Catalog
Every catalog model supports SFT, GRPO, and OPD and hybrid reasoning (“thinking”).Context window and LoRA rank
Each base model is served at a fixed context window (max_model_len) and
accepts LoRA adapters up to a per-model max rank. flash train enforces both
at submit: a config whose max_context_tokens exceeds the served context, whose
GRPO/OPD prompt plus max_completion_tokens cannot fit, or whose lora_rank
exceeds the cap, is rejected.
Serving capacity
Managed serving keeps one engine per base-model tier and loads trained adapters as LoRA slots on that tier. The smaller catalog models and the 4B serve on Modal L4 instances; the 9B and 27B serve on an H100 tier and the 35B on an H200 tier.Serving prices
Serving is billed per token after deployment. Prompt and completion tokens have separate per-model rates, and cached prompt tokens use the model’s cached-input rate. The prices below are per 1M tokens; your Freesolo billing dashboard shows the charges you accrue.
The Cached prompt column is the rate for prompt tokens served from the
automatic prefix cache. See Billing for how
prefix caching works and when it applies.
Reasoning (“thinking”) models
Every catalog model is hybrid-reasoning: it runs with or without an explicit reasoning step. Turn reasoning on with one line in your config:thinking field in the
configuration reference.
Choosing a model
Start small
Use
Qwen/Qwen3.5-0.8B or 2B to validate your setup and data
cheaply. Get a run working before you scale.Scale up for quality
Move to
Qwen/Qwen3.5-4B or 9B once the task is wired up
and you want stronger results. Larger models cost more per run.Pick a model, then train
Set
model in your config and submit a run.