---

base_model: lightx2v/MiniMax-H3-Prompt-Rewriter-LoRA-Omni

base_model_relation: adapter

library_name: gguf

pipeline_tag: image-text-to-text

tags:

- gguf

- lora

- llama.cpp

- qwen2.5-omni

- multimodal

- prompt-rewriting

- minimax-h3

- text-to-audio-video

- image-to-audio-video

language:

- en

---


MiniMax-H3 Prompt Rewriter LoRA Omni — GGUF

A GGUF conversion of

lightx2v/MiniMax-H3-Prompt-Rewriter-LoRA-Omni,

so the omni-modal rewriter runs under

llama.cpp against a quantized

Qwen2.5-Omni-7B.

This repository contains no new training — only a format conversion. The tensor

values are unchanged.

What is different about this one

It is the first of the three that both sees and hears, and the first that

covers every task in one adapter.

| Adapter | Base | Reads | Tasks |

|---|---|---|---|

| 27B | Qwen3.6-27B | text | T2AV |

| 8B | Qwen3-VL-8B | pictures | T2AV, I2AV, L2AV, FL2AV |

| this one | Qwen2.5-Omni-7B | pictures, clips, sound | all four, plus Ref2AV |

Ref2AV is the difference that matters. It is the full-reference mode — six

output fields instead of three, a retention analysis, and <Subject N>,

<Picture N>, <Video N> and <Audio N> labels the rewrite has to honour.

Until now a reference asset had to be described in words first; this base model

takes the asset itself, sound included.

| Task | References | Meaning |

|---|---|---|

| T2AV | none | text only |

| I2AV | 1 picture | the picture is the first frame |

| L2AV | 1 picture | the picture is the final frame |

| FL2AV | 2 pictures | first and last frame |

| Ref2AV | any mix | pictures, clips and sounds the target video reuses |

The two system prompts that go with these — one for the four frame tasks, one

for Ref2AV — are in

system_prompt.py

in the source repository. The adapter was trained on that exact wording.

Contents

| File | Size | Notes |

|---|---|---|

| MiniMax-H3-Prompt-Rewriter-LoRA-Omni-F16.gguf | 0.65 GB | 392 tensors, rank 128, adapter.lora.alpha = 128 |

| MiniMax-H3-Prompt-Rewriter-LoRA-Omni-Q8_0.gguf | 0.34 GB | the same adapter, quantised |

Tensor count and alpha match the source adapter exactly. The adapter touches the

thinker's text tower only — q/k/v/o and gate/up/down across 28 layers; the

vision and audio towers are untouched, which is why 392 tensors cover the whole

thing.

Take Q8_0 unless you have a reason not to. Names, order and every metadata

field are identical to the F16; only the stored values differ, by a mean of

0.005 relative across all 392 tensors and 0.006 on the worst one.

Which base to run it against

Both files in ggml-org/Qwen2.5-Omni-7B-GGUF,

since a multimodal model is a model plus a projector and the two have to come

from the same conversion:

| File | Size |

|---|---|

| Qwen2.5-Omni-7B-Q4_K_M.gguf | 4.68 GB |

| mmproj-Qwen2.5-Omni-7B-Q8_0.gguf | 1.55 GB |

About 9 GB of VRAM with this adapter attached and a 12k context. The projector

is only needed for tasks that carry references; T2AV runs without it.

Use with llama.cpp

Render the chat template yourself. This is the one thing worth knowing

before anything else. -no-cnv feeds the model raw text with no template at

all, and Qwen2.5-Omni answers that by continuing the prompt: the output loops,

never opens integrated_multimodal_description:, and looks exactly like a

broken adapter. It is not — it is a missing <|im_start|>. Use --jinja, or

render the template and pass the result with --file.

Rendered correctly, T2AV needs no projector:

llama-completion \
  -m Qwen2.5-Omni-7B-Q4_K_M.gguf \
  --lora MiniMax-H3-Prompt-Rewriter-LoRA-Omni-Q8_0.gguf \
  --jinja -sysf system_prompt.txt \
  -p "Rewrite request:
task: T2AV
resolution: 16:9
effective_duration: 10s
raw_prompt: A lone astronaut walks toward a ruined tower on a red desert plain." \
  -st -n 1400 --temp 0 -ngl 99 -c 12288

A task with references goes through llama-mtmd-cli, which applies the template

itself and splices each asset in where its marker sits. Note the user turn's

field names: effective_duration and raw_prompt, not the duration and

original_prompt the 8B adapter was trained on.

Picture 1 — exact first frame at 0.00 seconds:
<__media__>

Rewrite request:
task: I2AV
resolution: 16:9
effective_duration: 10s
raw_prompt: ...

For Ref2AV the block of references opens with Ordered MiniMax-H3 references:

and each asset is introduced by its own label line —

infer.py

in the source repository builds all of it.

Use in ComfyUI

MiniMax-H3-Prompt-Rewriter-ComfyUI

ships a node for this adapter. Pick a Qwen2.5-Omni base, choose the task,

connect the references, and this adapter is fetched from here on first use. The

options node's adapter dropdown lists both precisions.

What to expect of it

The trained shape arrives on the first try. With the adapter off, the same base

model and the same system prompt still print the three field names — and then

fills integrated_multimodal_description with a single sentence, no [Shot N]

markers and no camera language. With it on, the same request produces a shot

list with composition, lighting, camera motion and timing. That contrast is the

simplest way to confirm the adapter is actually attached.

How it was converted

convert_lora_to_gguf.py from llama.cpp, with nothing done to it. All 392

tensors map cleanly onto blk.N.attn_q|attn_k|attn_v|attn_output|ffn_gate|ffn_up|ffn_down.weight.lora_a|lora_b

with no renaming, even though the adapter's own tensor names carry no thinker.

prefix.

One thing does differ from the 8B conversion: --base-model-id cannot be used.

Qwen2.5-Omni's config.json keeps the text parameters under

thinker_config.text_config, and the hub path does not flatten it, so the

converter stops at could not find any of: ['n_layers', 'num_hidden_layers', ...].

Downloading that one 4 KB config into a directory and passing --base works,

because the local path does rename thinker_config for exactly this case. The

base weights are still not needed.

mkdir omni-base
curl -L https://huggingface.co/Qwen/Qwen2.5-Omni-7B/resolve/main/config.json -o omni-base/config.json

python convert_lora_to_gguf.py --base omni-base --outtype f16  lora-omni
python convert_lora_to_gguf.py --base omni-base --outtype q8_0 lora-omni

Both files are separate runs of the converter, not one quantised from the other.

Licence

The weights originate from

lightx2v/MiniMax-H3-Prompt-Rewriter-LoRA-Omni

and were converted without modifying their values. Whatever terms that

repository carries apply here unchanged; this repository claims nothing

additional.

Use of MiniMax-H3 itself is governed by the licence and acceptable-use terms in

the official MiniMax-H3 repository.