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odyn-network/benchmark-finetune-dpo-v1

Odyn benchmark: DPO LoRA fine-tuning peak VRAM (V1) Curated benchmark rows for validating GPU memory estimators during DPO + LoRA fine-tuning. Each row pairs a published or measured expected peak VRAM with inputs to a math engine (model size, context length, batch, LoRA rank, precision, parallelism) plus optional VRAM breakdown and provenance. This dataset is not preference-pair training JSONL (UltraFeedback-style). It is evaluation ground truth for placement / scheduler memory… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/benchmark-finetune-dpo-v1.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Odyn benchmark: DPO LoRA fine-tuning peak VRAM (V1)

Curated benchmark rows for validating GPU memory estimators during DPO + LoRA fine-tuning. Each row pairs a published or measured expected peak VRAM with inputs to a math engine (model size, context length, batch, LoRA rank, precision, parallelism) plus optional VRAM breakdown and provenance.

This dataset is not preference-pair training JSONL (UltraFeedback-style). It is evaluation ground truth for placement / scheduler memory models (Odyn Smart Digester math engine), sibling to `odyn-network/benchmark-finetune-lora-v1`.

Engine estimates use the LlamaFactory-style DPO LoRA path (pair_factor on activations/logits; no second full weight copy / disable_adapter). Some expected_* rows reflect TRL dual-reference paths and will disagree with that model by design — see source / measurement_scope.

Schema

Column	Type	Description
`description`	string	Human-readable scenario label
`expected_peak_vram_gb`	float	Reference peak VRAM (GB) from source
`validation_status`	string	`confirmed`, `estimated`, or `unverified`
`math_engine_peak_vram_gb`	float	Odyn math engine estimate (GB)
`math_engine_tier_gb`	float	Recommended GPU tier (GB)
`vram_vs_expected_pct`	float	`(math_engine - expected) / expected * 100`
`tier_vs_expected_pct`	float	Tier headroom vs expected
`breakdown_*_gb`	float	Weights, activations, optimizer, gradients, temp buffers, overhead
`measurement_scope`	string	e.g. `single_gpu`, `per_gpu_distributed`
`input_param_b`	float	Model size (billions of parameters)
`input_context_length`	int	Sequence / context length
`input_batch_size`	int	Per-step batch size
`input_gradient_accumulation_steps`	int	Gradient accumulation
`input_lora_rank`	int	LoRA rank (nullable)
`input_precision`	string	e.g. `bf16`, `fp16`
`input_num_gpus`	int	GPU count
`input_parallelism`	string	e.g. `none`, `ddp_zero2`, `ddp_zero3`
`tolerance_pct`	int	Acceptance band used in eval
`gradient_checkpointing`	bool	GC enabled
`source`	string	Citation / origin
`source_url`	string	Link to primary source

Sources

Rows cite Clore.ai TRL guides, Axolotl DPO guides, TRL GitHub issues, LlamaFactory / 360-LLaMA-Factory papers, and related public VRAM notes. See source and source_url per row.

Usage

python

from datasets import load_dataset

ds = load_dataset("odyn-network/benchmark-finetune-dpo-v1", split="train")
print(ds[0]["description"], ds[0]["expected_peak_vram_gb"])

Version

—V1 — 18 scenarios (benchmark_finetune_dpo_dataset_V1.csv)