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README.md

@@ -33,6 +33,24 @@ pipeline_tag: text-generation
 **Edge0-35b-a3b** — a 35B MoE LLM that runs at viable speed on portable devices in under **2.9 GiB of active memory** (1/8 of its 23 GB weight footprint), 
 via the [edge0](https://github.com/Edge0-AI/edge0) streaming inference framework.
 
+> **Preview status:** this is an early preview release of the edge0
+> pipeline. The checkpoint ships as int4 quantization plus LoRA and
+> prerouter adapters trained for this framework.
+
+## Highlights
+
+- **Runs in phone-class memory**: the full 4-bit checkpoint stays on
+  storage and experts are streamed on demand, so only the active
+  weights are in RAM — under **2.9 GiB**, with no sharding and no
+  upfront download of the weights into memory.
+- **Fast enough for interactive use**: 14.9–17.7 tok/s decode on a
+  Mac mini M4 Pro; long prompts fill in at 113–140 tok/s.
+- **Quality kept after quantization**: Recover-LoRA distillation keeps
+  the int4 model within **3.9 points** of its fp16 base.
+- **Works out of the box**: base, LoRA and prerouter adapters ship
+  together and load automatically via `edge0`.
+
+
 Three mechanisms make this work:
 
 - **SSD expert offload**: expert weights are streamed from storage on
@@ -48,10 +66,6 @@ Three mechanisms make this work:
   quantization loss at 4-bit (see Quality below).  Adapters stay
   unmerged: one read-only base serves multiple adapter sets.
 
-> **Preview status:** this is an early preview release of the edge0
-> pipeline. The checkpoint ships as int4 quantization plus LoRA and
-> prerouter adapters trained for this framework.
-
 ## Model summary
 
 | | |
@@ -96,19 +110,6 @@ Measured with `examples/bench.py` on a Mac mini M4 Pro, 24 GB:
 *Short contexts; long contexts add KV cache. Expert weights stream from
 SSD on demand and are not resident.
 
-## Highlights
-
-- **Runs in phone-class memory**: the full 4-bit checkpoint stays on
-  storage and experts are streamed on demand, so only the active
-  weights are in RAM — under **2.9 GiB**, with no sharding and no
-  upfront download of the weights into memory.
-- **Fast enough for interactive use**: 14.9–17.7 tok/s decode on a
-  Mac mini M4 Pro; long prompts fill in at 113–140 tok/s.
-- **Quality kept after quantization**: Recover-LoRA distillation keeps
-  the int4 model within **3.9 points** of its fp16 base.
-- **Works out of the box**: base, LoRA and prerouter adapters ship
-  together and load automatically via `edge0`.
-
 ## Use cases
 
 - Edge / on-device inference where GPU VRAM is scarce and storage is