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: 1,034 Bytes
72fc9e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"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
}
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