xLAM Function Calling 60k
60,000 tool-calling samples generated by Salesforce's APIGen pipeline across 3,673 executable APIs in 21 categories. Every sample passed three verification stages — format checking, actual function execution, and semantic verification — with >95% human-audited correctness. The quality benchmark for function-calling data, and the training set of the xLAM action-model family.
from datasets import load_dataset
ds = load_dataset("Salesforce/xlam-function-calling-60k")Preview a sample row
{
"query": "What's the weather in Warsaw this weekend in Celsius?",
"tools": [{ "name": "get_forecast", "description": "Get weather forecast", "parameters": { "city": "str", "unit": "str", "days": "int" } }],
"answers": [{ "name": "get_forecast", "arguments": { "city": "Warsaw", "unit": "celsius", "days": 2 } }]
}Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
Frequently asked questions
Can I use xLAM Function Calling 60k commercially?
Yes — xLAM Function Calling 60k is released under CC BY 4.0, 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 xLAM Function Calling 60k contain, and do I need all of it?
xLAM Function Calling 60k contains 60k 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 xLAM Function Calling 60k best used for?
High-precision tool calling — every sample was verified by actually executing the API call. It belongs to the Agentic & Function Calling section of our dataset hub, where you'll find alternatives and complementary sets.