Fine-Tuning & Custom Models implementation checklist

Verify that every training example strictly follows the target model's prompt format (e.g., ChatML, Alpaca, or Llama-3-Instruct) to prevent formatting-induced performance degradation.

Run a fuzzy matching script to remove near-duplicate entries in the training set that can cause overfitting on specific phrases.

Calculate the token count for every example using the target tokenizer to ensure no samples exceed the model's maximum context length during training.

Execute a regex or NLP-based scanner to identify and remove Personally Identifiable Information (PII) from the training corpus to meet compliance standards.

Verify that the dataset contains a representative ratio of edge cases and common queries to prevent the model from becoming biased toward high-frequency patterns.

Document the choice of LoRA rank (r) and alpha; ensure alpha is typically set to 2x the rank to maintain scaling stability.

Set gradient accumulation steps to achieve an effective batch size that balances training stability with available VRAM limits.

Configure the training pipeline to save checkpoints based on validation loss rather than step count to capture the best performing weights.

If using QLoRA, verify that the 4-bit NormalFloat (NF4) data type is utilized to maximize memory efficiency without significant precision loss.

Integrate Weights & Biases or MLflow to track training vs. validation loss in real-time to detect early-stage divergence or overfitting.

Test the final adapter against a 10-20% hold-out set that the model never saw during training or hyperparameter tuning.

Run the fine-tuned model against a set of general-purpose prompts to ensure that domain-specific tuning hasn't caused 'catastrophic forgetting' of basic reasoning.

Run the same prompt through the model multiple times at temperature 0 to ensure output consistency for structured data tasks.

Conduct a blind A/B test where domain experts rank outputs from the fine-tuned model against the base model or previous versions.

Measure Time To First Token (TTFT) and tokens per second (TPS) across different batch sizes to define production hardware requirements.

Decide between merging LoRA weights into the base model for faster inference or keeping them separate for multi-tenant adapter swapping.

Validate the model's compatibility with optimized engines like vLLM, TGI, or TensorRT-LLM to maximize throughput.

Evaluate the performance impact of converting the final model to AWQ or GGUF formats to reduce VRAM footprint in production.

Configure a liveness and readiness probe that verifies the model is loaded in VRAM and responding to a simple 'ping' prompt.

Implement a warm-up strategy for new pods to prevent initial request timeouts while weights are being loaded from storage to GPU memory.

Store a manifest file with the model binary linking it to the specific dataset version, commit hash of training code, and hyperparameter log.

Set up monitoring for token usage per API key or user to calculate the unit economics of the custom model deployment.

Establish a process to collect production logs for periodic comparison against the training distribution to identify when retraining is necessary.

Verify that the base model's license (e.g., Llama 3 Community License) allows for the intended commercial use and distribution of the fine-tuned weights.

Implement an automated fallback to a larger foundation model or a previous stable version if the fine-tuned model returns an error or low-confidence score.