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 ```typescript 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 ```typescript 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 = awa...