together-fine-tuning
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
More from zainhas/skills
together-audio
Use this skill for Together AI audio workflows: text-to-speech over REST, streaming, or realtime WebSocket APIs, plus speech-to-text transcription, translation, diarization, timestamps, and live transcription. Reach for it whenever the user needs audio in or audio out on Together AI rather than generic chat generation, image or video creation, or model training.
1together-images
Use this skill for Together AI image workflows: text-to-image generation, image editing with Kontext, FLUX model selection, LoRA-based styling, reference-image guidance, and local image downloads. Reach for it whenever the user wants to generate or edit images on Together AI rather than create videos or build text-only chat applications.
1together-video
Use this skill for Together AI video workflows: text-to-video generation, image-to-video with keyframe control, model and dimension selection, polling asynchronous jobs, and downloading completed videos. Reach for it whenever the user wants motion generation on Together AI rather than still-image generation or text-only inference.
1together-embeddings
Use this skill for Together AI embedding, retrieval, and reranking workflows: generating dense vectors, building semantic search or RAG pipelines, and using rerank models behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text generation.
1together-gpu-clusters
Use this skill for Together AI GPU clusters and raw infrastructure workflows: provisioning on-demand or reserved clusters, choosing Kubernetes vs Slurm, attaching shared storage, scaling, getting credentials, and operating cluster-backed ML or HPC jobs. Reach for it when the user needs multi-node compute or infrastructure control rather than a managed model endpoint.
1together-batch-inference
Use this skill for Together AI Batch API workflows: preparing JSONL inputs, uploading batch files, creating asynchronous jobs, polling status, downloading outputs, and optimizing large offline inference runs for lower cost. Reach for it whenever the user needs high-volume, non-interactive inference rather than real-time chat or evaluation jobs.
1