Fine Tuning

In this tutorial, you'll learn how to:

• Upload documents to Telnyx Storage
• Customize a language model for your unique needs, using those documents
• Chat with this fine-tuned language model in the portal or via API

Like most fine-tuning providers, Telnyx supports the standard .jsonl training file as input.

Unlike most fine-tuning providers, Telnyx will also use AI to generate a training file from your raw documents automatically. This is the workflow covered in this tutorial.

You can upload objects to Telnyx's S3-Compatible storage API using our quickstart or with our drag-and-drop interface in the portal.

Once you've uploaded your documents, you can fine tune on them via API or by navigating to the fine-tuning tab in the portal. Once there you can select a base model to train and the bucket with your training documents.

Behind the scenes, your documents will be automatically converted with AI into a .jsonl training file. Immediately after, we will begin fine-tuning your model using this training file.

This whole process may take several minutes to complete. You can monitor the status of your fine-tuning job in the fine-tuning tab.

Once your model is fine-tuned, you can try it out in the AI Playground in the portal by selecting your model in the dropdown.

You can also use your fine-tuned model via our chat completions API. Here is a Python example.

Note

Make sure you have set the TELNYX_API_KEY environment variable. Also, update the question and model variables in the sample code.

import os

from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("TELNYX_API_KEY"),
    base_url="https://api.telnyx.com/v2/ai",
)

question = "<ADD QUESTION HERE>"
model = "<ADD FINE TUNED MODEL NAME HERE>"
chat_completion = client.chat.completions.create(
  messages=[
    {
        "role": "user",
        "content": question
    }
  ],
  model=model,
  stream=True
)

for chunk in chat_completion:
  if chunk.choices[0].delta.content:
    print(chunk.choices[0].delta.content, end="", flush=True)