Together AI Performance Tuning Guide

v20260423

together-performance-tuning

Provides comprehensive guidance for performance tuning, inference, and fine-tuning using the Together AI platform. This skill supports working with Together AI's OpenAI-compatible API, covering model deployment, utilizing various open-source models (like Llama, Mixtral), and implementing best practices for efficient and cost-effective batch inference.

Together AI Performance Tuning Inference Fine-Tuning LLM OpenAI API Python

Get Skill

299 downloads

Overview

Together AI Performance Tuning

Overview

Guidance for performance tuning with Together AI inference and fine-tuning API.

Instructions

Key Points

Together AI is OpenAI-compatible: base_url = 'https://api.together.xyz/v1'
Use the together Python SDK or any OpenAI client library
Supports 100+ open-source models (Llama, Mixtral, Qwen, FLUX)
Fine-tuning available for supported models
Batch inference at 50% cost reduction

Error Handling

Error	Cause	Solution
`401 Unauthorized`	Invalid API key	Check at api.together.xyz
`Model not found`	Wrong model ID	Use `client.models.list()`
`429 Rate limit`	Too many requests	Implement backoff
`500 Server error`	Model overloaded	Retry with backoff

Resources

Next Steps

See related Together AI skills for more patterns.

Info

Category Artificial Intelligence

Name together-performance-tuning

Version v20260423

Size 1.46KB

Source jeremylongshore/claude-code-plugins-plus-skills

Updated At 2026-04-28