Instructions to use infosave/Nanbeige4.2-3Bcmf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- cortiq
How to use infosave/Nanbeige4.2-3Bcmf with cortiq:
# one Rust binary, no additional dependencies cargo install cortiq-cli # or a prebuilt binary from github.com/infosave2007/cmf/releases hf download infosave/Nanbeige4.2-3Bcmf --include "*.cmf" --local-dir . ls *.cmf # some repos ship more than one quantization
cortiq run FILE.cmf --prompt "What is the capital of France?"
cortiq serve FILE.cmf --port 8080 # OpenAI-compatible server
- Notebooks
- Google Colab
- Kaggle
Commit History
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gfx skill FINAL: masked runtime gate -5.1% vs backbone (3.974/4.187), replica 3.923; trained masters in q8_row; apply: cortiq skill apply nanbeige42-3b-q4t.cmf nanbeige42-gfx.skill.cmf -o gfx.cmf
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coding skill FINAL: masked runtime gate -1.1% vs backbone (9.633/9.743); trained masters in q8_row; apply: cortiq skill apply nanbeige42-3b-q4t.cmf nanbeige42-code.skill.cmf -o code.cmf
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coding skill (557 MB delta): masked replica -2.2% vs backbone (9.529/9.746), bare runtime gate +7.7% (mask not yet applied on quantized storage); phase A bottom uncaptured at 150 steps - a longer A will deepen it. apply: cortiq skill apply nanbeige42-3b-q4t.cmf nanbeige42-code.skill.cmf -o code.cmf
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gfx specialist v2: strict-f32 phase A, bottom 4.225@4.4%, gate -1.5% vs backbone
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standalone gfx skill (557 MB delta): cortiq skill apply nanbeige42-3b-q4t.cmf nanbeige42-gfx.skill.cmf -o specialist.cmf
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Upload experiments/nanbeige42-gfx-pervisit-skill.cmf with huggingface_hub
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Upload experiments/nanbeige42-gfx-pervisit-skill.cmf with huggingface_hub
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