Instructions to use NorviTech/arrmes-functiongemma-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use NorviTech/arrmes-functiongemma-270m with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download NorviTech/arrmes-functiongemma-270m --local-dir arrmes-functiongemma-270m
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,246 Bytes
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license: gemma
base_model: google/functiongemma-270m-it
tags:
- mlx
- lora
- functiongemma
- tool-calling
- arrmes
---
# Arrmes FunctionGemma-270M v9 Residual FP16 QLoRA Adapter (`v1.6.0`)
Unassisted residual FP16 LoRA adapter over the 4-bit `FunctionGemma-270M` base (`183.1 MiB` total footprint), subject to the [Gemma Terms of Use](https://ai.google.dev/gemma/terms) (`GEMMA_TERMS_NOTICE.txt`).

## Artifact & Unassisted Benchmark Summary (`LocalModel`, `backend="mlx-lm"`)
- **`v9/adapters.safetensors` SHA-256**: `a0337f083c362d66f406129d0ee6f4661597055bd51772233241c0e2690cf124`
- **Template-Disjoint `acceptance-v7` `v2` Positives (`N=150`)**: Track A Raw `126/150` (`84.0%`), Track B Verified `135/150` (`90.0%`, Wilson 95% LB `84.16%`)
- **Template-Disjoint `acceptance-v7` Hard Negatives (`N=150`)**: Track A Raw `49/150` (`32.67%`), Track B Verified `133/150` (`88.67%`, Wilson 95% LB `82.60%`)
- **Frozen `overnight-v1` (`N=80`)**: Track A Raw `59/80` (`73.75%`), Track B Verified `69/80` (`86.25%`)
- **Apple M4 Pro Metal Warm Latency**: `p50 = 110.08 ms`, `p95 = 124.80 ms` (`183.1 MiB` combined 4-bit base + FP16 LoRA footprint).
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