---

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

library_name: gguf

tags:

- gguf

- lora

- llama.cpp

- prompt-rewriting

- minimax-h3

- text-to-audio-video

language:

- en

---


MiniMax-H3 T2VA Prompt Rewriter LoRA — GGUF

A GGUF conversion of

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

so the rewriter runs under llama.cpp

against a quantized Qwen3.6-27B instead of the 52 GB bf16 checkpoint.

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

values are unchanged.

Why

The original adapter is a PEFT LoRA for Qwen/Qwen3.6-27B, which means a 52 GB

download before anything can run. Under llama.cpp the same adapter attaches to a

quantized base:

| Base | Download | VRAM with this adapter |

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

| Qwen3.6-27B-Q4_K_M.gguf | 15.7 GB | ~19 GB |

| Qwen3.6-27B-IQ4_XS.gguf | 14.4 GB | ~18 GB |

| Qwen3.6-27B-UD-Q3_K_XL.gguf | 13.5 GB | ~17 GB |

| Qwen3.6-27B-UD-IQ2_M.gguf | 10.1 GB | ~13 GB |

Offloading fewer layers lowers VRAM further at the cost of speed, and llama.cpp

brings ROCm, Metal and CPU along with CUDA.

Base quants come from

unsloth/Qwen3.6-27B-GGUF.

Contents

| File | Size | Notes |

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

| MiniMax-H3-Prompt-Rewriter-LoRA-F16.gguf | 3.48 GB | 992 tensors, rank 256, adapter.lora.alpha = 512 |

| MiniMax-H3-Prompt-Rewriter-LoRA-Q8_0.gguf | 1.85 GB | the same adapter, quantised |

Tensor count and alpha match the source adapter exactly.

Take Q8_0 unless you have a reason not to. It is quantised from the F16 in

this repository rather than converted separately, so tensor names, their order

and every metadata field are identical — only the stored values differ, by a

mean of 0.001 relative across all 992 tensors and 0.004 on the worst one.

Both behave the same where it matters: with either adapter attached the rewrite

comes back in the trained cut-point form, [Shot 2] At 00:06.500, while the

unadapted base model writes plain ranges like (0s-4s) instead.

Use with llama.cpp

llama-cli \
  -m Qwen3.6-27B-Q4_K_M.gguf \
  --lora MiniMax-H3-Prompt-Rewriter-LoRA-Q8_0.gguf \
  -sysf system_prompt.txt \
  -p "resolution: 16:9
duration: 15s
original_prompt: A red fox walks through a snowy forest at dawn." \
  -st -n 1400 --temp 0 -ngl 99 -c 8192 --reasoning off

system_prompt.txt must hold the exact system prompt from

prompt_template.py

in the source repository — the adapter was trained on that wording, and changing

it degrades the rewrite.

--reasoning off matters: it corresponds to the enable_thinking=False that the

reference infer.py passes. Without it the model spends hundreds of tokens

reasoning before starting the rewrite.

Measured on an RTX 5090 with Q4_K_M: 50 tok/s with the adapter, 78 tok/s

without it. That ~35% difference is llama.cpp computing the adapter's matmuls,

which is the simplest confirmation that it is actually applied.

Use in ComfyUI

MiniMax-H3-Prompt-Rewriter-ComfyUI

picks this adapter up automatically when a GGUF base model is selected and

llama-cpp-python is installed. It fetches F16 by default; name

MiniMax-H3-Prompt-Rewriter-LoRA-Q8_0.gguf in the options node's adapter

field to take the smaller one instead and it is downloaded from here.

How it was converted

convert_lora_to_gguf.py from llama.cpp, plus a one-hunk fix to

conversion/qwen.py.

llama.cpp reorders the V heads of Qwen3.5's linear-attention layers. For

in_proj_qkv, in_proj_z, in_proj_a and in_proj_b that reorder lands on the

output dimension and a LoRA passes through untouched. For out_proj it lands on

the input dimension, where LoraTorchTensor.reshape refuses — it cannot

reshape the axis that lives on the A factor.

The fix is to express that one reorder as an index instead of

reshape/permute/reshape. LoraTorchTensor.__getitem__ already routes a

last-axis index onto A, and

(B @ A)[..., p] == B @ A[..., p]

exactly, so nothing is approximated and no full-rank delta is materialised. For

ordinary dense tensors the two forms are bit-identical, so the base-model

conversion path is unaffected.

elif ".out_proj." in name:
    col_perm = self._reorder_v_heads(
        torch.arange(num_v_heads * head_v_dim, dtype=torch.long).unsqueeze(0),
        1, num_k_heads, num_v_per_k, head_v_dim,
    ).squeeze(0)
    data_torch = data_torch[..., col_perm]

Licence

The weights originate from

lightx2v/MiniMax-H3-Prompt-Rewriter-LoRA

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.