Data correction (#1)

- Data correction (9e282aaa73ae5e946b19df5b84f0efb23c0aa52e)


Co-authored-by: Tao Wei <TWei-flm@users.noreply.huggingface.co>

README.md

@@ -216,14 +216,16 @@ We report the following numbers with 1K prefill and 100 decode tokens:
 
 | Device                                               | Inference | Framework        | Model                | Prefill (tok/s) | Decode (tok/s) | Memory |
 | ---------------------------------------------------- | --------- | ---------------- | -------------------- | --------------- | -------------- | ------ |
-| AMD Ryzen AI 395+                                    | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1373            | 60             | 1700MB |
-| AMD Ryzen AI 9 HX 370                                | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1256            | 57             | 1700MB |
+| AMD Ryzen AI 395+                                    | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1487            | 60             | 1600MB (full context) |
+| AMD Ryzen AI 9 HX 370                                | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1487            | 57             | 1600MB (full context) |
 | AMD Ryzen AI 9 HX 370                                | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 2975            | 116            | 856MB  |
 | Qualcomm Snapdragon® X Elite                         | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 2591            | 63             | 0.9GB  |
 | Qualcomm Snapdragon® Gen4 (ROG Phone9 Pro)           | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 4391            | 82             | 0.9GB  |
 | Qualcomm Dragonwing IQ9 (IQ-9075) (IoT)              | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 2143            | 53             | 0.9 GB |
 | Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 335             | 70             | 719MB  |
 
+**LFM2.5-1.2B-Thinking excels at long-context inference.** For example, on AMD Ryzen™ NPUs with FastFlowLM, decoding throughput sustains ~52 tok/s at 16K context and ~46 tok/s even at the full 32K context, indicating robust long-context scalability. For more details on longer context benchmarks on AMD Ryzen™ NPUs with FastFlowLM, please review these [here](https://fastflowlm.com/docs/benchmarks/lfm2_results/).
+
 These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.
 
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