Datasets:

odyn-network
/

lora-hyperparameter-benchmark-v1

Curated benchmark of real, cited LoRA and QLoRA fine-tuning configurations for validating a hyperparameter advisor. Each row is a published or measured supervised (SFT) LoRA config with its hyperparameters (learning rate, LoRA rank/alpha/dropout, epochs, batch, sequence length, gradient checkpointing), the dataset it trained on, and per-field provenance.

Schema

Column	Type	Description
id	string	Unique row id
model	string	Base model name
model_size_b	float	Model size (billions of parameters)
base_precision	string	Training precision: full, 8bit, 4bit, awq-4bit, gptq-4bit, aqlm-2bit
lora_rank	int	LoRA rank
lora_alpha	int	LoRA alpha as stated by the source
lora_alpha_effective	float	Alpha after resolution (value the run effectively used)
lora_alpha_provenance	string	Origin of the alpha value (stated, framework_default, derived)
lora_dropout	float	LoRA dropout as stated
lora_dropout_effective	float	Dropout after resolution
lora_dropout_provenance	string	Origin of the dropout value
learning_rate	float	Learning rate
num_epochs	float	Training epochs (n/a where step-based)
batch_size	int	Per-device batch size
grad_accum	int	Gradient accumulation steps
seq_len	int	Sequence length / cutoff
gradient_checkpointing	bool	GC enabled
gradient_checkpointing_provenance	string	Origin of the GC value
dataset_samples	int	Training samples the run used
dataset	string	Dataset id (NR if the source did not disclose it)
cite	string	Human-readable citation
source_url	string	Link to primary source

Conventions: NR means not recorded or unrecoverable from the source. A stated value is the raw number the source gives. An effective value is what the run would actually use after normalization, and the matching provenance column records how it was determined (stated, framework_default, or derived). The advisor_warnings and advisor_suggestions columns hold the advisor output for the row, so the false-positive check is reproducible from the file alone (an accepted config should carry no warnings).

Provenance and recovery

Values were recovered from primary sources only: framework example configs, Hugging Face model cards, adapter_config.json, all_results.json and trainer_state.json, training_args.bin (inspected as a pickle, not executed), the HF datasets-server API for row counts, and published notebooks or write-ups. Anything not stated or safely derivable is left NR rather than guessed.

Sources

Rows cite framework examples and recipes (LlamaFactory, TRL, Axolotl, Unsloth, PEFT), Hugging Face model cards and notebooks, and public fine-tuning write-ups. See cite and source_url per row.

Usage

from datasets import load_dataset

ds = load_dataset("odyn-network/benchmark-finetune-lora-configs-v2", split="train")
print(ds[0]["model"], ds[0]["lora_rank"], ds[0]["learning_rate"])