Instructions to use ggml-org-dev/stories15M_MOE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ggml-org-dev/stories15M_MOE 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 ggml-org-dev/stories15M_MOE:F16 # Run inference directly in the terminal: llama cli -hf ggml-org-dev/stories15M_MOE:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ggml-org-dev/stories15M_MOE:F16 # Run inference directly in the terminal: llama cli -hf ggml-org-dev/stories15M_MOE:F16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf ggml-org-dev/stories15M_MOE:F16 # Run inference directly in the terminal: ./build/bin/llama cli -hf ggml-org-dev/stories15M_MOE:F16
Use Docker
docker model run hf.co/ggml-org-dev/stories15M_MOE:F16
- Llama new
- LM Studio
- Jan
- Ollama
How to use ggml-org-dev/stories15M_MOE with Ollama:
ollama run hf.co/ggml-org-dev/stories15M_MOE:F16
- Unsloth Desktop
- Docker Model Runner
How to use ggml-org-dev/stories15M_MOE with Docker Model Runner:
docker model run hf.co/ggml-org-dev/stories15M_MOE:F16
- Lemonade
How to use ggml-org-dev/stories15M_MOE with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ggml-org-dev/stories15M_MOE:F16
Run and chat with the model
lemonade run user.stories15M_MOE-F16
List all available models
lemonade list
- Atomic Chat
stories15M_MOE
This model is ModelCloud/tinyllama-15M-stories repeated 4 times to make 4 experts.
The model is used for testing, not intended to be used in production (unless your product is some kind of bedtime story teller)
Weight of router is initialized randomly
shakespeare LoRA adapter
A LoRA adapter trained on first 100 paragraphs of shakespeare can be found inside moe_shakespeare15M
With input: Look in thy glass
- Original model generates:
Look in thy glass was a little girl. She was only three years old and she was three years old. She was - LoRA adapter generates:
Look in thy glass in love of the eye: That's when when the eye see thy on the sun'
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