Agents: this page is also Markdown; every page is in llms.txt.

Export your model

Everything Facet builds for you is yours. Export a Task to keep a copy, run it on your own hardware, or move it elsewhere. Exporting is free.

Export

In the web app: open the Task's Settings, and under Export and archive choose Download. You get a zip of the Task's live version.
From your agent: export_task returns the same Instructions, Expectations and examples as data. The trained files come only in the zip.

What's inside

File	What it is
`README.md`	The base model and the exact revision (a fixed snapshot of it) this version was built on
`adapter/`	The part Facet trained for your Task: a LoRA adapter, the standard small add-on to a base model, in the PEFT format most tools read. Absent if the version only changed Instructions
`instructions.json`	The system prompt, example answers and output format Facet sends with every call
`expectations.md`	Each Expectation, how it's checked and its bar
`examples/`	The examples each version learned from, one JSON object per line; file N matches Expectation N

Hidden examples are not included: they stay in Facet, so its checks stay honest.

Run it yourself

The adapter loads on its base model with any tool that reads PEFT adapters. With vLLM, an open source server, on a machine with a GPU:

vllm serve <base model from README.md> --revision <revision from README.md> \
  --enable-lora --lora-modules support-triage=./adapter

Then call it as support-triage with any OpenAI SDK, and send the system prompt from instructions.json as the first message: Facet adds it to every call for you, but a server you run won't.

With Hugging Face Transformers:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

name, rev = "<base model from README.md>", "<revision from README.md>"
base = AutoModelForCausalLM.from_pretrained(name, revision=rev)
model = PeftModel.from_pretrained(base, "adapter")
tokenizer = AutoTokenizer.from_pretrained(name, revision=rev)

system = "<the system prompt from instructions.json>"
messages = [{"role": "system", "content": system}, {"role": "user", "content": "I was charged twice"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
print(tokenizer.decode(model.generate(inputs, max_new_tokens=200)[0][inputs.shape[1]:], skip_special_tokens=True))

Use the base model and revision named in README.md; an adapter only works on the base it was built on.

Next steps

Base models: what each base is and its license
Privacy and data: what Facet keeps and what deleting a Task removes