Datasetpublic

odyn-network/benchmark-dpo-hyperparameters-v1

Odyn benchmark: DPO LoRA fine-tuning hyperparameters (V1) Curated benchmark of real, cited DPO + LoRA fine-tuning configurations for validating a hyperparameter advisor. Each row is a published or measured config (from a framework example, model card, or write-up) with its hyperparameters — learning rate, LoRA rank/alpha/dropout, epochs, batch, beta, loss type, gradient checkpointing — plus the dataset it trained on and per-field provenance. Schema Column… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/benchmark-dpo-hyperparameters-v1.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Dataset Card

Odyn benchmark: DPO LoRA fine-tuning hyperparameters (V1)

Curated benchmark of real, cited DPO + LoRA fine-tuning configurations for validating a hyperparameter advisor. Each row is a published or measured config (from a framework example, model card, or write-up) with its hyperparameters — learning rate, LoRA rank/alpha/dropout, epochs, batch, beta, loss type, gradient checkpointing — plus the dataset it trained on and per-field provenance.

Schema

Column	Type	Description
id	string	Unique row id
model	string	Base model name
modelsizeb	float	Model size (billions of parameters)
base_precision	string	Training precision: full, bf16, fp16, 8bit, 4bit
finetuning_type	string	lora or qlora
lora_rank	int	LoRA rank
lora_alpha	int	LoRA alpha (scaling)
lora_dropout	float	LoRA dropout
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
training_objective	string	dpo, kto, orpo, cpo, mpo
beta	float	Preference regularization strength (n/a for ORPO)
loss_type	string	sigmoid, hinge, ipo, kto, orpo, etc.
gradient_checkpointing	bool	GC enabled
dataset_samples	int	Preference pairs actually trained on
dataset	string	Dataset id (NR if the source did not disclose it)
cite	string	Human-readable citation
source_url	string	Link to primary source
field_provenance	string	Per-field origin: stated, framework_default, derived, assumed, or NR (with method)

Conventions: NR = not recorded / unrecoverable from the source. n/a = the field does not apply to that objective (e.g. beta for ORPO). Provenance is tracked per field so stated (from the source), framework_default (unset → the framework's default), and derived (computed, e.g. from trainer_state.json step math, cross-checked) are never conflated.

Provenance & recovery

Values were recovered from primary sources only: framework example YAMLs (LLaMA-Factory, TRL, axolotl, alignment-handbook), Hugging Face model cards, adapter_config.json, all_results.json / trainer_state.json, training_args.bin (pickle-inspected, not executed), the HF datasets-server API for row counts, and published blogs/notebooks. Anything not stated or safely derivable is left NR rather than guessed.

Sources

Rows cite LLaMA-Factory, TRL, and Axolotl example configs; Hugging Face model cards and cookbook notebooks; the alignment-handbook; and write-ups from philschmid, Anyscale, and mlabonne. See cite and source_url per row.

Usage

python

from datasets import load_dataset

ds = load_dataset("odyn-network/benchmark-finetune-dpo-configs-v1", split="train")
print(ds[0]["model"], ds[0]["training_objective"], ds[0]["learning_rate"])