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license: apache-2.0
base_model: genmo/mochi-1-preview
library_name: mlx
pipeline_tag: text-to-video
tags:
- mlx
- text-to-video
- mochi
- apple-silicon
---
# Mochi 1 — MLX (SceneWorks)
Apple-Silicon (MLX) repackage of [`genmo/mochi-1-preview`](https://huggingface.co/genmo/mochi-1-preview)
for the SceneWorks native MLX runtime (`mlx-gen-mochi`). The AsymmDiT transformer ships as three
**self-contained, pre-quantized tiers**; the T5-XXL text encoder and AsymmVAE are **shared** across
tiers (hosted once as siblings, not duplicated per tier).
## Layout
```
q4/ transformer/model.safetensors + split_model.json + quantize_config.json (~9 GB)
q8/ transformer/model.safetensors + split_model.json + quantize_config.json (~12 GB)
bf16/ transformer/model.safetensors + split_model.json (~19 GB)
text_encoder/ T5-XXL, bf16 (2-shard) + model.safetensors.index.json (~9.5 GB)
vae/ AsymmVAE decoder, bf16 (~0.9 GB)
tokenizer/ T5 SentencePiece tokenizer
```
To load a tier, point the loader at the tier dir (e.g. `q4/`); the shared `text_encoder/`, `vae/`,
and `tokenizer/` resolve from the parent as siblings of the tier subdirs.
## Tiers
| Tier | Transformer precision | Approx. size |
|------|-----------------------|--------------|
| `q4` | 4-bit affine (group 64), 573 quantized Linears | ~9 GB |
| `q8` | 8-bit affine (group 64), 573 quantized Linears | ~12 GB |
| `bf16` | dense bf16 | ~19 GB |
Each tier's `split_model.json` records the quant selection (`quantized`, `quantization_bits`,
`quantization_group_size`); a tier dir *is* the quant selection (no on-the-fly requantization).
## Shared components
- **`text_encoder/`** — T5-XXL (`google/t5-v1_1-xxl`, 24-layer encoder, `d_model=4096`) hosted in
**bf16** (2-shard set + a matching `model.safetensors.index.json`), bit-identical to
round-to-bf16 of the upstream fp32 weights (the precision the reference pipeline loads at).
- **`vae/`** — `AutoencoderKLMochi` decoder in bf16.
- **`tokenizer/`** — the T5 SentencePiece tokenizer.
## Source & license
Repackaged from [`genmo/mochi-1-preview`](https://huggingface.co/genmo/mochi-1-preview). Licensed
**Apache-2.0**, matching the upstream Mochi 1 release. Weights are unchanged apart from the per-tier
quantization of the transformer and the dtype/shard repackaging described above.
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