# Together Core Workflow A

> 'Together AI core workflow a for inference, fine-tuning, and model deployment. Use when working with Together AI''s OpenAI-compatible API. Trigger: "together core workflow a". '

- Skill: `jeremylongshore/together-core-workflow-a` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jeremylongshore/together-core-workflow-a`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jeremylongshore/together-core-workflow-a/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jeremylongshore (https://skillmd.com/u/jeremylongshore)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/jeremylongshore/together-core-workflow-a

---


# Together AI Core Workflow A

## Overview

Fine-tune open-source models on your data with Together AI's fine-tuning API.

## Instructions

### Step 1: Prepare Training Data (JSONL)

```python
import json

# Format: one JSON object per line with messages array
training_data = [
    {"messages": [
        {"role": "system", "content": "You are a customer support agent."},
        {"role": "user", "content": "How do I reset my password?"},
        {"role": "assistant", "content": "Go to Settings > Security > Reset Password."},
    ]},
    {"messages": [
        {"role": "user", "content": "What are your business hours?"},
        {"role": "assistant", "content": "We're open Monday-Friday, 9 AM - 5 PM EST."},
    ]},
]

with open("training.jsonl", "w") as f:
    for item in training_data:
        f.write(json.dumps(item) + "\n")
```

### Step 2: Upload Training File

```python
from together import Together
client = Together()

# Upload file
file = client.files.upload(file="training.jsonl")
print(f"File ID: {file.id}")
```

### Step 3: Create Fine-Tuning Job

```python
job = client.fine_tuning.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="my-support-bot",
)
print(f"Job ID: {job.id}, Status: {job.status}")
```

### Step 4: Monitor Training

```python
import time

while True:
    status = client.fine_tuning.retrieve(job.id)
    print(f"Status: {status.status}, Step: {status.training_steps_completed}")
    if status.status in ("completed", "failed", "cancelled"):
        break
    time.sleep(30)

if status.status == "completed":
    print(f"Fine-tuned model: {status.fine_tuned_model}")
```

### Step 5: Use Fine-Tuned Model

```python
response = client.chat.completions.create(
    model=status.fine_tuned_model,  # Your custom model ID
    messages=[{"role": "user", "content": "How do I cancel my subscription?"}],
)
print(response.choices[0].message.content)
```

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Invalid JSONL | Wrong format | Each line must be valid JSON with messages array |
| Training OOM | Batch size too large | Reduce batch_size |
| Job failed | Data quality issue | Check training file format |

## Resources

- [Fine-Tuning Guide](https://docs.together.ai/docs/fine-tuning-quickstart)
- [Supported Models](https://docs.together.ai/docs/fine-tuning-models)
- [Fine-Tuning Python](https://docs.together.ai/docs/fine-tuning-python)

## Next Steps

For batch inference and dedicated endpoints, see `together-core-workflow-b`.

---

**Source:** [`jeremylongshore/claude-code-plugins-plus-skills`](https://github.com/jeremylongshore/claude-code-plugins-plus-skills) → `plugins/saas-packs/together-pack/skills/together-core-workflow-a/SKILL.md`