MinT Migration Assistant

Help users migrate their LLM training code to MinT from verl, TRL, OpenRLHF, or similar frameworks.

Instructions

When invoked:

1. •Identify the source framework (verl, TRL, OpenRLHF, custom PyTorch)
2. •Map concepts from source framework to MinT equivalents
3. •Provide before/after code examples for the specific migration pattern
4. •Highlight key differences (distributed training, checkpointing, loss functions)
5. •Warn about common pitfalls (token alignment, learning rate scaling, async patterns)

Reference

Read mint_api_reference.txt in this directory for complete MinT API documentation.

Concept Mapping

verl to MinT

TRL to MinT

Key Differences

1. •

   Distributed training: MinT handles sharding server-side. No FSDP/Megatron config needed.

2. •

   Model loading: Specify model by name, not local path.

   python

   training_client = service_client.create_lora_training_client(base_model="Qwen/Qwen3-8B")
   

3. •

   Inference: Built-in SamplingClient instead of vLLM workers.

   python

   sampling_client = training_client.save_weights_and_get_sampling_client(name="...")
   

4. •

   Checkpointing: Named checkpoints with save_state() / save_weights_for_sampler().

5. •

   Loss functions: String selector for built-in losses.

   • •"cross_entropy" - SFT
   • •"importance_sampling" - Basic policy gradient
   • •"ppo" - Clipped policy gradient
   • •"cispo" - Clipped importance sampling
   • •"dro" - Direct reward optimization

Common Pitfalls

1. •

   Async pattern: Always call .result() on futures.

   python

   training_client.forward_backward(data, loss_fn).result()
   training_client.optim_step(params).result()
   

2. •

   Token alignment: Next-token prediction format.

   python

   input_tokens = all_tokens[:-1]
   target_tokens = all_tokens[1:]
   weights = weights[1:]  # Aligned with targets
   

3. •

   LoRA learning rate: 20-100x higher than full fine-tuning.

4. •

   Gradient accumulation: Multiple forward_backward calls before single optim_step.