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---
license: mit
pretty_name: ReliabilityLoop v1
task_categories:
- text-generation
- other
language:
- en
tags:
- llm
- reliability
- benchmarking
- json
- sql
- code-generation
- evaluation
configs:
- config_name: viewer
data_files:
- split: train
path: reliability_v1_60_viewer.jsonl
---
# ReliabilityLoop v1
ReliabilityLoop v1 is a small, executable benchmark for local LLM reliability
across three production-style task types:
- `json`: schema-constrained structured extraction
- `sql`: text-to-SQL validated by SQLite execution
- `codestub`: Python function generation validated by unit tests
This dataset is designed for **verifier-based evaluation**: outputs must
*work*, not just look plausible.
## Files
- `reliability_v1_60.jsonl`
Canonical split with 60 tasks:
- 20 JSON tasks
- 20 SQL tasks
- 20 Code tasks
- `RELIABILITY_V1_SPEC.md`
Benchmark protocol and metric definitions.
## Primary Metric
- `policy_ok_rate` = `passed_tasks / total_tasks`
A task is counted as passed only if its verifier succeeds:
- JSON: parse + schema + expected field checks
- SQL: executes and matches expected columns/rows
- Code: function compiles and passes tests
## Recommended Evaluation Command
Using the ReliabilityLoop framework:
```bash
reliabilityloop reliability \
--backend ollama \
--model qwen2.5-coder:0.5b \
--prompts-file eval/reliability_v1_60.jsonl \
--limit 60 \
--max-tokens 96 \
--policy-json contract_first \
--policy-sql baseline_first \
--policy-code baseline_first
Outputs:
- summary.json
- leaderboard.md
- samples.jsonl
- wins.jsonl
## Reproducibility Notes
- Keep config fixed when comparing models:
- prompt file
- temperature
- token budgets
- policy mode
- Report raw samples.jsonl with summary metrics.
- If using memory/retrieval, ensure no leakage from evaluation prompts unless
explicitly evaluating memory-assisted mode.
## Anti-Leakage / Fairness Policy
For base benchmark comparisons:
- Use --memory-file disabled (or disjoint memory source).
- Evaluate all models on the same task file and settings.
- Publish command + config + artifacts.
## Intended Use
- Compare reliability of local LLMs under executable checks.
- Evaluate runtime strategies (policy routing, adaptive compute, memory
reuse).
- Build transparent, reproducible reliability leaderboards.
## Limitations
- Small benchmark (60 tasks), alpha-quality split.
- Not a replacement for broad general benchmarks.
- Current coverage focuses on structured and executable reliability, not open-
ended reasoning.
## Links
- Framework repo: https://github.com/ranausmanai/reliabilityloop
- Dataset: https://huggingface.co/datasets/ranausmans/reliabilityloop-v1 |