Instructions to use R3n3r0/dapack-catalog 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 R3n3r0/dapack-catalog 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 R3n3r0/dapack-catalog # Run inference directly in the terminal: llama cli -hf R3n3r0/dapack-catalog
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf R3n3r0/dapack-catalog # Run inference directly in the terminal: llama cli -hf R3n3r0/dapack-catalog
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 R3n3r0/dapack-catalog # Run inference directly in the terminal: ./llama-cli -hf R3n3r0/dapack-catalog
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 R3n3r0/dapack-catalog # Run inference directly in the terminal: ./build/bin/llama-cli -hf R3n3r0/dapack-catalog
Use Docker
docker model run hf.co/R3n3r0/dapack-catalog
- LM Studio
- Jan
- Ollama
How to use R3n3r0/dapack-catalog with Ollama:
ollama run hf.co/R3n3r0/dapack-catalog
- Unsloth Desktop
- Docker Model Runner
How to use R3n3r0/dapack-catalog with Docker Model Runner:
docker model run hf.co/R3n3r0/dapack-catalog
- Lemonade
How to use R3n3r0/dapack-catalog with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull R3n3r0/dapack-catalog
Run and chat with the model
lemonade run user.dapack-catalog-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
dapack β catalogue manifest and router
The glue that turns the three dapack packs into one routed catalogue:
| file | what it is |
|---|---|
manifest.json |
domains, measured capability manifests, TF-IDF detector, embedding-router centroids, file hashes |
embed/nomic-embed.gguf |
the embedding model for the ensemble router (nomic-embed-text-v1.5 Q8, 140 MB) |
Setup
Download the three packs and this repo's files into one directory:
mycatalog.dapack/
manifest.json <- from this repo
embed/nomic-embed.gguf <- from this repo
gguf/math.gguf <- R3n3r0/dapack-math (graded_math.gguf)
gguf/language.gguf <- R3n3r0/dapack-language (graded_language.gguf)
gguf/code.gguf <- R3n3r0/dapack-code (graded_code.gguf)
Then run it with the dapack runtime (binaries under Releases, nothing to compile):
./dapack serve mycatalog.dapack --addr :8080
The router is yours to choose
--router tfidf bag-of-words, zero extra memory, ~0.1 ms
--router embed dense centroids, ~145 MB VRAM, ~5 ms per request
--router ensemble both mixed 30/70 β 44% β 89% routing accuracy on a
frozen holdout; the two err on different prompts
--router auto (default) ensemble when this repo's files are present,
tfidf otherwise
Whichever router runs, the capability gate is identical: a request needing a capability a pack has measurably lost is routed to a pack that has it, or refused with a reason.
Runtime, measurements and docs: https://github.com/R3n3r0/dapack
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