diff --git a/src/generation/logits_process.js b/src/generation/logits_process.js
index 732af4f3f..f82634f75 100644
--- a/src/generation/logits_process.js
+++ b/src/generation/logits_process.js
@@ -151,7 +151,7 @@ export class ForcedBOSTokenLogitsProcessor extends LogitsProcessor {
      * Apply the BOS token forcing to the logits.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The logits with BOS token forcing.
+     * @returns {Tensor} The logits with BOS token forcing.
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
@@ -221,7 +221,7 @@ export class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor {
      * Apply the BOS token forcing to the logits.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The logits with BOS token forcing.
+     * @returns {Tensor} The logits with BOS token forcing.
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
@@ -391,7 +391,7 @@ export class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
      * Apply the no-repeat-ngram processor to the logits.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The logits with no-repeat-ngram processing.
+     * @returns {Tensor} The logits with no-repeat-ngram processing.
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
@@ -406,12 +406,22 @@ export class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
 }
 
 /**
- * A logits processor that penalises repeated output tokens.
+ * A logits processor that prevents the repetition of previous tokens through a penalty.
+ * This penalty is applied at most once per token. Note that, for decoder-only models like most LLMs,
+ * the considered tokens include the prompt.
+ * 
+ * In the original [paper](https://arxiv.org/pdf/1909.05858.pdf), the authors suggest the use of a
+ * penalty of around 1.2 to achieve a good balance between truthful generation and lack of repetition.
+ * To penalize and reduce repetition, use `penalty` values above 1.0, where a higher value penalizes
+ * more strongly. To reward and encourage repetition, use `penalty` values between 0.0 and 1.0, where
+ * a lower value rewards more strongly.
  */
 export class RepetitionPenaltyLogitsProcessor extends LogitsProcessor {
     /**
      * Create a RepetitionPenaltyLogitsProcessor.
-     * @param {number} penalty The penalty to apply for repeated tokens.
+     * @param {number} penalty The parameter for repetition penalty.
+     * - 1.0 means no penalty. Above 1.0 penalizes previously generated tokens.
+     * - Between 0.0 and 1.0 rewards previously generated tokens.
      */
     constructor(penalty) {
         super();
@@ -422,16 +432,12 @@ export class RepetitionPenaltyLogitsProcessor extends LogitsProcessor {
      * Apply the repetition penalty to the logits.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The logits with repetition penalty processing.
+     * @returns {Tensor} The logits with repetition penalty processing.
      */
     _call(input_ids, logits) {
-        // Modify the logits corresponding to each element in `input_ids`.
-        // As a consequence, the logits corresponding to tokens that appear
-        // many times in the output will be penalised more.
-
         for (let i = 0; i < input_ids.length; ++i) {
             const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
-            for (const input_id of input_ids[i]) {
+            for (const input_id of new Set(input_ids[i])) {
                 const token = Number(input_id);
                 if (batch_logits_data[token] < 0) {
                     batch_logits_data[token] *= this.penalty;
@@ -464,7 +470,7 @@ export class MinLengthLogitsProcessor extends LogitsProcessor {
      * Apply logit processor.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The processed logits.
+     * @returns {Tensor} The processed logits.
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
@@ -502,7 +508,7 @@ export class MinNewTokensLengthLogitsProcessor extends LogitsProcessor {
      * Apply logit processor.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The processed logits.
+     * @returns {Tensor} The processed logits.
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
@@ -535,7 +541,7 @@ export class NoBadWordsLogitsProcessor extends LogitsProcessor {
      * Apply logit processor.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The processed logits.
+     * @returns {Tensor} The processed logits.
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
@@ -596,7 +602,7 @@ export class ClassifierFreeGuidanceLogitsProcessor extends LogitsProcessor {
      * Apply logit processor.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The processed logits.
+     * @returns {Tensor} The processed logits.
      */
     _call(input_ids, logits) {
         if (logits.dims[0] !== 2 * input_ids.length) {
@@ -650,7 +656,7 @@ export class TemperatureLogitsWarper extends LogitsWarper {
      * Apply logit warper.
      * @param {bigint[][]} input_ids The input IDs.
      * @param {Tensor} logits The logits.
-     * @returns {Object} The processed logits.
+     * @returns {Tensor} The processed logits.
      */
     _call(input_ids, logits) {
         const batch_logits_data = /** @type {Float32Array} */(logits.data);