---
license: cc-by-nc-4.0
task_categories:
- text-generation
tags:
- lm-eval
- benchpress
- evaluation-results
configs:
- config_name: default
  data_files:
  - split: runs
    path: runs.parquet
  - split: aggregates
    path: aggregates.parquet
  - split: sample_scores
    path: sample_scores.parquet
---

# Benchpress Evaluation Results

This dataset contains normalized outputs from `lm-evaluation-harness`
runs for the Benchpress project. It is intended for analysis of
post-training recipe behavior across models, benchmarks, temperatures,
and random seeds.

## Coverage

- Runs: 110
- Model/stage recipes: olmo2-13b:dpo, olmo2-13b:rlvr, olmo2-13b:sft, olmo2-7b:dpo, olmo2-7b:rlvr, olmo2-7b:sft, olmo3-7b:dpo, olmo3-7b:rlvr, olmo3-7b:sft, smollm3-3b:apo, smollm3-3b:sft
- Temperatures: 0.0, 0.4, 0.7, 1.0
- Seeds: 0, 1, 2
- Tasks: 30
- Benchmark groups: arc, gsm8k_cot, hellaswag, humaneval_instruct, ifeval, mgsm_en_cot, truthfulqa_mc2
- Languages/settings: ar, bn, de, en, en_challenge, en_easy, es, fr, hi, ja, ru, sw, te, th, zh

## Important: benchmark questions/prompts are not included

To avoid redistributing third-party benchmark content under licenses
we cannot relicense (notably the multilingual Okapi subsets, which are
**CC BY-NC 4.0**), this release **does not contain the verbatim
prompt/question text**. The `sample_scores` split has no `doc`,
`arguments`, or full-`sample` columns.

What we keep is our own or derivable: model responses (`resps_json`),
the ground-truth `target`, scalar metrics and scores, and the stable
`doc_hash` / `prompt_hash` identifiers emitted by lm-eval. Those hashes
let you **re-join** these results to the original questions by loading
the upstream datasets yourself (under their own licenses) and hashing
their docs/prompts the same way. See the attribution table below for
the exact upstream dataset for each benchmark group.

Note: these are lm-eval's own `doc_hash` / `prompt_hash` values, not a
plain SHA-256 of the text. Reproducing them to re-join requires the
same `lm-evaluation-harness` version and task configuration used here
(see the `lm_eval_version` field in the `runs` split).

## Temperature/Seed Design

- Temperature `0.0` is evaluated once per recipe with seed(s): 0.
- Sampled temperatures 0.4, 0.7, 1.0 are evaluated with seeds 0, 1, 2.

## Splits

- `runs`: 110 rows
- `aggregates`: 12100 rows
- `sample_scores`: 7511570 rows

## Split Contents

- `runs`: one row per completed eval run/cell. The canonical run table:
  cell_slug, model, stage, stage_group, checkpoint repo, revision,
  temperature, seed, generation kwargs, and tool versions.
- `aggregates`: task-level metrics per run. Slim provenance
  (model, stage, seed, temperature) plus task/benchmark/language/
  metric/filter/value. Join to `runs` on those four keys for the rest.
- `sample_scores`: one row per evaluated sample/filter. Slim provenance
  plus `doc_id`, `doc_hash`, `prompt_hash`, `target`, primary
  metric/score, scalar metrics, and model `resps_json`. No verbatim
  prompt/question columns (see above).

## What Is Not Included

- Model weights are not included.
- Verbatim benchmark questions/prompts are not included (see above).
- Denormalized run metadata is not duplicated onto every `aggregates` /
  `sample_scores` row; join to the `runs` split on
  (model, stage, seed, temperature) instead.
- Raw JSON/JSONL/log files and file inventories are not included.

## Attribution and Licensing of Source Benchmarks

These results were produced by evaluating the models below on the
following public benchmarks via `lm-evaluation-harness`. Each benchmark
remains under its own license; the table records the exact upstream
Hugging Face dataset and its license. **Note the multilingual subsets
are non-commercial (CC BY-NC 4.0).**

| Benchmark group | Setting(s) | Upstream dataset | License |
| --- | --- | --- | --- |
| `arc` | en_easy, en_challenge | [`allenai/ai2_arc`](https://huggingface.co/datasets/allenai/ai2_arc) | CC BY-SA 4.0 |
| `arc` | es, ar, hi, ru (multilingual) | [`alexandrainst/m_arc`](https://huggingface.co/datasets/alexandrainst/m_arc) | CC BY-NC 4.0 (non-commercial) |
| `hellaswag` | en | [`Rowan/hellaswag`](https://huggingface.co/datasets/Rowan/hellaswag) | MIT |
| `hellaswag` | es, ar, hi, ru (multilingual) | [`alexandrainst/m_hellaswag`](https://huggingface.co/datasets/alexandrainst/m_hellaswag) | CC BY-NC 4.0 (non-commercial) |
| `truthfulqa_mc2` | en | [`truthfulqa/truthful_qa`](https://huggingface.co/datasets/truthfulqa/truthful_qa) | Apache-2.0 |
| `truthfulqa_mc2` | es, ar, hi, ru (multilingual) | [`alexandrainst/m_truthfulqa`](https://huggingface.co/datasets/alexandrainst/m_truthfulqa) | CC BY-NC 4.0 (non-commercial) |
| `gsm8k_cot` | en | [`openai/gsm8k`](https://huggingface.co/datasets/openai/gsm8k) | MIT |
| `mgsm_en_cot` | en, es, fr, de, ru, zh, ja, bn, sw, te, th (multilingual) | [`juletxara/mgsm`](https://huggingface.co/datasets/juletxara/mgsm) | CC BY-SA 4.0 |
| `ifeval` | en | [`google/IFEval`](https://huggingface.co/datasets/google/IFEval) | Apache-2.0 |
| `humaneval_instruct` | en | [`openai/openai_humaneval`](https://huggingface.co/datasets/openai/openai_humaneval) | MIT |

### Benchmark citations

- **arc** (allenai/ai2_arc): Clark et al., 2018, *Think you have Solved Question Answering? Try ARC* (arXiv:1803.05457).
- **arc** (alexandrainst/m_arc): Lai et al., 2023, *Okapi: Instruction-tuned LLMs in Multiple Languages with RLHF* (arXiv:2307.16039).
- **hellaswag** (Rowan/hellaswag): Zellers et al., 2019, *HellaSwag: Can a Machine Really Finish Your Sentence?* (arXiv:1905.07830).
- **hellaswag** (alexandrainst/m_hellaswag): Lai et al., 2023, *Okapi* (arXiv:2307.16039).
- **truthfulqa_mc2** (truthfulqa/truthful_qa): Lin et al., 2022, *TruthfulQA: Measuring How Models Mimic Human Falsehoods* (arXiv:2109.07958).
- **truthfulqa_mc2** (alexandrainst/m_truthfulqa): Lai et al., 2023, *Okapi* (arXiv:2307.16039).
- **gsm8k_cot** (openai/gsm8k): Cobbe et al., 2021, *Training Verifiers to Solve Math Word Problems* (arXiv:2110.14168).
- **mgsm_en_cot** (juletxara/mgsm): Shi et al., 2022, *Language Models are Multilingual Chain-of-Thought Reasoners* (arXiv:2210.03057); derived from GSM8K (Cobbe et al., 2021).
- **ifeval** (google/IFEval): Zhou et al., 2023, *Instruction-Following Evaluation for Large Language Models* (arXiv:2311.07911).
- **humaneval_instruct** (openai/openai_humaneval): Chen et al., 2021, *Evaluating Large Language Models Trained on Code* (arXiv:2107.03374).

## Models Evaluated

All evaluated checkpoints are publicly available on the Hugging Face Hub
under permissive (Apache-2.0) licenses. Please cite the corresponding
model papers/releases when using these results.

- **SmolLM3 (3B)** — `HuggingFaceTB/SmolLM3-3B` (Apache-2.0). Hugging Face, 2025, *SmolLM3: smol, multilingual, long-context reasoner* (https://hf.co/blog/smollm3).
- **OLMo 2 (7B, 13B)** — `allenai/OLMo-2-1124-7B-{SFT,DPO,Instruct}, allenai/OLMo-2-1124-13B-{SFT,DPO,Instruct}` (Apache-2.0). OLMo Team, 2025, *2 OLMo 2 Furious* (arXiv:2501.00656).
- **OLMo 3 (7B)** — `allenai/Olmo-3-7B-{SFT,DPO,Instruct}` (Apache-2.0). Allen Institute for AI, 2025, *Olmo 3* (arXiv:2512.13961).

Exact checkpoint repositories evaluated:

- [`HuggingFaceTB/SmolLM3-3B-checkpoints`](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints)
- [`allenai/OLMo-2-1124-13B-DPO`](https://huggingface.co/allenai/OLMo-2-1124-13B-DPO)
- [`allenai/OLMo-2-1124-13B-Instruct`](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct)
- [`allenai/OLMo-2-1124-13B-SFT`](https://huggingface.co/allenai/OLMo-2-1124-13B-SFT)
- [`allenai/OLMo-2-1124-7B-DPO`](https://huggingface.co/allenai/OLMo-2-1124-7B-DPO)
- [`allenai/OLMo-2-1124-7B-Instruct`](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct)
- [`allenai/OLMo-2-1124-7B-SFT`](https://huggingface.co/allenai/OLMo-2-1124-7B-SFT)
- [`allenai/Olmo-3-7B-Instruct`](https://huggingface.co/allenai/Olmo-3-7B-Instruct)
- [`allenai/Olmo-3-7B-Instruct-DPO`](https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO)
- [`allenai/Olmo-3-7B-Instruct-SFT`](https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT)

## Loading

```python
from datasets import load_dataset

ds = load_dataset('parquet', data_files={
    'runs': 'runs.parquet',
    'aggregates': 'aggregates.parquet',
    'sample_scores': 'sample_scores.parquet',
})
```