How to use from
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 bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
# Run inference directly in the terminal:
llama cli -hf bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
# Run inference directly in the terminal:
llama cli -hf bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
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 bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
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 bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
Use Docker
docker model run hf.co/bubblspace/Timecapsule2.7B-g3n-mix-match-gguf:F16
Quick Links

TimeCapsule Gemma 3n 2.7B Slice (FP16 GGUF)

This model is a 2.7 B parameter sub‑model of Gemma 3n, created using the MatFormer (Matryoshka Transformer) architecture and the Mix‑n‑Match slicing approach. It was sliced from the E4B checkpoint using the official E2.69B (layer‑level) configuration.


🧠 Intended Use

  • Primary use: High‑precision inference with Ollama via FP16 GGUF.
  • Best suited for: TimeCapsule‑SLM deep‑research workflows where latency, accuracy, and compute tradeoffs matter.

⚠️ Limitations & Considerations

  • Derived from a larger model — may not match the full E4B model in some evaluations.
  • Operates in FP16 precision — requires hardware (like A100/GPU or Ollama host) with FP16 support.
  • No additional quantization applied, preserving accuracy at some memory cost.

🛠 Creation Details

  • Parent model: google/gemma-3n-E4B-it
  • Slice configuration: Config for E2.69B (layer-level) from the official slicing-configs dataset
  • Converted from .safetensors to FP16 GGUF using llama.cpp’s convert_hf_to_gguf.py
  • Uploaded to this repository as: tc_mixmatch_f16.gguf

🧪 Usage Example

ollama run hf.co/bubblspace/Timecapsule2.7B-g3n-mix-match-gguf
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