Practical overview
When it fits
Use it as a research starting point when you want to examine or prototype how a language model can learn to call functions and incorporate their responses.
Who it is for
- Researchers exploring language-model tool use.
- Developers prototyping training flows for model-generated function calls.
Documented uses
- Prompt a model to insert calls to a provided function into text.
- Execute recognized calls and use their outputs in example training flows.
- Filter token sequences according to the effect of API responses.
- Fine-tune a model on filtered tool-use examples.
Requirements & limits
The public GitHub repository specifies an MIT license. The README documents installation with pip install toolformer-pytorch.
- The repository explicitly describes itself as a work in progress.
- The README demonstrates a PaLM example and allows other PyTorch modules with the stated logits shape.
- The documented training flow includes sampling, executing supplied functions, filtering and fine-tuning.
- The TODO list identifies unfinished areas including error handling, tracking training statistics and multi-tool invocation.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Toolformer-Pytorch and prepare a calendar tool-use example
Install the package and configure the README’s Calendar example for tool-use fine-tuning and sampling.
Before you start
- Python environment with PyTorch available; the example imports
torch. - A model that returns logits shaped
(batch, seq, num_tokens); the README’s example usesPaLMand calls.cuda(). - A tool function returning a string and example text inputs.
Steps
Install the package with the command below.
pip install toolformer-pytorchDefine a
Calendar()function that returns the current date as a string, and write a teaching prompt that shows the model where to insert[Calendar()]calls.Create a
PaLMmodel and pass it, the prompt,tool_id='Calendar', theCalendarfunction, andfinetune=TruetoToolformer.Pass the example text list to the
Toolformerinstance; after the README’s statedfinetune completemessage, callsample_model_with_api_calls()with a question.
Expected outcome
The example is set up to sample tool-call insertions, execute the Calendar function on selected calls, filter results, fine-tune, and then sample a response with API calls.
Guide limitations
- The repository describes itself as a work in progress.
- The README says the example should invoke the calendar, but does not establish that it succeeds in a particular environment.
- The example calls
.cuda(); no hardware or runtime compatibility details are specified.
Invoke registered functions in generated text
Use `invoke_tools` to execute recognized function calls embedded in a string and replace them with their results.
Before you start
- A function registry mapping function names to callable functions.
- Text containing calls in the bracketed form shown in the README.
Steps
Define the functions to expose, such as
inc(i)anddec(i).Create a registry mapping their names to those functions.
Pass the registry and text containing bracketed calls to
invoke_tools.
Expected outcome
The README example replaces recognized calls such as `[inc(1)]` and `[dec(2)]` with their results, while leaving an unregistered call such as `[ignored(3)]` unchanged.
Guide limitations
- The README lists error handling for invalid function names, parameters, execution, and output as unfinished work.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- lucidrains/toolformer-pytorch · Official homepage title · retained in its source language