diff --git a/llama-rs/src/lib.rs b/llama-rs/src/lib.rs
index aa0a5baf..c718dda1 100644
--- a/llama-rs/src/lib.rs
+++ b/llama-rs/src/lib.rs
@@ -1,5 +1,6 @@
 mod ggml;
 
+use core::slice;
 use std::{
     collections::HashMap,
     fmt::Display,
@@ -422,6 +423,17 @@ pub enum InferenceError {
     UserCallback(Box<dyn std::error::Error>),
 }
 
+/// Used in a call to `evaluate` to request information from the transformer.
+#[derive(Default)]
+pub struct EvaluateOutputRequest {
+    /// Returns all the logits for the provided batch of tokens.
+    /// Output shape is n_batch * n_vocab
+    pub all_logits: Option<Vec<f32>>,
+    /// Returns the embeddings for the provided batch of tokens
+    /// Output shape is n_batch * n_embd
+    pub embeddings: Option<Vec<f32>>,
+}
+
 /// NOTE: The original code relies in promotion rules and automatic cast between
 /// int to float. What we do instead is use this macro to convert every term of
 /// the multiplication to f64, which should have enough precision bits to hold
@@ -1094,11 +1106,17 @@ impl Model {
     }
 
     /// Evaluates the transformer.
+    ///
+    /// The provided `output_request` struct lets you specify which additional
+    /// data you are interested in fetching from the transformer. Setting a
+    /// field to a `Some` value will clear and fill the provided vector with
+    /// data. The provided vector will be resized to the exact output size.
     pub fn evaluate(
         &self,
         session: &mut InferenceSession,
         params: &InferenceParameters,
         input_tokens: &[TokenId],
+        output_request: &mut EvaluateOutputRequest,
     ) {
         let n = input_tokens.len();
         let n_past = session.n_past as i32;
@@ -1317,12 +1335,16 @@ impl Model {
             input_layer = current;
         }
 
+        // Used at the end to optionally extract the embeddings.
+        let embeddings_tensor;
+
         // norm
         {
             input_layer = ctx0.op_norm(&input_layer);
 
             // inpL = norm*inpL
             input_layer = ctx0.op_mul(&ctx0.op_repeat(&self.norm, &input_layer), &input_layer);
+            embeddings_tensor = input_layer.share();
         }
 
         // lm_head
@@ -1347,6 +1369,28 @@ impl Model {
             )
         };
 
+        // Extract logits
+        if let Some(all_logits) = &mut output_request.all_logits {
+            all_logits.resize(n_vocab as usize * n, 0.0);
+            // SAFETY: Tensor data can be read (properly aligned, initialized,
+            // data will not be mutated or otherwise aliased during the copy),
+            // and we're not reading past the end of the tensor data.
+            assert_eq!(input_layer.nelements(), n_vocab * n as i32);
+            unsafe {
+                input_layer.read_data(0, bytemuck::cast_slice_mut(all_logits));
+            }
+        }
+
+        // Extract embeddings
+        if let Some(embeddings) = &mut output_request.embeddings {
+            embeddings.resize(n_embd as usize * n, 0.0);
+            // SAFETY: Same rationale as for the "Extract logits" section applies.
+            assert_eq!(embeddings_tensor.nelements(), n_embd * n as i32);
+            unsafe {
+                embeddings_tensor.read_data(0, bytemuck::cast_slice_mut(embeddings));
+            }
+        }
+
         // Adjust the required memory per token if we didn't know that already
         if session.mem_per_token == 0 {
             session.mem_per_token = ctx0.used_mem() / n;
@@ -1418,7 +1462,7 @@ impl InferenceSession {
         }
 
         for batch in prompt_tokens.chunks(8) {
-            model.evaluate(self, params, batch);
+            model.evaluate(self, params, batch, &mut EvaluateOutputRequest::default());
             for &tk in batch {
                 // NOTE: No string ever tokenizes to the end of sentence. So we
                 // can just return the id here.
@@ -1452,7 +1496,12 @@ impl InferenceSession {
         self.tokens.push(next_token);
 
         // Then, evaluate the network again to compute the new last_logits
-        model.evaluate(self, params, &[next_token]);
+        model.evaluate(
+            self,
+            params,
+            &[next_token],
+            &mut EvaluateOutputRequest::default(),
+        );
 
         // Return the next token
         Ok(if next_token as TokenId == EOD_TOKEN_ID {
@@ -1533,7 +1582,6 @@ impl InferenceSession {
     /// ggml context. While the provided `InferenceSnapshotRef` object is alive,
     /// no other methods for this model object should be called.
     pub unsafe fn get_snapshot(&mut self) -> InferenceSnapshotRef<'_> {
-        use core::slice;
         let memory_k = unsafe {
             slice::from_raw_parts(self.memory_k.data() as *mut u8, self.memory_k.nbytes())
         };