| --- |
| 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"]) |
| |
| |
| |
