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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.

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Upload your documents

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

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Fine-tune a language model on your documents

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. AI Fine Tune Creation 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. AI Fine Tune List

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Chat with your new model

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.
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)