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".
Best use case
together-core-workflow-a is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using together-core-workflow-a should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/together-core-workflow-a/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How together-core-workflow-a Compares
| Feature / Agent | together-core-workflow-a | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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`.Related Skills
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