Hermes Function Calling v1 — LLM Agentic & Function Calling Dataset
The structured-output and function-calling compilation used to train the Hermes 2 Pro series: ~12k ShareGPT-format samples across five task types — single-turn and multi-turn function calls, agentic JSON mode, single-turn JSON mode, and structured extraction. Includes a cleaned 5k Glaive subset. The go-to set when you need reliable JSON output as well as tool calls.
Dataset Details
| Provider | Nous Research |
| Category | Agentic & Function Calling |
| Size | 12k Samples |
| License | Apache 2.0 |
| Downloads | n/a |
| Tags | Function-Calling, JSON-Mode, Structured-Output, Agents, 2024 |
from datasets import load_dataset
ds = load_dataset("NousResearch/hermes-function-calling-v1")
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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 Hermes Function Calling v1 commercially?
Yes — Hermes Function Calling v1 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 Hermes Function Calling v1 contain, and do I need all of it?
Hermes Function Calling v1 contains 12k Samples. 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 Hermes Function Calling v1 best used for?
Models that must return valid structured JSON — for agents, extraction, and tool pipelines. It belongs to the Agentic & Function Calling section of our dataset hub, where you'll find alternatives and complementary sets.
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