Glaive Function Calling v2 — LLM Agentic & Function Calling Dataset
The standard open function-calling SFT dataset: ~113k conversations (112,960 rows) where a system prompt defines available JSON function schemas and the assistant learns when to call them, when to answer directly, and how to handle function responses. The foundation that countless open tool-calling fine-tunes (and cleaned derivatives like Hermes) build on.
Dataset Details
| Provider | Glaive AI |
| Category | Agentic & Function Calling |
| Size | 113k Rows |
| License | Apache 2.0 |
| Downloads | n/a |
| Tags | Function-Calling, Tool-Use, JSON, Agents |
from datasets import load_dataset
ds = load_dataset("glaiveai/glaive-function-calling-v2")
Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
| 7B QLoRA | ~6GB VRAM |
| 13B QLoRA | ~10GB VRAM |
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Frequently asked questions
Can I use Glaive Function Calling v2 commercially?
Yes — Glaive Function Calling v2 is released under Apache 2.0, a permissive license that allows commercial use, including training models you ship in a product. Check the dataset card for attribution requirements before release.
How much data does Glaive Function Calling v2 contain, and do I need all of it?
Glaive Function Calling v2 contains 113k Rows. You rarely need all of it: for style and format fine-tuning, a few hundred to a few thousand examples are enough — load a slice (e.g. split="train[:1000]") and scale up only if quality plateaus.
What is Glaive Function Calling v2 best used for?
Teaching a model basic function-calling: when to call, what JSON to emit, how to use results. It belongs to the Agentic & Function Calling section of our dataset hub, where you'll find alternatives and complementary sets.
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