f32 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf zmarcusss/ggml-model-f32:F32 # Run inference directly in the terminal: llama cli -hf zmarcusss/ggml-model-f32:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf zmarcusss/ggml-model-f32:F32 # Run inference directly in the terminal: llama cli -hf zmarcusss/ggml-model-f32:F32
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 zmarcusss/ggml-model-f32:F32 # Run inference directly in the terminal: ./llama-cli -hf zmarcusss/ggml-model-f32:F32
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 zmarcusss/ggml-model-f32:F32 # Run inference directly in the terminal: ./build/bin/llama-cli -hf zmarcusss/ggml-model-f32:F32
Use Docker
docker model run hf.co/zmarcusss/ggml-model-f32:F32
- LM Studio
- Jan
- Ollama
How to use zmarcusss/ggml-model-f32 with Ollama:
ollama run hf.co/zmarcusss/ggml-model-f32:F32
- Unsloth Studio
How to use zmarcusss/ggml-model-f32 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 zmarcusss/ggml-model-f32 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 zmarcusss/ggml-model-f32 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for zmarcusss/ggml-model-f32 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use zmarcusss/ggml-model-f32 with Docker Model Runner:
docker model run hf.co/zmarcusss/ggml-model-f32:F32
- Lemonade
How to use zmarcusss/ggml-model-f32 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull zmarcusss/ggml-model-f32:F32
Run and chat with the model
lemonade run user.ggml-model-f32-F32
List all available models
lemonade list
Model Summery
MobileVLM is a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models at the scale of 1.4B and 2.7B parameters, trained from scratch, a multimodal vision model that is pre-trained in the CLIP fashion, cross-modality interaction via an efficient projector. We evaluate MobileVLM on several typical VLM benchmarks. Our models demonstrate on par performance compared with a few much larger models. More importantly, we measure the inference speed on both a Qualcomm Snapdragon 888 CPU and an NVIDIA Jeston Orin GPU, and we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively.
The MobileVLM-1.7B was built on our MobileLLaMA-1.4B-Chat to facilitate the off-the-shelf deployment.
Model Sources
- Repository: https://github.com/Meituan-AutoML/MobileVLM
- Paper: https://arxiv.org/abs/2312.16886
How to Get Started with the Model
Inference examples can be found at Github.
Training Details
Please refer to our paper: MobileVLM: A Fast, Strong and Open Vision Language Assistant for Mobile Devices
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