Fine-Tuning | Together AI

# Fine-tune open-source models for
real production use

Improve accuracy, reduce hallucinations, and control behavior — without managing training infrastructure.

## Why fine-tune models
with Together AI?

Build models that are faster, more accurate,  and fully yours

• Reliable infrastructure at any scale
  Multi-node orchestration that eliminates job failures. Fine-tune 100B+ models (DeepSeek-V3, Qwen3-235B) that break other platforms, with the reliability to experiment rapidly.
• Research-driven performance gains
  ML systems research built into every job. Train with 2-4x longer contexts at no extra cost, advanced DPO variants from SOTA recipes, and continuous optimizations that make your runs faster over time.
• Universal model compatibility
  Fine-tune any open-source model from Hugging Face Hub. No vendor lock-in, no format conversions — seamless integration with your existing workflows.

Fine-tune leading models

Explore top-performing models across text, image, video, code, and voice.

new

Chat

• DeepSeek V4 Pro
• MiniMax M3
• GLM-5.2
• Kimi K3

Video

• ByteDance Seedance 2.0

new

Code

• PrismMLTernary Bonsai 27B
• Inkling

Have your own model?

Deploy custom containers on Together’s managed GPU infrastructure with automatic scaling, job queues, and built-in observability.

Fine-tuning options

Choose how fine-tuned models are trained and hosted based on dataset size, cost, and control.

• LoRA fine-tuning
  Lightweight fine-tuning for fast iteration and lower cost.

• Small to medium datasets

• Fast training & deployment

• Easy to update or roll back

• Full fine-tuning
  Train the entire model for maximum control and quality.

• Large or complex datasets

• Deeper behavior changes

• Dedicated infrastructure

Everything you need to fine-tune at scale

Fine-tune any open-source model on your data. Deploy securely onto scalable infrastructure.

• Large frontier model support
• 100B+ param models
• Multi-GPU training
• Faster training

Fine-tune large open-source models like Kimi-K2 and GLM-4.7 for tool use, reasoning, and agentic tasks. Drive advanced model behavior through a single API without managing underlying training infrastructure.

Advanced model shaping capabilities

For teams pushing models beyond standard fine-tuning

• Speculative decoding
  Accelerate inference with custom speculative decoding, training lightweight draft models to predict multiple tokens
• Quantization
  Apply FP8 and NVFP4 quantization to push the limits of model efficiency, maximizing hardware utilization with minimal quality loss.
• Reinforcement learning
  Leverage PyTorch-based reinforcement learning to shape model policies for reasoning, tool use, and long-horizon agentic behavior.

Production-grade security and data privacy

We take security and compliance seriously, with strict data privacy controls to keep your information protected. Your data and models remain fully under your ownership, safeguarded by robust security measures.