---
title: MiniMax-H3-FP8
canonical_url: "https://www.modelscope.cn/models/unsloth/MiniMax-H3-FP8"
md_url: "https://www.modelscope.cn/models/unsloth/MiniMax-H3-FP8.md"
repository: unsloth/MiniMax-H3-FP8
last_updated: 2026-10-16
license: other
base_model:
  - MiniMaxAI/MiniMax-H3
base_model_relation: quantized
parameters: 152.1B
tensor_type:
  - F32
  - I8
  - BF16
  - F8_E4M3
  - U8
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
downloads: 494
stars: 3
tags:
  - fp8
  - int8
  - quantized
  - text-to-video
  - image-to-video
  - video-generation
  - unsloth
---

# MiniMax-H3-FP8

> MiniMax-H3-FP8 - unsloth 在 ModelScope 开源的模型。MiniMax-H3, FP8 and INT8

unsloth/MiniMax-H3-FP8 是 ModelScope 魔搭社区上的 152.1B 参数机器学习模型，采用 other 许可，基于 MiniMaxAI/MiniMax-H3 构建。

- **Repository**: unsloth/MiniMax-H3-FP8
- **License**: other
- **Parameters**: 152.1B
- **Base model**: MiniMaxAI/MiniMax-H3
- **Tags**: fp8, int8, quantized, text-to-video, image-to-video, video-generation, unsloth
- **Downloads**: 494
- **Stars**: 3
- **Last updated**: 2026-10-16

Source: https://www.modelscope.cn/models/unsloth/MiniMax-H3-FP8

---

# MiniMax-H3, FP8 and INT8

Pre-quantized PyTorch checkpoints of
[MiniMaxAI/MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3), for diffusers plus torchao.

MiniMax H3 is an omni-modal generative system that produces video with native stereo audio, up to
15 seconds at 24 FPS with 32 kHz stereo audio. The files here quantize the `fl2va_pruned`
H3-Base first-and-last-frame variant, which takes zero, one or two input images plus text, and the
`ref2va_pruned` omni-reference variant, which takes a prompt plus up to twelve image, video and
audio references. Both schemes quantize the same 200 main-block matmuls, 95.8% of the parameters,
and leave every 1-D gain, every bias and the whole modulation path bit-identical.

## Examples

Same prompt, same seed, same settings, one clip per checkpoint. 960x544, 124 frames, 24 FPS,
8 steps, guidance 1.0, seed 11, on a single card.

> a red panda stepping along a mossy log in a misty forest, cinematic

| INT8 | FP8 |
|---|---|
| <img src="assets/h3_int8.gif" width="380"> | <img src="assets/h3_fp8.gif" width="380"> |

The GIFs are downsampled and silent. For the full 960x544 clips with their native 32 kHz stereo
audio track, play
[`assets/h3_int8.mp4`](https://huggingface.co/unsloth/MiniMax-H3-FP8/blob/main/assets/h3_int8.mp4)
and
[`assets/h3_fp8.mp4`](https://huggingface.co/unsloth/MiniMax-H3-FP8/blob/main/assets/h3_fp8.mp4).
H3 generates the audio jointly with the video, so the audio is part of the model output rather
than something added afterwards.

Stills from other prompts, three per format:

INT8

<img src="samples/int8_1.png" width="260"> <img src="samples/int8_2.png" width="260"> <img src="samples/int8_3.png" width="260">

FP8

<img src="samples/fp8_1.png" width="260"> <img src="samples/fp8_2.png" width="260"> <img src="samples/fp8_3.png" width="260">

## Files

H3 ships two denoiser partitions and a load opens exactly one. `transformer/` serves the keyframe
workflow `fl2va`, which also covers text-only generation; `transformer_ref/` serves the
omni-reference workflow `ref2va`. They are separate weights, so each partition has its own
checkpoints here and the two sets are not interchangeable.

Keyframe and text-only (`fl2va`, from `transformer/`):

| File | Size |
|---|---|
| `MiniMax-H3-INT8.pt` | 18.86 GiB |
| `MiniMax-H3-INT8-ConvRot.pt` | 18.86 GiB |
| `MiniMax-H3-FP8.pt` | 18.87 GiB |

Omni-reference (`ref2va`, from `transformer_ref/`):

| File | Size |
|---|---|
| `MiniMax-H3-Ref2VA-INT8-ConvRot.pt` | 18.86 GiB |
| `MiniMax-H3-Ref2VA-FP8.pt` | 18.87 GiB |

**Every checkpoint lives here, INT8 included.** One repo on purpose: these are one artifact built
a few ways, and a loader picks between them by filename. Against the 40.23 GB bf16 pruned source
any of them takes the checkpoint to 20.25 GB and the end-to-end render peak from 57.11 GB to
36.97 GB. Prefer INT8 with `torch.compile` and FP8 without it.

Pick the pair that matches the workflow you are running. The two partitions have the same class,
the same config and the same 635-key state dict, so a keyframe checkpoint seeded into the
reference workflow loads cleanly, passes every metadata check and generates from the wrong
weights rather than failing. The `Ref2VA` in the filename is the only thing that distinguishes
them.

The `-ConvRot` INT8 files store their weights in a Hadamard-rotated basis and need a loader that
rotates the activations to match; they carry a format tag that makes an older loader refuse them
rather than read them as plain INT8. Where both an INT8 and an INT8-ConvRot file exist, the
ConvRot one is the newer build.

### Reference (Ref2VA) build recipe

Identical to the keyframe rungs apart from the source file: 313 Linears quantized, 55 skipped, the
`adaLN` modulation path left at full precision, and on the INT8 arm a ConvRot Hadamard rotation at
group size 256 over all 313. The source is
`minimax_h3_ref2va_pruned_bf16.safetensors` from
[Comfy-Org/MiniMax-H3](https://huggingface.co/Comfy-Org/MiniMax-H3), which is the reference
partition with the same curve-form modulation pruning as the keyframe one, and both files record
it in `metadata.base_checkpoint`.

Verified before publishing: each file loads through the pre-quantized path against the
`transformer_ref` config with no missing or unexpected keys, and renders a 640x384, 124-frame
reference clip. Measured against the reference-partition bfloat16 denoiser at the same prompt,
seed and shape (20 steps, two prompts, one image reference), the same composition comes back with
fine detail redistributed: SSIM 0.92 and 0.82 for INT8 and 0.88 and 0.78 for FP8, against a
determinism ceiling of 1.00 for the bfloat16 arm rendered twice. Those numbers are a divergence
measure, not a quality score.

Both are torchao pre-quantized transformer state dicts, per-output-channel absmax scales, INT8
symmetric and FP8 e4m3. The skeleton is built on `meta` and the quantized subclass tensors are
assigned rather than copied, so dense bf16 never touches the GPU. Each file carries
`base_model_id = MiniMaxAI/MiniMax-H3` and a `base_checkpoint` naming the exact source it was cut
from (`.../minimax_h3_fl2va_pruned_bf16.safetensors` or
`.../minimax_h3_ref2va_pruned_bf16.safetensors`) in its metadata, which the loader checks before
accepting it.

The keyframe source is `minimax_h3_fl2va_pruned_bf16.safetensors` from
[Comfy-Org/MiniMax-H3](https://huggingface.co/Comfy-Org/MiniMax-H3), which already has the
modulation pruned to `[96768, 8]` per block plus a shared `adaln_t_table [1025, 8]`. Note that
stable-diffusion.cpp cannot load these, it has no int8 linear path that reads external per-channel
scales, so for sd.cpp use
[unsloth/MiniMax-H3-GGUF](https://huggingface.co/unsloth/MiniMax-H3-GGUF) instead.

## Licence

MiniMax H3 Community License Agreement, from MiniMax-H3. Full text in
[`LICENSE`](https://huggingface.co/unsloth/MiniMax-H3-FP8/blob/main/LICENSE). Read it before use:
it defines an Applicable Territory and excludes some jurisdictions from it. MiniMax also publish a
[Q&A about the licence](https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/QA-about-License.md).

These files are Model Derivatives, not a plain copy: the transformer is quantized and its
modulation is pruned, both of which change the numerics. Section III of the licence wants that
stated, so [`NOTICE`](https://huggingface.co/unsloth/MiniMax-H3-FP8/blob/main/NOTICE) lists every
change along with the attribution. Not an official MiniMax product, and not endorsed by MiniMax.