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togethercomputer/skills

together-fine-tuning

LoRA, full fine-tuning, DPO preference tuning, VLM training, function-calling tuning, reasoning tuning, and BYOM uploads on Together AI.

First seen Mar 31, 2026

Installation

$ npx skills add togethercomputer/skills --skill together-fine-tuning
View on skills.sh ↗ GitHub repository ↗

Summary

  • LoRA, full fine-tuning, DPO preference tuning, VLM training, function-calling tuning, reasoning tuning, and BYOM uploads on Together AI.
  • Reach for it whenever the user wants to adapt a model on custom data rather than only run inference, evaluate outputs, or host an existing model.

Also in this package

Other skills from togethercomputer/skills · top by installs.

npx skills add togethercomputer/skills
together-chat-completions 93 together-batch-inference 88 together-images 83 together-embeddings 78 together-evaluations 76 together-audio 74 together-dedicated-containers 73 together-video 72 together-sandboxes 71 together-gpu-clusters 70

Browse all from togethercomputer/skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 34
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,265 B
  • docs SUMMARY.md 310 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 77 installs

SKILL.md

Together Fine-Tuning

Overview

Use Together AI fine-tuning when the user needs to adapt a model to their own data or behavior.

Supported workflows in this repo:

  • LoRA fine-tuning
  • full fine-tuning
  • DPO preference tuning
  • VLM fine-tuning
  • function-calling fine-tuning
  • reasoning fine-tuning
  • BYOM upload paths

When This Skill Wins

  • Train a model on custom instruction or conversational data
  • Improve function-calling reliability with supervised examples
  • Train on preferences rather than only demonstrations
  • Fine-tune multimodal or reasoning-oriented models
  • Deploy a fine-tuned output model later through dedicated endpoints

Hand Off To Another Skill

  • Use together-chat-completions for plain inference without training
  • Use together-evaluations to measure a model before or after tuning
  • Use together-dedicated-model-inference to host the resulting tuned model
  • Use together-gpu-clusters only when the user needs raw infrastructure rather than managed tuning

Quick Routing

  • Standard LoRA or full fine-tuning

- Start with [scripts/finetuneworkflow.py](scripts/finetuneworkflow.py) - Read [references/data-formats.md](references/data-formats.md)

  • DPO preference tuning

- Start with [scripts/dpoworkflow.py](scripts/dpoworkflow.py)

  • Function-calling tuning

- Start with [scripts/functioncallingfinetune.py](scripts/functioncallingfinetune.py)

  • Reasoning tuning

- Start with [scripts/reasoningfinetune.py](scripts/reasoningfinetune.py)

  • VLM tuning

- Start with [scripts/vlmfinetune.py](scripts/vlmfinetune.py)

  • Model support and deployment options

- Read [references/supported-models.md](references/supported-models.md) - Read [references/deployment.md](references/deployment.md)

Workflow

  1. Choose the tuning method that matches the desired behavior change.
  2. Validate dataset format before spending tokens on training.
  3. Upload training data and keep the returned file ID.
  4. Create the job with explicit method-specific parameters.
  5. Monitor job state, events, checkpoints, and per-step training metrics before handing off to deployment.

High-Signal Rules

  • Python scripts require the Together v2 SDK (together>=2.0.0). If the user is on an older version, they must upgrade first: uv pip install --upgrade "together>=2.0.0".
  • Prefer LoRA unless the user has a specific reason to pay for full fine-tuning.
  • Keep data-format validation close to the upload step so bad files fail early.
  • Treat deployment as a separate phase; fine-tuning success does not automatically mean serving success.
  • Use the method-specific script instead of overloading one generic workflow for all modes.
  • Parameterize dataset paths, model IDs, and suffixes in automation instead of embedding one demo dataset forever.

Resource Map

  • Data formats: [references/data-formats.md](references/data-formats.md)
  • Supported models: [references/supported-models.md](references/supported-models.md)
  • Deployment guide: [references/deployment.md](references/deployment.md)
  • LoRA or full workflow: [scripts/finetuneworkflow.py](scripts/finetuneworkflow.py)
  • DPO workflow: [scripts/dpoworkflow.py](scripts/dpoworkflow.py)
  • Function-calling workflow: [scripts/functioncallingfinetune.py](scripts/functioncallingfinetune.py)
  • Reasoning workflow: [scripts/reasoningfinetune.py](scripts/reasoningfinetune.py)
  • VLM workflow: [scripts/vlmfinetune.py](scripts/vlmfinetune.py)

Official Docs