100 Fine-Tuning & Custom Models resources for developers

Structure training data into the messages-array format required by the OpenAI Fine-Tuning API to ensure compatibility with gpt-4o-mini and gpt-3.5-turbo fine-tuning endpoints.

Utilize the .map() function with batched=True to apply tokenization and formatting scripts across large-scale datasets efficiently in memory.

Implement a human-in-the-loop interface to label and curate high-quality preference data for RLHF or DPO workflows.

Use a teacher model (e.g., GPT-4) to rewrite existing instruction sets into more complex versions, increasing the depth of the training signal.

Run Cleanlab's Datalab to automatically identify and remove outlier or mislabeled examples that can degrade fine-tuning performance.

Leverage larger open models to generate synthetic reasoning chains (Chain of Thought) for training smaller 7B or 8B models.

Apply MinHash and Locality Sensitive Hashing (LSH) to remove near-duplicate examples from your corpus to prevent overfitting on repetitive data.

Use Lilac to visualize embeddings and clusters within your training set to identify gaps in domain coverage before starting a training run.

Map your dataset in latent space to verify that the fine-tuning data is sufficiently distinct from the base model's pre-training distribution.

Inject DDL statements directly into the prompt field of your training data to teach the model specific database schemas.

Use the Unsloth library to achieve 2x faster fine-tuning and 70% less VRAM usage for Llama-3, Mistral, and Gemma models.

Implement 4-bit NormalFloat quantization to fine-tune 70B parameter models on a single 48GB GPU (like an A6000).

Standardize experiments using Axolotl's declarative YAML configuration files to manage hyperparameters, dataset paths, and LoRA settings.

Set r=16 or r=32 for most tasks; ensure lora_alpha is set to 2x the rank to maintain consistent scaling of the weights.

Enable DeepSpeed ZeRO-3 for full-parameter fine-tuning to shard model states across multiple GPUs when PEFT is insufficient.

Target all linear layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj) in LoRA to achieve maximum performance.

Use the TRL library to align models with preference pairs (chosen vs. rejected) without the complexity of PPO.

Add random noise to the embedding layer during training to improve the model's robustness and generalization to unseen prompts.

Log loss curves, GPU utilization, and gradient norms to identify training instability or vanishing gradients in real-time.

Enable Flash Attention 2 in your training scripts to reduce memory overhead and speed up the attention mechanism by up to 3x.

Run the EleutherAI Evaluation Harness across standard benchmarks (MMLU, GSM8K) to detect regressions in general reasoning.

Deploy fine-tuned weights using vLLM for high-throughput serving with PagedAttention and continuous batching.

Use LoRAX to serve hundreds of different fine-tuned adapters on a single base model instance to minimize infrastructure costs.

Use GPT-4 as an automated evaluator to score your fine-tuned model's responses against a reference set of high-quality answers.

Convert fine-tuned weights to GGUF format using llama.cpp to run custom models on consumer hardware or edge devices.

Run systematic tests comparing the base model vs. the fine-tuned model across hundreds of test cases to quantify improvement.

Always save and distribute model weights in .safetensors format to prevent arbitrary code execution vulnerabilities associated with pickles.

Deploy via Hugging Face TGI for production features like dynamic batching, stop sequences, and optimized CUDA kernels.

Use Mergekit to combine your fine-tuned LoRA weights with other domain-specific models using SLERP or TIES merging techniques.

Create a custom Modelfile to package your fine-tuned weights, system prompt, and parameters for easy distribution within a team.