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
Download adapter_config.json from NorviTech/arrmes-functiongemma-270m: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/NorviTech/arrmes-functiongemma-270m/resolve/main/adapter_config.json
- Command line
-
hf download hf://NorviTech/arrmes-functiongemma-270m/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/NorviTech/arrmes-functiongemma-270m/resolve/main/adapter_config.json
1.03 kB
| { | |
| "adapter_path": "adapters/functiongemma-arrmes-v7", | |
| "batch_size": 2, | |
| "config": "configs/functiongemma-arrmes-v7.yaml", | |
| "data": "data/train-v7/mlx_functiongemma", | |
| "fine_tune_type": "lora", | |
| "grad_accumulation_steps": 1, | |
| "grad_checkpoint": true, | |
| "iters": 80, | |
| "learning_rate": 2e-05, | |
| "lora_parameters": { | |
| "rank": 8, | |
| "dropout": 0.0, | |
| "scale": 16.0 | |
| }, | |
| "lr_schedule": null, | |
| "mask_prompt": true, | |
| "max_seq_length": 2560, | |
| "model": "models/functiongemma", | |
| "num_layers": -1, | |
| "optimizer": "adam", | |
| "optimizer_config": { | |
| "adam": {}, | |
| "adamw": {}, | |
| "muon": {}, | |
| "sgd": {}, | |
| "adafactor": {} | |
| }, | |
| "project_name": null, | |
| "report_to": null, | |
| "resume_adapter_file": "adapters/functiongemma-distilled-v5/adapters.safetensors", | |
| "save_every": 80, | |
| "seed": 20261102, | |
| "steps_per_eval": 80, | |
| "steps_per_report": 30, | |
| "test": false, | |
| "test_batches": 500, | |
| "train": true, | |
| "val_batches": 4 | |
| } | |