Instructions to use ggml-org-dev/stories15M 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 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:Q8_0 # Run inference directly in the terminal: llama cli -hf ggml-org-dev/stories15M:Q8_0
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:Q8_0 # Run inference directly in the terminal: llama cli -hf ggml-org-dev/stories15M:Q8_0
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:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf ggml-org-dev/stories15M:Q8_0
Use Docker
docker model run hf.co/ggml-org-dev/stories15M:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use ggml-org-dev/stories15M with Ollama:
ollama run hf.co/ggml-org-dev/stories15M:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use ggml-org-dev/stories15M with Docker Model Runner:
docker model run hf.co/ggml-org-dev/stories15M:Q8_0
- Lemonade
How to use ggml-org-dev/stories15M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ggml-org-dev/stories15M:Q8_0
Run and chat with the model
lemonade run user.stories15M-Q8_0
List all available models
lemonade list
- Atomic Chat
stories15M
Tiny model used by the llama.cpp test suite and CI. It is not meant for real-world use.
Originally hosted at ggml-org/models/tinyllamas/.
stories15M*.gguf: karpathy/tinyllamas stories15M (F32, Q4_0, Q8_0)stories15M-be*.gguf: big-endian variants (s390x CI)split/: the same model split into 3 shards (F32 and Q8_0), for split-loading tests
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