Together Core Workflow B

Together AI — Fine-Tuning & Model Management

Overview

Create fine-tuning jobs, monitor training runs, and deploy custom models on Together AI's infrastructure. Use this workflow when you need to customize an open-source model on your own data, track training metrics, manage model versions, or set up dedicated inference endpoints for production. This is the secondary workflow — for basic inference and chat completions, see together-core-workflow-a.

Instructions

Step 1: Upload Training Data and Create a Fine-Tune Job

import Together from 'together-ai';
const client = new Together({ apiKey: process.env.TOGETHER_API_KEY });

const file = await client.files.upload({
  file: fs.createReadStream('training.jsonl'),
  purpose: 'fine-tune',
});

const job = await client.fineTuning.create({
  training_file: file.id,
  model: 'meta-llama/Llama-3.3-70B-Instruct-Turbo',
  n_epochs: 3,
  learning_rate: 1e-5,
  batch_size: 4,
  suffix: 'support-agent-v2',
});
console.log(`Fine-tune job ${job.id} — status: ${job.status}`);

Step 2: Monitor Training Progress

let status = await client.fineTuning.retrieve(job.id);
while (!['completed', 'failed', 'cancelled'].includes(status.status)) {
  console.log(`Status: ${status.status} — ${status.training_steps_completed}/${status.total_steps} steps`);
  if (status.metrics) console.log(`  Loss: ${status.metrics.training_loss.toFixed(4)}`);
  await new Promise(r => setTimeout(r, 30_000));
  status = await client.fineTuning.retrieve(job.id);
}
console.log(`Final model: ${status.fine_tuned_model}`);

Step 3: List and Manage Model Versions

const models = await client.models.list({ owned_by: 'me' });
models.data.forEach(m =>
  console.log(`${m.id} — created ${m.created_at}, type: ${m.type}`)
);

// Delete an old model version
await client.models.delete('my-org/support-agent-v1');
console.log('Deleted old model version');

Step 4: Deploy to a Dedicated Endpoint

const endpoint = await client.endpoints.create({
  model: status.fine_tuned_model,
  instance_type: 'gpu-a100-80gb',
  min_replicas: 1,
  max_replicas: 3,
  autoscale_target_utilization: 0.7,
});
console.log(`Endpoint ${endpoint.id} — URL: ${endpoint.url}`);
console.log(`Status: ${endpoint.status}, replicas: ${endpoint.current_replicas}`);

Error Handling

Output

A successful workflow uploads training data, monitors a fine-tuning job to completion, and deploys the custom model to an autoscaling dedicated endpoint for production.

Resources

• Together AI Docs
• Fine-Tuning Guide
• API Reference

Next Steps

See together-sdk-patterns for client initialization and batch inference helpers.