[docs] simplify FastH3 model card

README.md

@@ -25,88 +25,44 @@ tags:
 
 # FastVideo-FastH3-4-step-Preview-v1-VSA-Synthetic-Step1900
 
-FastH3 4-Step Preview v1 checkpoint from FastVideo. This variant is the
-`dmd2_sp2_fsdp64_v11_dataonly_mixed_vsa64` synthetic-data DMD2 MiniMax-H3
-student at training step 1900. It generates synchronized video and audio with
-four transformer forwards and uses VSA-H3 at 90% sparsity.
+A FastH3 Preview v1 ablation from
+[FastVideo](https://github.com/hao-ai-lab/FastVideo). It generates synchronized
+video and audio from text with four transformer forwards. This step-1900 model
+was trained with DMD2 on synthetic Base H3 videos and uses VSA-H3 at 90%
+sparsity.
 
-See the complete [FastH3 4-Step Preview v1
-collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3).
+[Blog](https://haoailab.com/blogs/fasth3-preview/) ·
+[Matching LoRA](https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA/tree/main/vsa-synthetic-step1900) ·
+[Recommended checkpoint](https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree) ·
+[FastH3 collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3)
 
-> **VSA requirement:** This checkpoint must be run with FastVideo's Video
-> Sparse Attention (VSA-H3) backend and kernel enabled at 90% sparsity with
-> 64-token tiles. Dense attention is not a supported substitute.
+> This checkpoint requires FastVideo's VSA-H3 attention backend.
 
-## Checkpoint identity
-
-- Training run: `20260825T003627Z-v11-dataonly-sp2-fastgen-fix`
-- Lineage parent: `20260823T191211Z-v11-dataonly-sp2-initial`
-- W&B: [arg07fnt](https://wandb.ai/wlsaidhi/h3-dmd2-vsa/runs/arg07fnt)
-- Step: 1900
-- Transformer content SHA-256: `932493fce3c32de6bb49e0d568ec493bec365a62b0e5a92262ab73a1619f0dda`
-- Checkpoint metadata SHA-256: `d93f3974fa6a916a0989327b8861c0fb9f5c028b33f95e65c45925ea49dcdbfe`
-- FastVideo execution commit: `6ac300b5a7c472e3a139f1cb3ff7a03b58f3e26e`
-- Data commit: `b92be606a439c67006220459df236d18c48fd38c3e11f4771d21fe0d078a3371`
-
-`checkpoint_content.json` records every indexed transformer shard and digest.
-`checkpoint_metadata.json` preserves the source training configuration.
-
-## Contents
-
-This is a self-contained Diffusers modular-pipeline package. The distilled
-bf16 student is under `transformer/`; the text encoder, tokenizer, processor,
-video VAE, audio VAE, and schedulers are unmodified MiniMax-H3 components.
-
-The `transformer_ref` component is not bundled because this student targets
-T2AV. Its modular-index entry remains on `MiniMaxAI/MiniMax-H3`.
-
-## Exact FastVideo inference contract
-
-The student was trained on the explicit ladder `[999, 749, 500, 250]`. These
-are four transformer forwards; do not replace the ladder with an arbitrary
-four-point scheduler grid.
+## Run with FastVideo
 
 ```bash
-export FASTVIDEO_DMD_DENOISING_STEPS=999,749,500,250
+git clone https://github.com/hao-ai-lab/FastVideo.git
+cd FastVideo
+uv venv --python 3.12
+source .venv/bin/activate
+UV_TORCH_BACKEND=cu130 uv pip install -e ".[fasth3]"
+```
 
+```bash
 python examples/inference/basic/basic_fasth3.py \
   --model-path FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-Synthetic-Step1900 \
-  --prompt '<MiniMax-H3 T2AV prompt>' \
-  --steps 5 \
-  --num-gpus 4 \
-  --vsa-sparsity 0.9 \
-  --vsa-tile-size 64 \
-  --vsa-kernel sm100a
+  --prompt "your prompt" \
+  --no-warmup \
+  --repeats 1
 ```
 
-The five scheduler grid points execute the four trained jumps. Use guidance
-scale 1.0 and VSA-H3 at 90% sparsity with 64-token tiles. Compilation is an
-optional runtime optimization; first validate output parity on the pinned
-FastVideo commit.
-
-## Training summary
-
-- Method: data-only DMD2 on synthetic Base-H3 video latents. The student is
-  trained from forward-noised target latents; no data-free carried rollout is
-  mixed into this run.
-- Student grid: `[999, 749, 500, 250]`.
-- Student attention: VSA-H3 at 90% sparsity with 64-token tiles.
-- Global batch size: 64.
-- Student and critic learning rate: `2e-06` and
-  `2e-06`.
-- Precision: fp32 training state and bf16 inference export.
-- Training topology: 64 B200 GPUs, SP=2,
-  HSDP replicate=1 / shard=64.
-- Continuous score-clock shift: `2.4`; warp max: `0.999`.
-
-## Limitations
+The script uses FastVideo's optimized four-GPU defaults and the trained
+four-forward schedule. On systems without the Blackwell kernels, add
+`--vsa-kernel triton --no-fa4`; adjust `--num-gpus` for your setup.
 
-- Intermediate checkpoint at step 1900 of a planned 4000-step run.
-- T2AV only; the reference-conditioned transformer was not distilled here.
-- Quality remains below base MiniMax-H3 on difficult motion, fine structure,
-  and some audio details.
-- Sampling outside the trained ladder or attention geometry is
-  off-distribution.
-- Inherits the MiniMax H3 Community License and base-model limitations.
+## Scope
 
-Indexed student: 688 bf16 tensors / 70,099,502,592 logical bytes.
+This preview supports text-to-audio-video generation. FL2VA and Ref2VA were
+not distilled. Difficult motion, fine detail, and some audio may remain below
+the base MiniMax H3 model. This checkpoint inherits the
+[MiniMax H3 Community License](LICENSE).