---
license: apache-2.0
language:
- en
pretty_name: "Odyn DPO Hyperparameter Config Benchmark (V1)"
tags:
- dpo
- lora
- preference-optimization
- fine-tuning
- hyperparameters
- benchmark
- evaluation
size_categories:
- n<1K
configs:
- config_name: default
  data_files: benchmark_dpo_lora_configs.csv
---

# 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 |
| model_size_b | 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"])