Any-to-Any
MLX
Safetensors
gemma4
mlx-vlm
rlcd
multimodal
classification
parallel-inference
image-text-to-text
audio
video
4-bit precision
Instructions to use larkooo/gemma-e2b-rlcd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use larkooo/gemma-e2b-rlcd with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download larkooo/gemma-e2b-rlcd --local-dir gemma-e2b-rlcd
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 845 Bytes
53e24ca | 1 2 3 4 5 6 7 8 | # Demo assets
`demo.mp4` is a 24-second, 1080p, silent replay of the measured support-triage comparison. Both clocks use real elapsed-time scale; each answer appears when its measured median completion time elapses. It is a designed replay rather than a screen recording. Eight representative fields are shown; all 28 outputs are in `demo-metrics.json`.
The source is [`support_28` in the workload report](../../reports/demo-workload-benchmark.json): four measured repetitions per path after warmup, 4.4145 seconds parallel versus 15.6597 seconds normal generation. All 28 outputs match; both paths pass 15/17 annotated checks. The metrics file retains the report hash and complete methodology.
The video uses IBM Plex Sans under the [SIL Open Font License](Plex-OFL.txt). The repository URL and measured results appear in the closing frame.
|