Update README.md

README.md

@@ -67,6 +67,21 @@ print(tokenizer.decode(outputs[0]))
 
 For local inference, you can use `llama.cpp`, `ONNX`, `MLX` and `MLC`. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23).
 
+### Long context processing
+
+The current `config.json` is set for context length up to 65,536 tokens. To handle longer inputs (128k or 256k), we utilize YaRN you can change the `max_position_embeddings` and rope_scaling` to:
+```
+{
+  ...,
+  "rope_scaling": {
+    "factor": 2.0, #2x65536=131 072 
+    "original_max_position_embeddings": 65536,
+    "type": "yarn"
+  }
+}
+```
+
+
 ## Evaluation
 
 In this section, we report the evaluation results of SmolLM3 model. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them.