Datasets:

Lucasgitguy-co
/

LvW-bench

---
license: mit
language:
- en
pretty_name: LvW-bench
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- question-answering
- text-generation
tags:
- benchmark
- small-models
- tiny-models
- system-one
- system-two
- structured-decisions
- calibration
- reasoning
- local-inference
- gguf
- safetensors
configs:
- config_name: spot_snake
  data_files:
  - split: test
    path: spot_snake/test-*.parquet
  - split: train
    path: spot_snake/train-*.parquet
- config_name: it_support
  data_files:
  - split: test
    path: it_support/test-*.parquet
  - split: train
    path: it_support/train-*.parquet
- config_name: delivery
  data_files:
  - split: test
    path: delivery/test-*.parquet
  - split: train
    path: delivery/train-*.parquet
- config_name: code_review
  data_files:
  - split: test
    path: code_review/test-*.parquet
  - split: train
    path: code_review/train-*.parquet
- config_name: calendar
  data_files:
  - split: test
    path: calendar/test-*.parquet
  - split: train
    path: calendar/train-*.parquet
- config_name: decide_all
  default: true
  data_files:
  - split: test
    path: decide_all/test-*.parquet
  - split: train
    path: decide_all/train-*.parquet
- config_name: gsm8k
  data_files:
  - split: test
    path: gsm8k/test-*.parquet
- config_name: mmlu
  data_files:
  - split: test
    path: mmlu/test-*.parquet
- config_name: mmlu_pro
  data_files:
  - split: test
    path: mmlu_pro/test-*.parquet
- config_name: truthfulqa
  data_files:
  - split: test
    path: truthfulqa/test-*.parquet
- config_name: ifeval
  data_files:
  - split: test
    path: ifeval/test-*.parquet
- config_name: generated
  data_files:
  - split: test
    path: generated/test-*.parquet
- config_name: generative_all
  data_files:
  - split: test
    path: generative_all/test-*.parquet
- config_name: reason_subset
  data_files:
  - split: test
    path: reason_subset/test-*.parquet
---
# LvW-bench

A benchmark for tiny models that you run on your own machine. It scores both kinds of small
model in one place:

- **System 1, `decide`:** typed decisions. The model gets a state and typed questions
  (`choice`, `score`, `noul`) and answers each with a probability distribution. Scores are
  accuracy, KL from gold, Brier and ECE, the same as
  [LocalLLaMA/typed-decisions](https://huggingface.co/datasets/LocalLLaMA/typed-decisions).
  Native decision models (d1, Jev-style heads) answer through `/v1/systemone`. A text model
  answers through the probabilities of its first output token.
- **System 2, `answer` and `reason`:** generated answers on permissive subsets of GSM8K, MMLU,
  MMLU-Pro, TruthfulQA and IFEval, plus a seeded generated set. Each model runs twice, once
  with thinking off and a small budget (`answer`) and once with thinking on (`reason`).
  Thinking is native `<think>` when the chat template has it, and step by step otherwise.

The runner is `lvwb`, from the [GitHub repo](https://github.com/Gitguy268/LvW-bench). It runs
on macOS, Linux and Windows, from the terminal (CLI and TUI), from a desktop app, or in the
browser. It accepts a Hugging Face link or id, a local `.gguf` file, a local `.safetensors`
folder, or any OpenAI-compatible server.

![Accuracy against KL for System 1, and answer against reason accuracy for System 2](https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench/resolve/main/assets/leaderboard.png)

## Leaderboard

Bench version 0.1.0, `standard` profile:

- `decide` uses the whole `decide_all` test split (500 cases, 1,500 decisions).
- `answer` uses the whole `generative_all` test split (1,143 items).
- `reason` uses `reason_subset` (240 items, 40 per set, 2,048-token budget).

All runs were local, on llama.cpp for GGUF and transformers for safetensors. Every row
records the exact repo commit, file, quant and engine build. The result and prediction files
are in [`results/`](https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench/tree/main/results).

#### System 1 · decide

| # | Model | Accuracy ↑ | KL ↓ | Brier ↓ | ECE ↓ | Coverage | p50 / case | Adapter |
|---|---|---|---|---|---|---|---|---|
| 1 | [LiquidAI/d1-2B-GGUF](https://huggingface.co/LiquidAI/d1-2B-GGUF) · GGUF Q4_K_M | 0.580 | 0.635 | 0.360 | 0.107 | 100% | 1698 ms | systemone |
| 2 | [Lucasgitguy-co/lfm3-vl-2b-heretic-gguf](https://huggingface.co/Lucasgitguy-co/lfm3-vl-2b-heretic-gguf) · GGUF Q4_K_M | 0.432 | 1.914 | 0.684 | 0.359 | 100% | 3294 ms | logprobs |
| 3 | [LiquidAI/LFM2-350M-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-GGUF) · GGUF Q4_K_M | 0.410 | 1.320 | 0.555 | 0.240 | 100% | 918 ms | logprobs |
| 4 | [Lucasgitguy-co/minicpm-mermaid-gguf](https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-gguf) · GGUF Q4_K_M | 0.396 | 1.606 | 0.685 | 0.378 | 100% | 3424 ms | logprobs |
| 5 | [bartowski/SmolLM2-1.7B-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-1.7B-Instruct-GGUF) · GGUF Q4_K_M | 0.383 | 0.813 | 0.479 | 0.144 | 100% | 3183 ms | logprobs |
| 6 | [unsloth/Qwen3-0.6B-GGUF](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) · GGUF Q4_K_M | 0.373 | 1.680 | 0.752 | 0.410 | 100% | 1494 ms | logprobs |
| 7 | [Lucasgitguy-co/minimind-mermaid-run2](https://huggingface.co/Lucasgitguy-co/minimind-mermaid-run2)/ep3 · safetensors | 0.353 | 2.669 | 0.631 | 0.347 | 79% | 335 ms | logprobs |
| 8 | [LiquidAI/LFM2-1.2B-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF) · GGUF Q4_K_M | 0.299 | 1.980 | 0.798 | 0.489 | 100% | 2286 ms | logprobs |
| 9 | [bartowski/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-135M-Instruct-GGUF) · GGUF Q4_K_M | 0.282 | 0.888 | 0.519 | 0.224 | 100% | 175 ms | logprobs |
| 10 | [bartowski/SmolLM2-360M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-360M-Instruct-GGUF) · GGUF Q4_K_M | 0.271 | 0.909 | 0.505 | 0.203 | 100% | 577 ms | logprobs |
| – | Prior (ignores the input) | 0.482 | 0.631 | 0.373 | 0.022 | – | – | reference |
| – | Uniform (same probability on every option) | 0.331 | 0.709 | 0.429 | 0.105 | – | – | reference |

#### System 1 · decide: specialists

Trained on the same task as a workflow (the Spot Snake models learned `spot_snake` moves with the same teacher), so not comparable with the zero-shot table.

| # | Model | Accuracy ↑ | KL ↓ | Brier ↓ | ECE ↓ | Coverage | p50 / case | Adapter |
|---|---|---|---|---|---|---|---|---|
| 1 | [Lucasgitguy-co/spot-snake-d1-3B-v0.1](https://huggingface.co/Lucasgitguy-co/spot-snake-d1-3B-v0.1) · GGUF Q4_K_M | 0.563 | 2.372 | 0.487 | 0.272 | 100% | 2172 ms | systemone |
| 2 | [Lucasgitguy-co/spot-snake-d1-2B-v0.1](https://huggingface.co/Lucasgitguy-co/spot-snake-d1-2B-v0.1) · GGUF Q4_K_M | 0.535 | 1.722 | 0.565 | 0.329 | 100% | 1564 ms | systemone |
| 3 | [Lucasgitguy-co/spot-snake-minimind-jev-v0.1](https://huggingface.co/Lucasgitguy-co/spot-snake-minimind-jev-v0.1) · safetensors | 0.334 | 0.709 | 0.429 | 0.102 | 7% | 0 ms | systemone |

#### System 2 · answer

| # | Model | Accuracy ↑ | 95% CI | gsm8k | mmlu | mmlu_pro | truthfulqa | ifeval | generated | tok/answer | tok/s |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | [Lucasgitguy-co/minicpm-mermaid-gguf](https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-gguf) · GGUF Q4_K_M | 0.396 | 0.368–0.425 | 0.080 | 0.535 | 0.186 | 0.360 | 0.710 | 0.413 | 27 | 57 |
| 2 | [LiquidAI/LFM2-1.2B-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF) · GGUF Q4_K_M | 0.385 | 0.355–0.413 | 0.010 | 0.504 | 0.150 | 0.333 | 0.755 | 0.449 | 74 | 159 |
| 3 | [Lucasgitguy-co/lfm3-vl-2b-heretic-gguf](https://huggingface.co/Lucasgitguy-co/lfm3-vl-2b-heretic-gguf) · GGUF Q4_K_M | 0.382 | 0.355–0.410 | 0.045 | 0.408 | 0.186 | 0.360 | 0.735 | 0.480 | 54 | 91 |
| 4 | [bartowski/SmolLM2-1.7B-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-1.7B-Instruct-GGUF) · GGUF Q4_K_M | 0.305 | 0.279–0.333 | 0.030 | 0.548 | 0.171 | 0.213 | 0.530 | 0.249 | 64 | 82 |
| 5 | [unsloth/Qwen3-0.6B-GGUF](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) · GGUF Q4_K_M | 0.298 | 0.273–0.326 | 0.025 | 0.386 | 0.136 | 0.320 | 0.605 | 0.267 | 57 | 129 |
| 6 | [LiquidAI/LFM2-350M-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-GGUF) · GGUF Q4_K_M | 0.269 | 0.241–0.293 | 0.020 | 0.447 | 0.121 | 0.220 | 0.565 | 0.169 | 89 | 346 |
| 7 | [bartowski/SmolLM2-360M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-360M-Instruct-GGUF) · GGUF Q4_K_M | 0.212 | 0.187–0.236 | 0.045 | 0.259 | 0.136 | 0.207 | 0.390 | 0.204 | 82 | 233 |
| 8 | [bartowski/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-135M-Instruct-GGUF) · GGUF Q4_K_M | 0.158 | 0.137–0.181 | 0.015 | 0.228 | 0.100 | 0.213 | 0.225 | 0.156 | 110 | 749 |
| 9 | [Lucasgitguy-co/minimind-mermaid-run2](https://huggingface.co/Lucasgitguy-co/minimind-mermaid-run2)/ep3 · safetensors | 0.029 | 0.019–0.039 | 0.010 | 0.009 | 0.007 | 0.000 | 0.105 | 0.031 | 193 | 399 |
#### System 2 · reason
| # | Model | Accuracy ↑ | 95% CI | gsm8k | mmlu | mmlu_pro | truthfulqa | ifeval | generated | tok/answer | tok/s |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | [LiquidAI/LFM2-1.2B-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF) · GGUF Q4_K_M | 0.517 | 0.450–0.579 | 0.600 | 0.500 | 0.300 | 0.375 | 0.750 | 0.575 | 461 | 206 |
| 2 | [Lucasgitguy-co/lfm3-vl-2b-heretic-gguf](https://huggingface.co/Lucasgitguy-co/lfm3-vl-2b-heretic-gguf) · GGUF Q4_K_M | 0.508 | 0.450–0.571 | 0.700 | 0.500 | 0.250 | 0.375 | 0.450 | 0.775 | 1477 | 140 |
| 3 | [Lucasgitguy-co/minicpm-mermaid-gguf](https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-gguf) · GGUF Q4_K_M | 0.404 | 0.338–0.463 | 0.100 | 0.475 | 0.350 | 0.425 | 0.650 | 0.425 | 488 | 168 |
| 4 | [bartowski/SmolLM2-1.7B-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-1.7B-Instruct-GGUF) · GGUF Q4_K_M | 0.379 | 0.317–0.442 | 0.400 | 0.500 | 0.275 | 0.200 | 0.425 | 0.475 | 316 | 103 |
| 5 | [LiquidAI/LFM2-350M-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-GGUF) · GGUF Q4_K_M | 0.317 | 0.258–0.375 | 0.425 | 0.400 | 0.200 | 0.125 | 0.500 | 0.250 | 350 | 502 |
| 6 | [unsloth/Qwen3-0.6B-GGUF](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) · GGUF Q4_K_M | 0.312 | 0.254–0.371 | 0.650 | 0.075 | 0.025 | 0.025 | 0.650 | 0.450 | 924 | 187 |
| 7 | [bartowski/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-135M-Instruct-GGUF) · GGUF Q4_K_M | 0.146 | 0.104–0.192 | 0.025 | 0.150 | 0.225 | 0.150 | 0.225 | 0.100 | 954 | 603 |
| 8 | [bartowski/SmolLM2-360M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-360M-Instruct-GGUF) · GGUF Q4_K_M | 0.129 | 0.087–0.171 | 0.075 | 0.075 | 0.100 | 0.050 | 0.250 | 0.225 | 507 | 203 |
| 9 | [Lucasgitguy-co/minimind-mermaid-run2](https://huggingface.co/Lucasgitguy-co/minimind-mermaid-run2)/ep3 · safetensors | 0.033 | 0.013–0.058 | 0.000 | 0.000 | 0.000 | 0.000 | 0.175 | 0.025 | 2202 | 370 |

To add a model, run it with `lvwb run <model>` and open a
[discussion](https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench/discussions). Attach the
result JSON and its `.predictions.jsonl`, and say whether the model is general or a
specialist. `lvwb submit <result.json>` prints the steps. `bench/score.py` rescores the
predictions file to the same numbers without the model.

## Benchmarking a model

```bash
brew trust gitguy268/tap && brew install gitguy268/tap/lvw-bench   # or a release binary
lvwb run Lucasgitguy-co/spot-snake-d1-2B-v0.1           # HF id: picks the GGUF, starts llama-server
lvwb run https://huggingface.co/unsloth/Qwen3-0.6B-GGUF # HF link
lvwb run ~/models/my-model-Q4_K_M.gguf                  # local GGUF
lvwb run ~/models/my-model/                             # local safetensors folder (uv + transformers)
lvwb run http://127.0.0.1:8080/v1 --backend openai      # a server that is already running
lvwb tui                                                # pick a preset in the terminal UI
```

- `--track decide|answer|reason`
- `--profile quick|standard|full`
- `--quant Q8_0`, `--subfolder ep3`, `--revision refs/pr/1`
- `--seed N` draws a fresh generated set
- `--mode specialist` if the model was trained on `train`

GGUF needs `llama-server` (`brew install llama.cpp`). Native `/v1/systemone` decisions need
llama.cpp 0.9.0 or later. Safetensors need [uv](https://docs.astral.sh/uv/). The first run
installs PyTorch in a cached environment.

<details><summary>Rescoring with Python only</summary>

```bash
pip install datasets
python bench/score.py my-model__standard__20261105-120000.predictions.jsonl
```

`bench/score.py` holds the same rules as the Go scorer, and a golden test file keeps the two
identical. The System 1 part follows typed-decisions' `bench/score_with_jobs.py`.

</details>

## Reading a score

- **System 1 gold is computed by rules, not by a teacher model.** Each workflow builds a
  case from sampled facts and derives the gold distribution from fixed rules. Facts that can
  be read off the state, such as whether two meetings overlap, have a sharp gold. Judgement
  calls, such as which action fits a damaged parcel, keep a soft gold. A score measures
  agreement with those rules. The Prior row, label frequencies from `train` that ignore the
  input, is the floor to beat.
- **System 2 scores generated answers.** The scorer extracts the final answer (the
  `Answer:` line, the leading letter or the last number) after removing `<think>` blocks. A
  think block that never closes means the budget ran out, and that answer counts as wrong.
  **These numbers are not comparable with lm-eval loglikelihood scores** (for example TPN's
  ARC or MMLU `acc_norm`), which compare option probabilities and never let the model write.
- **General and specialist rows are not comparable.** The Spot Snake models were trained on
  Spot's moves with the same teacher and text as `spot_snake`, so they are listed as
  specialists. `spot_snake` has more "pocket" positions than real games, where one move closes
  Spot in. The Spot Snake d1 models mostly keep going straight there, so they score lower than
  their in-game teacher agreement.
- **Coverage** is the share of questions a model answered. A head that only ranks moves
  answers the other questions with nothing, and those are scored as uniform.

## The data

| Config | Track | Train | Test | Source | Licence |
|---|---|---|---|---|---|
| `spot_snake` | decide | 300 | 100 | original, rule gold | MIT |
| `it_support` | decide | 300 | 100 | original, rule gold | MIT |
| `delivery` | decide | 300 | 100 | original, rule gold | MIT |
| `code_review` | decide | 300 | 100 | original, rule gold | MIT |
| `calendar` | decide | 300 | 100 | original, rule gold | MIT |
| `decide_all` | decide | 1500 | 500 | all five workflows | MIT |
| `gsm8k` | answer, reason | – | 200 | openai/gsm8k test | MIT |
| `mmlu` | answer, reason | – | 228 | cais/mmlu test, 4 per subject | MIT |
| `mmlu_pro` | answer, reason | – | 140 | TIGER-Lab/MMLU-Pro test, 10 per category | MIT |
| `truthfulqa` | answer, reason | – | 150 | truthfulqa/truthful_qa mc1, options shuffled | Apache-2.0 |
| `ifeval` | answer, reason | – | 200 | google/IFEval, verifiable subset | Apache-2.0 |
| `generated` | answer, reason | – | 225 | original, seeded generator (seed 7) | MIT |
| `generative_all` | answer | – | 1143 | the six sets above | mixed, see rows |
| `reason_subset` | reason | – | 240 | 40 per set, the items the reason leaderboard runs | mixed, see rows |

### System 1 rows

`state` and `questions` together are the body of a `POST /v1/systemone` request. `gold`
holds the gold distribution per question. The flat columns `<question>__label`,
`__probabilities`, `__confidence`, `__score` and `__probability_true` hold the same values.
`factors` describe how the case was built and are not model input.

| Workflow | Questions |
|---|---|
| `spot_snake` | `move` (choice), `eats_now` (noul), `boxed_in_risk` (score 0–3) |
| `it_support` | `category` (choice), `priority` (score), `needs_onsite` (noul) |
| `delivery` | `action` (choice), `notify_customer` (noul), `severity` (score) |
| `code_review` | `decision` (choice), `risk` (score), `needs_tests` (noul) |
| `calendar` | `clash` (noul), `resolution` (choice), `urgency` (score) |

```python
from datasets import load_dataset
import json
row = load_dataset("Lucasgitguy-co/LvW-bench", "decide_all", split="test")[0]
state, questions, gold = (json.loads(row[k]) for k in ("state", "questions", "gold"))
```

### System 2 rows

Each row has `prompt`, `choices` (multiple choice only), `answer` and `answer_type`.
`answer_type` is one of `mc`, `number`, `exact`, `ifeval`, `json` or `mermaid`. The checker
for each type is in `bench/score.py`. `meta` holds what the checker needs: IFEval
instruction ids and kwargs, required JSON values, or required Mermaid edges.

## How it was built

- **Sources:** permissive subsets are sampled with a fixed seed. ARC (CC-BY-SA) and HellaSwag
  (no licence) are left out. TruthfulQA options are shuffled, because upstream always lists
  the correct one first. IFEval keeps only prompts whose instructions can be verified (17
  instruction types).
- **System 1:** `train` comes from different seeds than `test`, and the build refuses to
  package if a state appears in both.
- **Generated set:** 9 families of 25 items each: arithmetic, word problems, letter counts,
  sorting, weekdays and dates, syllogisms, list lookups, JSON output and Mermaid flowcharts.
  Every answer is computed, so `--seed` makes a fresh set no model can have memorised.

## Limits

- The models are small and the sets are small. Read the 95% confidence intervals before
  ranking two close rows.
- System 1 gold follows simple rules written for this benchmark. A model that reasons
  better than the rules can score lower.
- Running a text model on System 1 through first-token probabilities depends on the chat
  template. Backends without logprobs fall back to sampling 8 answers per question, marked
  `sampled`.
- Text only in v0.1. Vision-language models run in text mode.

## Licence

The original data, code and results are MIT. The rows from GSM8K, MMLU and MMLU-Pro stay
MIT, and the rows from TruthfulQA and IFEval stay Apache-2.0. Each row's `license` column
says which applies. See `NOTICE` and `licenses/`. Models keep their own licences.

```bibtex
@misc{lvwbench2026,
  title  = {LvW-bench: a local benchmark for tiny System 1 and System 2 models},
  author = {Van De Weerd, Lucas},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench}
}
```

	---
	license: mit
	language:
	- en
	pretty_name: LvW-bench
	size_categories:
	- 1K<n<10K
	task_categories:
	- text-classification
	- question-answering
	- text-generation
	tags:
	- benchmark
	- small-models
	- tiny-models
	- system-one
	- system-two
	- structured-decisions
	- calibration
	- reasoning
	- local-inference
	- gguf
	- safetensors
	configs:
	- config_name: spot_snake
	data_files:
	- split: test
	path: spot_snake/test-*.parquet
	- split: train
	path: spot_snake/train-*.parquet
	- config_name: it_support
	data_files:
	- split: test
	path: it_support/test-*.parquet
	- split: train
	path: it_support/train-*.parquet
	- config_name: delivery
	data_files:
	- split: test
	path: delivery/test-*.parquet
	- split: train
	path: delivery/train-*.parquet
	- config_name: code_review
	data_files:
	- split: test
	path: code_review/test-*.parquet
	- split: train
	path: code_review/train-*.parquet
	- config_name: calendar
	data_files:
	- split: test
	path: calendar/test-*.parquet
	- split: train
	path: calendar/train-*.parquet
	- config_name: decide_all
	default: true
	data_files:
	- split: test
	path: decide_all/test-*.parquet
	- split: train
	path: decide_all/train-*.parquet
	- config_name: gsm8k
	data_files:
	- split: test
	path: gsm8k/test-*.parquet
	- config_name: mmlu
	data_files:
	- split: test
	path: mmlu/test-*.parquet
	- config_name: mmlu_pro
	data_files:
	- split: test
	path: mmlu_pro/test-*.parquet
	- config_name: truthfulqa
	data_files:
	- split: test
	path: truthfulqa/test-*.parquet
	- config_name: ifeval
	data_files:
	- split: test
	path: ifeval/test-*.parquet
	- config_name: generated
	data_files:
	- split: test
	path: generated/test-*.parquet
	- config_name: generative_all
	data_files:
	- split: test
	path: generative_all/test-*.parquet
	- config_name: reason_subset
	data_files:
	- split: test
	path: reason_subset/test-*.parquet
	---

	# LvW-bench

	A benchmark for tiny models that you run on your own machine. It scores both kinds of small
	model in one place:

	- System 1, `decide`: typed decisions. The model gets a state and typed questions
	(`choice`, `score`, `noul`) and answers each with a probability distribution. Scores are
	accuracy, KL from gold, Brier and ECE, the same as
	[LocalLLaMA/typed-decisions](https://huggingface.co/datasets/LocalLLaMA/typed-decisions).
	Native decision models (d1, Jev-style heads) answer through `/v1/systemone`. A text model
	answers through the probabilities of its first output token.
	- System 2, `answer` and `reason`: generated answers on permissive subsets of GSM8K, MMLU,
	MMLU-Pro, TruthfulQA and IFEval, plus a seeded generated set. Each model runs twice, once
	with thinking off and a small budget (`answer`) and once with thinking on (`reason`).
	Thinking is native `<think>` when the chat template has it, and step by step otherwise.

	The runner is `lvwb`, from the [GitHub repo](https://github.com/Gitguy268/LvW-bench). It runs
	on macOS, Linux and Windows, from the terminal (CLI and TUI), from a desktop app, or in the
	browser. It accepts a Hugging Face link or id, a local `.gguf` file, a local `.safetensors`
	folder, or any OpenAI-compatible server.

	![Accuracy against KL for System 1, and answer against reason accuracy for System 2](https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench/resolve/main/assets/leaderboard.png)

	## Leaderboard

	Bench version 0.1.0, `standard` profile:

	- `decide` uses the whole `decide_all` test split (500 cases, 1,500 decisions).
	- `answer` uses the whole `generative_all` test split (1,143 items).
	- `reason` uses `reason_subset` (240 items, 40 per set, 2,048-token budget).

	All runs were local, on llama.cpp for GGUF and transformers for safetensors. Every row
	records the exact repo commit, file, quant and engine build. The result and prediction files
	are in [`results/`](https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench/tree/main/results).

	#### System 1 · decide

	\| # \| Model \| Accuracy ↑ \| KL ↓ \| Brier ↓ \| ECE ↓ \| Coverage \| p50 / case \| Adapter \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| 1 \| [LiquidAI/d1-2B-GGUF](https://huggingface.co/LiquidAI/d1-2B-GGUF) · GGUF Q4_K_M \| 0.580 \| 0.635 \| 0.360 \| 0.107 \| 100% \| 1698 ms \| systemone \|
	\| 2 \| [Lucasgitguy-co/lfm3-vl-2b-heretic-gguf](https://huggingface.co/Lucasgitguy-co/lfm3-vl-2b-heretic-gguf) · GGUF Q4_K_M \| 0.432 \| 1.914 \| 0.684 \| 0.359 \| 100% \| 3294 ms \| logprobs \|
	\| 3 \| [LiquidAI/LFM2-350M-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-GGUF) · GGUF Q4_K_M \| 0.410 \| 1.320 \| 0.555 \| 0.240 \| 100% \| 918 ms \| logprobs \|
	\| 4 \| [Lucasgitguy-co/minicpm-mermaid-gguf](https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-gguf) · GGUF Q4_K_M \| 0.396 \| 1.606 \| 0.685 \| 0.378 \| 100% \| 3424 ms \| logprobs \|
	\| 5 \| [bartowski/SmolLM2-1.7B-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-1.7B-Instruct-GGUF) · GGUF Q4_K_M \| 0.383 \| 0.813 \| 0.479 \| 0.144 \| 100% \| 3183 ms \| logprobs \|
	\| 6 \| [unsloth/Qwen3-0.6B-GGUF](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) · GGUF Q4_K_M \| 0.373 \| 1.680 \| 0.752 \| 0.410 \| 100% \| 1494 ms \| logprobs \|
	\| 7 \| [Lucasgitguy-co/minimind-mermaid-run2](https://huggingface.co/Lucasgitguy-co/minimind-mermaid-run2)/ep3 · safetensors \| 0.353 \| 2.669 \| 0.631 \| 0.347 \| 79% \| 335 ms \| logprobs \|
	\| 8 \| [LiquidAI/LFM2-1.2B-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF) · GGUF Q4_K_M \| 0.299 \| 1.980 \| 0.798 \| 0.489 \| 100% \| 2286 ms \| logprobs \|
	\| 9 \| [bartowski/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-135M-Instruct-GGUF) · GGUF Q4_K_M \| 0.282 \| 0.888 \| 0.519 \| 0.224 \| 100% \| 175 ms \| logprobs \|
	\| 10 \| [bartowski/SmolLM2-360M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-360M-Instruct-GGUF) · GGUF Q4_K_M \| 0.271 \| 0.909 \| 0.505 \| 0.203 \| 100% \| 577 ms \| logprobs \|
	\| – \| Prior (ignores the input) \| 0.482 \| 0.631 \| 0.373 \| 0.022 \| – \| – \| reference \|
	\| – \| Uniform (same probability on every option) \| 0.331 \| 0.709 \| 0.429 \| 0.105 \| – \| – \| reference \|

	#### System 1 · decide: specialists

	Trained on the same task as a workflow (the Spot Snake models learned `spot_snake` moves with the same teacher), so not comparable with the zero-shot table.

	\| # \| Model \| Accuracy ↑ \| KL ↓ \| Brier ↓ \| ECE ↓ \| Coverage \| p50 / case \| Adapter \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| 1 \| [Lucasgitguy-co/spot-snake-d1-3B-v0.1](https://huggingface.co/Lucasgitguy-co/spot-snake-d1-3B-v0.1) · GGUF Q4_K_M \| 0.563 \| 2.372 \| 0.487 \| 0.272 \| 100% \| 2172 ms \| systemone \|
	\| 2 \| [Lucasgitguy-co/spot-snake-d1-2B-v0.1](https://huggingface.co/Lucasgitguy-co/spot-snake-d1-2B-v0.1) · GGUF Q4_K_M \| 0.535 \| 1.722 \| 0.565 \| 0.329 \| 100% \| 1564 ms \| systemone \|
	\| 3 \| [Lucasgitguy-co/spot-snake-minimind-jev-v0.1](https://huggingface.co/Lucasgitguy-co/spot-snake-minimind-jev-v0.1) · safetensors \| 0.334 \| 0.709 \| 0.429 \| 0.102 \| 7% \| 0 ms \| systemone \|

	#### System 2 · answer

	\| # \| Model \| Accuracy ↑ \| 95% CI \| gsm8k \| mmlu \| mmlu_pro \| truthfulqa \| ifeval \| generated \| tok/answer \| tok/s \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| 1 \| [Lucasgitguy-co/minicpm-mermaid-gguf](https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-gguf) · GGUF Q4_K_M \| 0.396 \| 0.368–0.425 \| 0.080 \| 0.535 \| 0.186 \| 0.360 \| 0.710 \| 0.413 \| 27 \| 57 \|
	\| 2 \| [LiquidAI/LFM2-1.2B-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF) · GGUF Q4_K_M \| 0.385 \| 0.355–0.413 \| 0.010 \| 0.504 \| 0.150 \| 0.333 \| 0.755 \| 0.449 \| 74 \| 159 \|
	\| 3 \| [Lucasgitguy-co/lfm3-vl-2b-heretic-gguf](https://huggingface.co/Lucasgitguy-co/lfm3-vl-2b-heretic-gguf) · GGUF Q4_K_M \| 0.382 \| 0.355–0.410 \| 0.045 \| 0.408 \| 0.186 \| 0.360 \| 0.735 \| 0.480 \| 54 \| 91 \|
	\| 4 \| [bartowski/SmolLM2-1.7B-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-1.7B-Instruct-GGUF) · GGUF Q4_K_M \| 0.305 \| 0.279–0.333 \| 0.030 \| 0.548 \| 0.171 \| 0.213 \| 0.530 \| 0.249 \| 64 \| 82 \|
	\| 5 \| [unsloth/Qwen3-0.6B-GGUF](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) · GGUF Q4_K_M \| 0.298 \| 0.273–0.326 \| 0.025 \| 0.386 \| 0.136 \| 0.320 \| 0.605 \| 0.267 \| 57 \| 129 \|
	\| 6 \| [LiquidAI/LFM2-350M-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-GGUF) · GGUF Q4_K_M \| 0.269 \| 0.241–0.293 \| 0.020 \| 0.447 \| 0.121 \| 0.220 \| 0.565 \| 0.169 \| 89 \| 346 \|
	\| 7 \| [bartowski/SmolLM2-360M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-360M-Instruct-GGUF) · GGUF Q4_K_M \| 0.212 \| 0.187–0.236 \| 0.045 \| 0.259 \| 0.136 \| 0.207 \| 0.390 \| 0.204 \| 82 \| 233 \|
	\| 8 \| [bartowski/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-135M-Instruct-GGUF) · GGUF Q4_K_M \| 0.158 \| 0.137–0.181 \| 0.015 \| 0.228 \| 0.100 \| 0.213 \| 0.225 \| 0.156 \| 110 \| 749 \|
	\| 9 \| [Lucasgitguy-co/minimind-mermaid-run2](https://huggingface.co/Lucasgitguy-co/minimind-mermaid-run2)/ep3 · safetensors \| 0.029 \| 0.019–0.039 \| 0.010 \| 0.009 \| 0.007 \| 0.000 \| 0.105 \| 0.031 \| 193 \| 399 \|

	#### System 2 · reason

	\| # \| Model \| Accuracy ↑ \| 95% CI \| gsm8k \| mmlu \| mmlu_pro \| truthfulqa \| ifeval \| generated \| tok/answer \| tok/s \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| 1 \| [LiquidAI/LFM2-1.2B-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF) · GGUF Q4_K_M \| 0.517 \| 0.450–0.579 \| 0.600 \| 0.500 \| 0.300 \| 0.375 \| 0.750 \| 0.575 \| 461 \| 206 \|
	\| 2 \| [Lucasgitguy-co/lfm3-vl-2b-heretic-gguf](https://huggingface.co/Lucasgitguy-co/lfm3-vl-2b-heretic-gguf) · GGUF Q4_K_M \| 0.508 \| 0.450–0.571 \| 0.700 \| 0.500 \| 0.250 \| 0.375 \| 0.450 \| 0.775 \| 1477 \| 140 \|
	\| 3 \| [Lucasgitguy-co/minicpm-mermaid-gguf](https://huggingface.co/Lucasgitguy-co/minicpm-mermaid-gguf) · GGUF Q4_K_M \| 0.404 \| 0.338–0.463 \| 0.100 \| 0.475 \| 0.350 \| 0.425 \| 0.650 \| 0.425 \| 488 \| 168 \|
	\| 4 \| [bartowski/SmolLM2-1.7B-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-1.7B-Instruct-GGUF) · GGUF Q4_K_M \| 0.379 \| 0.317–0.442 \| 0.400 \| 0.500 \| 0.275 \| 0.200 \| 0.425 \| 0.475 \| 316 \| 103 \|
	\| 5 \| [LiquidAI/LFM2-350M-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-GGUF) · GGUF Q4_K_M \| 0.317 \| 0.258–0.375 \| 0.425 \| 0.400 \| 0.200 \| 0.125 \| 0.500 \| 0.250 \| 350 \| 502 \|
	\| 6 \| [unsloth/Qwen3-0.6B-GGUF](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) · GGUF Q4_K_M \| 0.312 \| 0.254–0.371 \| 0.650 \| 0.075 \| 0.025 \| 0.025 \| 0.650 \| 0.450 \| 924 \| 187 \|
	\| 7 \| [bartowski/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-135M-Instruct-GGUF) · GGUF Q4_K_M \| 0.146 \| 0.104–0.192 \| 0.025 \| 0.150 \| 0.225 \| 0.150 \| 0.225 \| 0.100 \| 954 \| 603 \|
	\| 8 \| [bartowski/SmolLM2-360M-Instruct-GGUF](https://huggingface.co/bartowski/SmolLM2-360M-Instruct-GGUF) · GGUF Q4_K_M \| 0.129 \| 0.087–0.171 \| 0.075 \| 0.075 \| 0.100 \| 0.050 \| 0.250 \| 0.225 \| 507 \| 203 \|
	\| 9 \| [Lucasgitguy-co/minimind-mermaid-run2](https://huggingface.co/Lucasgitguy-co/minimind-mermaid-run2)/ep3 · safetensors \| 0.033 \| 0.013–0.058 \| 0.000 \| 0.000 \| 0.000 \| 0.000 \| 0.175 \| 0.025 \| 2202 \| 370 \|

	To add a model, run it with `lvwb run <model>` and open a
	[discussion](https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench/discussions). Attach the
	result JSON and its `.predictions.jsonl`, and say whether the model is general or a
	specialist. `lvwb submit <result.json>` prints the steps. `bench/score.py` rescores the
	predictions file to the same numbers without the model.

	## Benchmarking a model

	```bash
	brew trust gitguy268/tap && brew install gitguy268/tap/lvw-bench # or a release binary
	lvwb run Lucasgitguy-co/spot-snake-d1-2B-v0.1 # HF id: picks the GGUF, starts llama-server
	lvwb run https://huggingface.co/unsloth/Qwen3-0.6B-GGUF # HF link
	lvwb run ~/models/my-model-Q4_K_M.gguf # local GGUF
	lvwb run ~/models/my-model/ # local safetensors folder (uv + transformers)
	lvwb run http://127.0.0.1:8080/v1 --backend openai # a server that is already running
	lvwb tui # pick a preset in the terminal UI
	```

	- `--track decide\|answer\|reason`
	- `--profile quick\|standard\|full`
	- `--quant Q8_0`, `--subfolder ep3`, `--revision refs/pr/1`
	- `--seed N` draws a fresh generated set
	- `--mode specialist` if the model was trained on `train`

	GGUF needs `llama-server` (`brew install llama.cpp`). Native `/v1/systemone` decisions need
	llama.cpp 0.9.0 or later. Safetensors need [uv](https://docs.astral.sh/uv/). The first run
	installs PyTorch in a cached environment.

	<details><summary>Rescoring with Python only</summary>

	```bash
	pip install datasets
	python bench/score.py my-model__standard__20261105-120000.predictions.jsonl
	```

	`bench/score.py` holds the same rules as the Go scorer, and a golden test file keeps the two
	identical. The System 1 part follows typed-decisions' `bench/score_with_jobs.py`.

	</details>

	## Reading a score

	- System 1 gold is computed by rules, not by a teacher model. Each workflow builds a
	case from sampled facts and derives the gold distribution from fixed rules. Facts that can
	be read off the state, such as whether two meetings overlap, have a sharp gold. Judgement
	calls, such as which action fits a damaged parcel, keep a soft gold. A score measures
	agreement with those rules. The Prior row, label frequencies from `train` that ignore the
	input, is the floor to beat.
	- System 2 scores generated answers. The scorer extracts the final answer (the
	`Answer:` line, the leading letter or the last number) after removing `<think>` blocks. A
	think block that never closes means the budget ran out, and that answer counts as wrong.
	These numbers are not comparable with lm-eval loglikelihood scores (for example TPN's
	ARC or MMLU `acc_norm`), which compare option probabilities and never let the model write.
	- General and specialist rows are not comparable. The Spot Snake models were trained on
	Spot's moves with the same teacher and text as `spot_snake`, so they are listed as
	specialists. `spot_snake` has more "pocket" positions than real games, where one move closes
	Spot in. The Spot Snake d1 models mostly keep going straight there, so they score lower than
	their in-game teacher agreement.
	- Coverage is the share of questions a model answered. A head that only ranks moves
	answers the other questions with nothing, and those are scored as uniform.

	## The data

	\| Config \| Track \| Train \| Test \| Source \| Licence \|
	\|---\|---\|---\|---\|---\|---\|
	\| `spot_snake` \| decide \| 300 \| 100 \| original, rule gold \| MIT \|
	\| `it_support` \| decide \| 300 \| 100 \| original, rule gold \| MIT \|
	\| `delivery` \| decide \| 300 \| 100 \| original, rule gold \| MIT \|
	\| `code_review` \| decide \| 300 \| 100 \| original, rule gold \| MIT \|
	\| `calendar` \| decide \| 300 \| 100 \| original, rule gold \| MIT \|
	\| `decide_all` \| decide \| 1500 \| 500 \| all five workflows \| MIT \|
	\| `gsm8k` \| answer, reason \| – \| 200 \| openai/gsm8k test \| MIT \|
	\| `mmlu` \| answer, reason \| – \| 228 \| cais/mmlu test, 4 per subject \| MIT \|
	\| `mmlu_pro` \| answer, reason \| – \| 140 \| TIGER-Lab/MMLU-Pro test, 10 per category \| MIT \|
	\| `truthfulqa` \| answer, reason \| – \| 150 \| truthfulqa/truthful_qa mc1, options shuffled \| Apache-2.0 \|
	\| `ifeval` \| answer, reason \| – \| 200 \| google/IFEval, verifiable subset \| Apache-2.0 \|
	\| `generated` \| answer, reason \| – \| 225 \| original, seeded generator (seed 7) \| MIT \|
	\| `generative_all` \| answer \| – \| 1143 \| the six sets above \| mixed, see rows \|
	\| `reason_subset` \| reason \| – \| 240 \| 40 per set, the items the reason leaderboard runs \| mixed, see rows \|

	### System 1 rows

	`state` and `questions` together are the body of a `POST /v1/systemone` request. `gold`
	holds the gold distribution per question. The flat columns `<question>__label`,
	`__probabilities`, `__confidence`, `__score` and `__probability_true` hold the same values.
	`factors` describe how the case was built and are not model input.

	\| Workflow \| Questions \|
	\|---\|---\|
	\| `spot_snake` \| `move` (choice), `eats_now` (noul), `boxed_in_risk` (score 0–3) \|
	\| `it_support` \| `category` (choice), `priority` (score), `needs_onsite` (noul) \|
	\| `delivery` \| `action` (choice), `notify_customer` (noul), `severity` (score) \|
	\| `code_review` \| `decision` (choice), `risk` (score), `needs_tests` (noul) \|
	\| `calendar` \| `clash` (noul), `resolution` (choice), `urgency` (score) \|

	```python
	from datasets import load_dataset
	import json
	row = load_dataset("Lucasgitguy-co/LvW-bench", "decide_all", split="test")[0]
	state, questions, gold = (json.loads(row[k]) for k in ("state", "questions", "gold"))
	```

	### System 2 rows

	Each row has `prompt`, `choices` (multiple choice only), `answer` and `answer_type`.
	`answer_type` is one of `mc`, `number`, `exact`, `ifeval`, `json` or `mermaid`. The checker
	for each type is in `bench/score.py`. `meta` holds what the checker needs: IFEval
	instruction ids and kwargs, required JSON values, or required Mermaid edges.

	## How it was built

	- Sources: permissive subsets are sampled with a fixed seed. ARC (CC-BY-SA) and HellaSwag
	(no licence) are left out. TruthfulQA options are shuffled, because upstream always lists
	the correct one first. IFEval keeps only prompts whose instructions can be verified (17
	instruction types).
	- System 1: `train` comes from different seeds than `test`, and the build refuses to
	package if a state appears in both.
	- Generated set: 9 families of 25 items each: arithmetic, word problems, letter counts,
	sorting, weekdays and dates, syllogisms, list lookups, JSON output and Mermaid flowcharts.
	Every answer is computed, so `--seed` makes a fresh set no model can have memorised.

	## Limits

	- The models are small and the sets are small. Read the 95% confidence intervals before
	ranking two close rows.
	- System 1 gold follows simple rules written for this benchmark. A model that reasons
	better than the rules can score lower.
	- Running a text model on System 1 through first-token probabilities depends on the chat
	template. Backends without logprobs fall back to sampling 8 answers per question, marked
	`sampled`.
	- Text only in v0.1. Vision-language models run in text mode.

	## Licence

	The original data, code and results are MIT. The rows from GSM8K, MMLU and MMLU-Pro stay
	MIT, and the rows from TruthfulQA and IFEval stay Apache-2.0. Each row's `license` column
	says which applies. See `NOTICE` and `licenses/`. Models keep their own licences.

	```bibtex
	@misc{lvwbench2026,
	title = {LvW-bench: a local benchmark for tiny System 1 and System 2 models},
	author = {Van De Weerd, Lucas},
	year = {2026},
	url = {https://huggingface.co/datasets/Lucasgitguy-co/LvW-bench}
	}
	```