Add pre-packed NVFP4 decoders and update docs

fast-8L/config.json

@@ -13,7 +13,6 @@
     "quantized_layers": "transformer_blocks.*.{attn.to_q,attn.to_k,attn.to_v,attn.to_out.0,ff.net.0.proj,ff.net.2}",
     "high_precision": "proj_in, proj_out, norms, register tokens, post_quant_conv"
   },
-  "stored_dtype_note": "NVFP4 linear weights are stored as bf16 master weights and packed to NVFP4 at load; packing is deterministic, so decoded pixels match training.",
   "compute_dtype": "bfloat16 autocast",
   "host_vae": "any MiniMax-H3 video VAE with >= 8 decoder blocks (e.g. the 26-block light VAE of the FastH3 V2/Trim stacks); the loader keeps 8",
   "tensor_count_by_dtype": {
@@ -21,5 +20,19 @@
     "bfloat16": 96
   },
   "nvfp4_linear_parameters": 537083904,
-  "source_step": 5000
+  "source_step": 5000,
+  "files": {
+    "decoder.safetensors": {
+      "format": "fastvideo_h3_decoder_nvfp4_deploy_v1",
+      "contents": "NVFP4 linears as bf16 master weights, packed to NVFP4 at load; other tensors in their trained dtype",
+      "bytes": 1099886160,
+      "use": "reference weights, re-quantization, further training"
+    },
+    "decoder-nvfp4.safetensors": {
+      "format": "fastvideo_h3_decoder_nvfp4_packed_v1",
+      "contents": "pre-packed NVFP4: per linear packed_weight (uint8, two E2M1 codes per byte), weight_inv_scale (E4M3 per-16 block scales, FlashInfer SfLayout.layout_128x4 swizzle), weight_global_sf (fp32), bias (bf16), input_amax (fp32); other tensors in their trained dtype (fp32)",
+      "bytes": 328147232,
+      "use": "smallest download and lowest load memory; Blackwell only; decodes bit-identically to decoder.safetensors"
+    }
+  }
 }