diff --git a/docs/source/llm/run-on-ios.md b/docs/source/llm/run-on-ios.md
index 88ad94c38d3..f096995fca9 100644
--- a/docs/source/llm/run-on-ios.md
+++ b/docs/source/llm/run-on-ios.md
@@ -80,17 +80,22 @@ do {
 
 #### Generating
 
-Generate up to a given number of tokens from an initial prompt. The callback block is invoked once per token as it’s produced.
+Generate tokens from an initial prompt, configured with an `ExecuTorchLLMConfig` object. The callback block is invoked once per token as it’s produced.
 
 Objective-C:
 ```objectivec
+ExecuTorchLLMConfig *config = [[ExecuTorchLLMConfig alloc] initWithBlock:^(ExecuTorchLLMConfig *c) {
+  c.temperature = 0.8;
+  c.sequenceLength = 2048;
+}];
+
 NSError *error = nil;
-BOOL success = [runner generate:@"Once upon a time"
-                 sequenceLength:50
-              withTokenCallback:^(NSString *token) {
-                NSLog(@"Generated token: %@", token);
-              }
-                          error:&error];
+BOOL success = [runner generateWithPrompt:@"Once upon a time"
+                                   config:config
+                            tokenCallback:^(NSString *token) {
+                              NSLog(@"Generated token: %@", token);
+                            }
+                                    error:&error];
 if (!success) {
   NSLog(@"Generation failed: %@", error);
 }
@@ -99,7 +104,10 @@ if (!success) {
 Swift:
 ```swift
 do {
-  try runner.generate("Once upon a time", sequenceLength: 50) { token in
+  try runner.generate("Once upon a time", Config {
+    $0.temperature = 0.8
+    $0.sequenceLength = 2048
+  }) { token in
     print("Generated token:", token)
   }
 } catch {
@@ -121,6 +129,136 @@ Swift:
 runner.stop()
 ```
 
+#### Resetting
+
+To clear the prefilled tokens from the KV cache and reset generation stats, call:
+
+Objective-C:
+```objectivec
+[runner reset];
+```
+
+Swift:
+```swift
+runner.reset()
+```
+
+### MultimodalRunner
+
+The `ExecuTorchLLMMultimodalRunner` class (bridged to Swift as `MultimodalRunner`) provides an interface for loading and running multimodal models that can accept a sequence of text, image, and audio inputs.
+
+#### Multimodal Inputs
+
+Inputs are provided as an array of `ExecuTorchLLMMultimodalInput` (or `MultimodalInput` in Swift). You can create inputs from String for text, `ExecuTorchLLMImage` for images (`Image` in Swift), and `ExecuTorchLLMAudio` for audio features (`Audio`) in Swift.
+
+Objective-C:
+```objectivec
+ExecuTorchLLMMultimodalInput *textInput = [ExecuTorchLLMMultimodalInput inputWithText:@"What's in this image?"];
+
+NSData *imageData = ...; // Your raw image bytes
+ExecuTorchLLMImage *image = [[ExecuTorchLLMImage alloc] initWithData:imageData width:336 height:336 channels:3];
+ExecuTorchLLMMultimodalInput *imageInput = [ExecuTorchLLMMultimodalInput inputWithImage:image];
+```
+
+Swift:
+```swift
+let textInput = MultimodalInput("What's in this image?")
+
+let imageData: Data = ... // Your raw image bytes
+let image = Image(data: imageData, width: 336, height: 336, channels: 3)
+let imageInput = MultimodalInput(image)
+
+let audioFeatureData: Data = ... // Your raw audio feature bytes
+let audio = Audio(float: audioFeatureData, batchSize: 1, bins: 128, frames: 3000)
+let audioInput = MultimodalInput(audio)
+```
+
+#### Initialization
+
+Create a runner by specifying the paths to your multimodal model and its tokenizer.
+
+Objective-C:
+```objectivec
+NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"llava" ofType:@"pte"];
+NSString *tokenizerPath = [[NSBundle mainBundle] pathForResource:@"llava_tokenizer" ofType:@"bin"];
+
+ExecuTorchLLMMultimodalRunner *runner = [[ExecuTorchLLMMultimodalRunner alloc] initWithModelPath:modelPath
+                                                                                   tokenizerPath:tokenizerPath];
+```
+
+Swift:
+```swift
+let modelPath = Bundle.main.path(forResource: "llava", ofType: "pte")!
+let tokenizerPath = Bundle.main.path(forResource: "llava_tokenizer", ofType: "bin")!
+
+let runner = MultimodalRunner(modelPath: modelPath, tokenizerPath: tokenizerPath)
+```
+
+#### Loading
+
+Explicitly load the model before generation.
+
+Objective-C:
+```objectivec
+NSError *error = nil;
+BOOL success = [runner loadWithError:&error];
+if (!success) {
+  NSLog(@"Failed to load: %@", error);
+}
+```
+
+Swift:
+```swift
+do {
+  try runner.load()
+} catch {
+  print("Failed to load: \(error)")
+}
+```
+
+#### Generating
+
+Generate tokens from an ordered array of multimodal inputs.
+
+Objective-C:
+```objectivec
+NSArray<ExecuTorchLLMMultimodalInput *> *inputs = @[textInput, imageInput];
+
+ExecuTorchLLMConfig *config = [[ExecuTorchLLMConfig alloc] initWithBlock:^(ExecuTorchLLMConfig *c) {
+  c.sequenceLength = 768;
+}];
+
+NSError *error = nil;
+BOOL success = [runner generateWithInputs:inputs
+                                   config:config
+                            tokenCallback:^(NSString *token) {
+                              NSLog(@"Generated token: %@", token);
+                            }
+                                    error:&error];
+if (!success) {
+  NSLog(@"Generation failed: %@", error);
+}
+```
+
+Swift:
+```swift
+let inputs = [textInput, imageInput]
+
+do {
+  try runner.generate(inputs, Config {
+    $0.sequenceLength = 768
+  }) { token in
+    print("Generated token:", token)
+  }
+} catch {
+  print("Generation failed:", error)
+}
+```
+
+#### Stopping and Resetting
+
+The stop and reset methods for `MultimodalRunner` behave identically to those on `TextRunner`.
+
 ## Demo
 
 Get hands-on with our [etLLM iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/llm/apple) to see the LLM runtime APIs in action.