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PROJECT "FineTunedLLM"
DESCRIPTION "Fine-tuning a small LLM with checkpoint resume and custom callbacks"
VERSION "1.0"
AUTHOR "OktoSeek"

DATASET {

    train: "dataset/instruction_train.jsonl"

    validation: "dataset/instruction_val.jsonl"

    test: "dataset/instruction_test.jsonl"

    format: "instruction"

    type: "generation"

    language: "en"
}

MODEL {

    base: "oktoseek/base-llm-7b"

    architecture: "transformer"

    parameters: 7B

    context_window: 4096

    precision: "fp16"
}

TRAIN {

    epochs: 5

    batch_size: 4

    gradient_accumulation: 8

    learning_rate: 0.0001

    optimizer: "adamw"

    scheduler: "cosine_with_restarts"

    loss: "cross_entropy"

    device: "cuda"

    gpu: true

    mixed_precision: true

    early_stopping: true

    checkpoint_steps: 100

    checkpoint_path: "./checkpoints"

    weight_decay: 0.01

    gradient_clip: 1.0

    warmup_steps: 100

    save_strategy: "steps"
}

METRICS {
    loss
    perplexity
    bleu
    rouge_l
    token_efficiency
    response_coherence
}

VALIDATE {

    on_train: false

    on_validation: true

    frequency: 1

    save_best_model: true

    metric_to_monitor: "loss"
}

INFERENCE {

    max_tokens: 512

    temperature: 0.7

    top_p: 0.9

    top_k: 50

    repetition_penalty: 1.1

    stop_sequences: ["\n\nHuman:", "\n\nAssistant:"]
}

EXPORT {

    format: ["gguf", "onnx", "okm", "safetensors"]

    path: "export/"

    quantization: "int8"

    optimize_for: "speed"
}

DEPLOY {

    target: "api"

    endpoint: "http://localhost:9000/llm"

    requires_auth: true

    port: 9000

    max_concurrent_requests: 50
}

HOOKS {

    before_train: "scripts/preprocess_data.py"

    after_epoch: "scripts/custom_early_stop.py"

    on_checkpoint: "scripts/backup_checkpoint.sh"
}

LOGGING {

    save_logs: true

    metrics_file: "runs/finetuned-llm/metrics.json"

    training_file: "runs/finetuned-llm/training_logs.json"

    log_level: "info"

    log_every: 10
}