adapter_config.json

{
    "adapter_path": "adapters-lfm",
    "batch_size": 1,
    "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% elif message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + '\n' }}{% endif %}{% endfor %}",
    "config": "lora_config_lfm.yaml",
    "data": "datasets",
    "fine_tune_type": "lora",
    "grad_accumulation_steps": 1,
    "grad_checkpoint": false,
    "iters": 1000,
    "learning_rate": 1e-05,
    "lora": {
        "rank": 8,
        "scale": 10.0,
        "dropout": 0.0,
        "keys": [
            "self_attn.q_proj",
            "self_attn.v_proj"
        ],
        "num_layers": 12
    },
    "lora_parameters": {
        "rank": 8,
        "dropout": 0.0,
        "scale": 20.0
    },
    "lr_schedule": null,
    "mask_prompt": false,
    "max_seq_length": 2048,
    "model": "LiquidAI/LFM2.5-350M-MLX-4bit",
    "num_layers": 16,
    "optimizer": "adam",
    "optimizer_config": {
        "adam": {},
        "adamw": {},
        "muon": {},
        "sgd": {},
        "adafactor": {}
    },
    "project_name": null,
    "report_to": null,
    "resume_adapter_file": null,
    "save_every": 100,
    "seed": 0,
    "steps_per_eval": 1000,
    "steps_per_report": 200,
    "test": false,
    "test_batches": 500,
    "train": true,
    "val_batches": 10
}