What is This Skill?
This skill teaches AI coding agents how to fine-tune models on Together AI. It covers LoRA (efficient), full fine-tuning, DPO preference tuning, vision-language model (VLM) training, and function-calling fine-tuning — with correct API calls, data formats, and training parameters.
Answer-Ready: Together AI Fine-Tuning Skill for coding agents. Covers LoRA, full fine-tuning, DPO, VLM training, and function-calling tuning. Correct data formats, hyperparameters, and job management. Part of official 12-skill collection.
Best for: ML engineers fine-tuning open-source models on Together AI. Works with: Claude Code, Cursor, Codex CLI.
What the Agent Learns
LoRA Fine-Tuning
from together import Together
client = Together()
job = client.fine_tuning.create(
training_file="file-abc123",
model="meta-llama/Llama-3.1-8B-Instruct",
n_epochs=3,
learning_rate=1e-5,
lora=True,
lora_r=16,
)
print(f"Job ID: {job.id}")Training Data Format (JSONL)
{"messages": [{"role": "system", "content": "You are helpful."}, {"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello!"}]}Supported Methods
| Method | Use Case | Cost |
|---|---|---|
| LoRA | Most tasks, efficient | Low |
| Full fine-tuning | Maximum quality | High |
| DPO | Preference alignment | Medium |
| VLM training | Vision+language | Medium |
| Function-calling | Tool use training | Low |
Job Management
# Check status
status = client.fine_tuning.retrieve(job.id)
# List jobs
jobs = client.fine_tuning.list()
# Cancel
client.fine_tuning.cancel(job.id)FAQ
Q: Which method should I use? A: Start with LoRA — it is faster, cheaper, and works well for most use cases. Use full fine-tuning only if LoRA quality is insufficient.