Instructions to use KikoCis/gemma-4-31b-agent-v6-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use KikoCis/gemma-4-31b-agent-v6-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gemma-4-31b-agent-v6-MLX KikoCis/gemma-4-31b-agent-v6-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Sanitize adapter_config: keep only LoRA-loading fields, drop training-process params
Browse files- adapters/adapter_config.json +3 -35
adapters/adapter_config.json
CHANGED
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@@ -1,41 +1,9 @@
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{
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"adapter_path": "adapters/gemma4-31b-v6",
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"batch_size": 2,
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"clear_cache_threshold": 0,
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"config": "/tmp/v6_31b_train_config.yaml",
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"data": "benchmarks/dataset_v6",
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"fine_tune_type": "lora",
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"
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"grad_checkpoint": true,
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"iters": 1000,
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"learning_rate": 3e-05,
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"lora_parameters": {
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"rank": 8,
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"dropout": 0.0,
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"scale": 20.0
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}
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"mask_prompt": true,
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"max_seq_length": 2048,
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"model": "mlx/gemma4-31b-q4",
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"num_layers": 16,
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"optimizer": "adam",
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"optimizer_config": {
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"adam": {},
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"adamw": {},
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"muon": {},
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"sgd": {},
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"adafactor": {}
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},
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"project_name": null,
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"report_to": null,
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"resume_adapter_file": null,
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"save_every": 250,
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"seed": 0,
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"steps_per_eval": 250,
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"steps_per_report": 25,
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"test": false,
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"test_batches": 500,
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"train": true,
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"val_batches": 25
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}
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{
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"fine_tune_type": "lora",
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"num_layers": 16,
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"lora_parameters": {
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"rank": 8,
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"dropout": 0.0,
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"scale": 20.0
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}
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}
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