Can I monitor my training loss through this skill?

Yes, the skill provides implementation patterns for retrieving real-time training metrics, including loss and step completion status.

Does it support autoscaling for custom models?

Yes, Step 4 of the workflow includes logic for deploying models to dedicated endpoints with configurable min/max replicas and utilization targets.

Can I use this for Llama 3 models?

Yes, this skill supports fine-tuning for Together AI's supported models, including the Llama 3 and 3.3 series.

Does it handle training data preparation?

The skill focuses on uploading existing JSONL files and managing the API side of the training process; users should ensure their data is correctly formatted before starting the workflow.

What is the difference between workflow-a and workflow-b?

Workflow-a handles basic inference and chat completions, while workflow-b is dedicated to the advanced lifecycle of fine-tuning, training management, and dedicated endpoint deployment.

Together AI Fine-Tuning & Model Management

Name: Together AI Fine-Tuning & Model Management
Author: jeremylongshore

byjeremylongshore

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2,083

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Data Science & ML

Streamlines the fine-tuning of open-source models, training monitoring, and production deployment on Together AI infrastructure.

Together AI Core Workflow B is a specialized skill for Claude Code designed to manage the end-to-end lifecycle of custom AI models. It automates complex tasks such as uploading JSONL training datasets, initiating fine-tuning jobs for models like Llama 3.3, monitoring loss metrics in real-time, and deploying resulting weights to dedicated, autoscaling GPU endpoints. This skill is essential for developers who need to move beyond generic inference and optimize open-source models for specific domain tasks or proprietary datasets using Together AI's OpenAI-compatible API.

Key Features

01Real-time monitoring of training metrics and loss progress

02Automated training data upload and validation for fine-tuning

03Fine-tuning job creation for Llama 3 and other open-source models

04Model versioning and lifecycle management (list/delete versions)

052,083 GitHub stars

06Deployment to dedicated autoscaling GPU endpoints for production

Use Cases

01Managing multiple fine-tuned model versions for A/B testing

02Automating the deployment of private model endpoints for high-throughput environments

03Customizing Llama models for industry-specific customer support agents

Key Features

01Real-time monitoring of training metrics and loss progress

02Automated training data upload and validation for fine-tuning

03Fine-tuning job creation for Llama 3 and other open-source models

04Model versioning and lifecycle management (list/delete versions)

052,083 GitHub stars

06Deployment to dedicated autoscaling GPU endpoints for production

Use Cases

01Managing multiple fine-tuned model versions for A/B testing

02Automating the deployment of private model endpoints for high-throughput environments

03Customizing Llama models for industry-specific customer support agents