# Together AI Fine-Tuning Skill for Claude Code

> Skill that teaches Claude Code Together AI's fine-tuning API. Covers LoRA, full fine-tuning, DPO preference tuning, VLM training, and function-calling fine-tuning.

## Install

Save the content below to `.claude/skills/` or append to your `CLAUDE.md`:

## Quick Use

```bash
npx skills add togethercomputer/skills
```

## 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

```python
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)

```json
{"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

```python
# 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.

## Source & Thanks

> Part of [togethercomputer/skills](https://github.com/togethercomputer/skills) — MIT licensed.

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## Quick Start

```bash
npx skills add togethercomputer/skills
```

## What Is This Skill?

Teaches AI agents how to fine-tune models on Together AI, including LoRA, full fine-tuning, DPO, VLM training, and function-calling fine-tuning.

**In one sentence**: Together AI fine-tuning Skill — five methods (LoRA/full/DPO/VLM/function-calling), data formats and hyperparameters. Official.

## Source & Thanks

> [togethercomputer/skills](https://github.com/togethercomputer/skills) — MIT

---
Source: https://tokrepo.com/en/workflows/together-ai-fine-tuning-skill-claude-code-9f339e9b
Author: Together AI