---
license: mit
language:
- en
tags:
- tool-use
- function-calling
- benchmark
- quantization
- evaluation
pretty_name: QuantCall Evaluation Suite
dataset_info:
  features:
  - name: id
    dtype: string
  - name: tier
    dtype: string
  - name: category
    dtype: string
  - name: query
    dtype: string
  - name: tools
    sequence: string
  - name: ground_truth_calls
    sequence: string
  - name: expects_call
    dtype: bool
  splits:
  - name: smoke_v1
    num_examples: 10
---

# QuantCall Evaluation Suite

Deterministic, versioned evaluation samples used by the
[QuantCall benchmark](https://github.com/Happynood/quant-toolcall-bench)
to measure how quantization degrades LLM function-calling reliability.

## Contents

| File | Description |
|------|-------------|
| `data/smoke_v1.jsonl` | T0 smoke tier — 10 hand-crafted instances, always available without a GPU |
| `data/schemas/tool_schemas.json` | Extracted JSON Schemas for all tools in `smoke_v1` |

## Format

Each instance in `smoke_v1.jsonl` is one JSON object per line:

```json
{
  "id": "T0-001",
  "tier": "T0",
  "category": "simple",
  "query": "What is the weather like in Paris?",
  "tools": [{"name": "get_weather", "description": "...", "json_schema": {...}}],
  "ground_truth_calls": [{"name": "get_weather", "arguments": {"city": "Paris"}}],
  "expects_call": true
}
```

## Versioning

Files are version-pinned (`smoke_v1`, `smoke_v2`, …). Never overwrite a pinned
version; add new versions when the evaluation set changes. This ensures all
published results remain reproducible against the exact sample they were run on.

## Links

- GitHub: https://github.com/Happynood/quant-toolcall-bench
- Results dataset: https://huggingface.co/datasets/Happynood/quantcall-results
- Leaderboard: https://huggingface.co/spaces/Happynood/quantcall-leaderboard