MiniMax-H3 Rewriter Options

Most of the pack's nodes take an options input that accepts this node's single output. That's the whole design: the rewriters, writers and captioners keep a clean front panel, and everything you touch once a month lives here. Leave it unconnected and every node falls back to the decoding parameters the adapter was published with - which is the correct default for almost everyone.

So the honest advice is: don't connect it until you have a reason. When you do, here's what you're getting.

The decoding knobs

• max_new_tokens (2048), temperature (0.7), top_p (0.8), top_k (20), repetition_penalty (1.05) - sampling parameters, used only when greedy is off on the main node. If a writer comes back missing a field, lowering temperature is the first thing to try.
• attn_implementation - sdpa default; eager or flash_attention_2 if you have it. Non-GGUF models only.

The model-management ones

• adapter - which build of the LoRA to apply. The default entry (lightx2v/MiniMax-H3-Prompt-Rewriter-LoRA) means whichever build the model list names for the base you picked - PEFT adapter for a Transformers base, the catalog's GGUF for a GGUF one, the 8B adapter on the 8B node. Below it sit the published precisions: F16 and Q8_0 for each LoRA. The Q8_0 is half the download and rewrites the same - if you're on the GGUF route and never picked this up, that's a free gigabyte or two back. Any .gguf LoRA you drop into models/LLM appears here as on disk:.
• use_lora - on by default. Turn it off to run the plain base model, which is the "what's the LoRA actually doing?" experiment.
• auto_download - fetch missing weights. Turn it off to fail loudly instead of downloading 52 GB by surprise.
• gpu_layers / n_ctx - GGUF only. gpu_layers of -1 is all of them; lower it to fit a smaller card at the cost of speed. n_ctx (8192) is the context llama.cpp allocates - the writer nodes raise it themselves, so this is mostly a rewriter-league knob.
• gguf_runtime - auto uses llama-cpp-python if it's importable and the official llama.cpp binaries otherwise. Force llama.cpp when an installed wheel is broken; force llama-cpp-python to keep the model resident between runs (the binaries can't).
• llama_backend - which official binary build to fetch: auto → Vulkan (34 MB, about half CUDA throughput), cuda (511 MB, ~2× faster, Windows only - upstream publishes no Linux CUDA build), or cpu. An llama.cpp you already have is run as-is and this stops mattering.
• device - auto, cpu, or a cuda:N per GPU. Give the language model a second card and keep_model_loaded becomes worth switching on - nothing has to be evicted to make room. A device the machine doesn't have is refused, not quietly demoted.
• trust_remote_code - off, and should stay off. A Transformers checkpoint can ship its own Python, loaded and run with your user's rights; off means such a model is refused rather than executed. Turn it on only for a model you picked and trust - not because a downloaded workflow asked for it.

Where people get burned

• The adapter field is what a downloaded workflow gets a say in. Two things to know: a network path (\\host\share\...) is refused outright, because merely looking at one is an authentication attempt against that host. And a swapped LoRA is invisible - the node still runs and fills every field, it just writes something else - which is why the applied adapter is logged to the console every run.
• llama-cpp-python CUDA wheels are a trap. Both current ones fail on ordinary consumer hardware (one needs AVX-512 that consumer Intel doesn't have; the other refuses modern RTX cards). The official llama.cpp release archives carry 14 CPU backend variants and native SASS, which is why the same model runs fine as a subprocess where the wheel dies. If you want the in-process backend, the Vulkan wheel is the one that works.
• gguf_runtime and llama_backend answer different questions. One picks what runs the model; the other picks which build to fetch and only applies when the binaries are in use. They're both in this node because they're both GGUF concerns, but they're not the same knob.

It's the least glamorous node in the pack and the one that saves you the most pain once you know it's there.