diff --git a/README.md b/README.md
index 385d047..2da815c 100644
--- a/README.md
+++ b/README.md
@@ -80,3 +80,10 @@ Currently supported vision models and download links:
    - [mlx-community/Qwen2-VL-7B-Instruct-4bit](https://model.lmstudio.ai/download/mlx-community/Qwen2-VL-7B-Instruct-4bit) - 4.68 GB
  - Llava-v1.6
    - [mlx-community/llava-v1.6-mistral-7b-4bit](https://model.lmstudio.ai/download/mlx-community/llava-v1.6-mistral-7b-4bit) - 4.26 GB
+
+## Testing
+
+To run tests, run the following command from the root of this repo:
+```
+python -m unittest discover tests
+```
diff --git a/demo.py b/demo.py
index a3628d5..792381a 100644
--- a/demo.py
+++ b/demo.py
@@ -82,10 +82,9 @@ def image_to_base64(image_path):
     generator = create_generator(
         model_kit,
         prompt_tokens,
-        None,
-        images_base64,
-        args.stop_strings,
-        {"max_tokens": 1024},
+        images_b64=images_base64,
+        stop_strings=args.stop_strings,
+        max_tokens=1024,
         top_logprobs=args.top_logprobs,
     )
     for generation_result in generator:
diff --git a/mlx_engine/generate.py b/mlx_engine/generate.py
index 2c4074b..3627305 100644
--- a/mlx_engine/generate.py
+++ b/mlx_engine/generate.py
@@ -1,6 +1,7 @@
-from typing import Callable, Iterator, List, NamedTuple, Optional
+from typing import Callable, Iterator, List, Literal, NamedTuple, Optional
 import json
 from pathlib import Path
+import sys
 
 import mlx_lm
 
@@ -8,12 +9,24 @@
 from mlx_engine.vision.vision_model_kit import VisionModelKit
 from mlx_engine.processors.outlines_logits_processor import OutlinesLogitsProcessor
 from mlx_engine.utils.top_logprobs import summarize_top_logprobs, TokenLogprob
-from mlx_engine.stop_processor import StopProcessor, GenerationStopCondition
+from mlx_engine.stop_string_processor import (
+    StopStringProcessor,
+    StopStringProcessorResult,
+)
 from mlx_engine.utils.set_seed import set_seed
 
 
 MAX_TOP_LOGPROBS = 10
 
+StopReason = Literal["eos_token", "stop_string"]
+
+
+class GenerationStopCondition(NamedTuple):
+    stop_reason: StopReason
+    stop_string: str
+    # sequence of token ids that the stop string was found in
+    stop_tokens: List[int]
+
 
 class GenerationResult(NamedTuple):
     text: str
@@ -28,81 +41,156 @@ def load_model(
     """
     Load a language model or vision-language model from the specified path.
 
+    This function determines the model type based on the config.json file in the model directory
+    and initializes either a standard language model or a vision-language model accordingly.
+
     Args:
-        model_path (str | Path): Path to the model directory containing model files and config.json
-        max_kv_size (int): Maximum key-value cache size for the model
-        trust_remote_code (bool): Whether to trust and load remote code during model initialization
+        model_path (str | Path): Path to the model directory containing model files and config.json.
+        max_kv_size (int): Maximum size of the key-value cache used during model inference.
+        trust_remote_code (bool): Whether to allow loading of remote code during model initialization.
 
     Returns:
-        ModelKit | VisionModelKit: An instance of either ModelKit for language models or
-            VisionModelKit for vision-language models, depending on the model type
+        ModelKit | VisionModelKit: An initialized model instance:
+            - ModelKit for standard language models
+            - VisionModelKit for models that can process both text and images
 
     Raises:
-        FileNotFoundError: If config.json is not found in the model path
-        json.JSONDecodeError: If config.json is not valid JSON
+        FileNotFoundError: If config.json is not found in the specified model path
+        json.JSONDecodeError: If config.json exists but contains invalid JSON
+        ValueError: If the model configuration is invalid or unsupported
     """
     model_path = Path(model_path)
     config_json = json.loads((model_path / "config.json").read_text())
 
     if "vision_config" in config_json:
-        return VisionModelKit(model_path, trust_remote_code)
+        return VisionModelKit(model_path, max_kv_size, trust_remote_code)
     else:
         return ModelKit(model_path, max_kv_size)
 
 
-# Adapted from mlx_lm.utils.stream_generate
 def create_generator(
     model_kit: ModelKit | VisionModelKit,
     prompt_tokens: List[int],
-    prompt_progress_callback: Optional[Callable[[float], None]],
-    images_b64: Optional[List[str]],
-    stop_strings: Optional[List[str]],
-    generate_args: dict,
+    *,
+    prompt_progress_callback: Optional[Callable[[float], None]] = None,
+    images_b64: Optional[List[str]] = None,
+    stop_strings: Optional[List[str]] = None,
     top_logprobs: Optional[int] = None,
+    repetition_penalty: Optional[float] = None,
+    repetition_context_size: Optional[int] = 20,
+    temp: Optional[float] = None,
+    top_p: Optional[float] = None,
+    min_p: Optional[float] = None,
+    min_tokens_to_keep: Optional[int] = None,
+    seed: Optional[int] = None,
+    json_schema: Optional[str] = None,
+    max_tokens: Optional[int] = 10000000,
 ) -> Iterator[GenerationResult]:
     """
     Create a generator that streams text generation results from the model.
 
+    This function sets up and manages the text generation process, handling various generation
+    parameters, processing callbacks, and managing generation constraints. It supports both
+    standard language models and vision-language models.
+
     Args:
-        model_kit (ModelKit | VisionModelKit): The model to use for generation
+        model_kit (ModelKit | VisionModelKit): The initialized model to use for generation
         prompt_tokens (List[int]): List of token IDs representing the input prompt
-        prompt_progress_callback (Optional[Callable[[float], None]]): Optional callback function
-            that receives generation progress as a float between 0 and 1
-        images_b64 (Optional[List[str]]): Optional list of base64-encoded images for
-            vision-language models
-        stop_strings (Optional[List[str]]): Optional list of strings that will stop generation
+        prompt_progress_callback (Optional[Callable[[float], None]]): Callback function that receives
+            generation progress as a float between 0 and 1
+        images_b64 (Optional[List[str]]): List of base64-encoded images for vision-language models
+        stop_strings (Optional[List[str]]): List of strings that will trigger generation to stop
             when encountered
-        generate_args (dict): Dictionary of generation parameters that are passed to `mlx_lm`
-            Full list of parameters:
-            https://github.com/ml-explore/mlx-examples/blob/bd6d910/llms/mlx_lm/utils.py#L158
-        top_logprobs (Optional[int]): Number of top token probabilities to return per token.
+        top_logprobs (Optional[int]): Number of top token probabilities to return per token
             Must be <= MAX_TOP_LOGPROBS
+        repetition_penalty (Optional[float]): Penalty factor for repeated tokens. Higher values
+            discourage repetition
+        repetition_context_size (Optional[int]): Number of previous tokens to consider for
+            repetition penalty. Defaults to 20
+        temp (Optional[float]): Temperature for sampling. Higher values increase randomness
+        top_p (Optional[float]): Top-p (nucleus) sampling parameter
+        min_p (Optional[float]): Minimum probability threshold for token sampling
+        min_tokens_to_keep (Optional[int]): Minimum number of tokens to keep during sampling
+        seed (Optional[int]): Random seed for reproducible generation
+        json_schema (Optional[str]): JSON schema for structured output generation
+        max_tokens (Optional[int]): Maximum number of tokens to generate. Defaults to 10000000
 
     Yields:
-        GenerationResult: Named tuple containing:
-            - text: Generated text segment
-            - tokens: List of generated tokens, as TokenLogprob named tuples
-            - top_logprobs: Token probability information if requested
-            - stop_condition: Information about why generation stopped, if applicable
+        GenerationResult: A named tuple containing:
+            - text (str): Generated text segment
+            - tokens (List[TokenLogprob]): List of generated tokens with their probabilities
+            - top_logprobs (List[List[TokenLogprob]]): Token probability information if requested
+            - stop_condition (Optional[GenerationStopCondition]): Information about why
+              generation stopped, if applicable
 
     Raises:
-        ValueError: If top_logprobs exceeds MAX_TOP_LOGPROBS
+        ValueError: If top_logprobs exceeds MAX_TOP_LOGPROBS or if any parameters are invalid
     """
-    set_seed(generate_args.pop("seed", None))
+    set_seed(seed)
+
+    generate_args = {}
+    if type(model_kit) is not VisionModelKit:
+        generate_args["max_kv_size"] = model_kit.max_kv_size
+
+    # Set up repetition penalty
+    repetition_penalty_kwargs = {}
+    if repetition_penalty is not None:
+        repetition_penalty_kwargs["repetition_penalty"] = repetition_penalty
+        if repetition_context_size is not None:
+            repetition_penalty_kwargs["repetition_context_size"] = (
+                repetition_context_size
+            )
+    generate_args["logits_processors"] = mlx_lm.utils.make_logits_processors(
+        logit_bias=None,
+        **repetition_penalty_kwargs,
+    )
+
 
     # Process prompt
-    generate_step_input = model_kit.process_prompt(
-        prompt_tokens, images_b64, prompt_progress_callback, generate_args
+    stream_generate_input = model_kit.process_prompt(
+        prompt_tokens,
+        images_b64,
+        prompt_progress_callback,
+        repetition_context_size,
+        generate_args,
     )
 
+    # Workaround until mlx_lm.utils.stream_generate supports prompt as type mx.array
+    stream_generate_input = stream_generate_input.tolist()
+
+    # Set up sampler
+    generate_args["sampler"] = mlx_lm.utils.make_sampler(
+        **{
+            k: v
+            for k, v in {
+                "temp": temp,
+                "top_p": top_p,
+                "min_p": min_p,
+                "min_tokens_to_keep": min_tokens_to_keep,
+            }.items()
+            if v is not None
+        }
+    )
+
+    # For vision models, immediately record the token once it's sampled
+    if type(model_kit) is VisionModelKit:
+        sampler_func = generate_args["sampler"]
+        def sampler_func_wrapper(*args, **kwargs):
+            token = sampler_func(*args, **kwargs)
+            model_kit.record_sampled_token(token)
+            return token
+        generate_args["sampler"] = sampler_func_wrapper
+        
+
+
     # Add outlines logits processor if json_schema is provided
-    logits_processor = []
-    json_schema = generate_args.pop("json_schema", None)
     is_structured_output_request = json_schema is not None
     if is_structured_output_request:
-        logits_processor.append(OutlinesLogitsProcessor(model_kit, json_schema))
-    generate_args["logits_processor"] = logits_processor
+        generate_args["logits_processors"].append(
+            OutlinesLogitsProcessor(model_kit, json_schema)
+        )
 
+    # Validate top_logprobs
     if top_logprobs is None:
         top_logprobs = 0
     if top_logprobs > MAX_TOP_LOGPROBS:
@@ -110,66 +198,132 @@ def create_generator(
             f"top_logprobs must be less than or equal to {MAX_TOP_LOGPROBS}"
         )
 
-    max_tokens = generate_args.pop("max_tokens")
-    tokenizer = model_kit.tokenizer
-    detokenizer = model_kit.detokenizer
-    detokenizer.reset()
-    # keep track of tokens buffered by detokenizer to yield accurate generation results
+    # Keep track of tokens buffered by detokenizer to yield accurate generation results
     token_buffer: List[TokenLogprob] = []
     top_logprobs_buffer: List[List[TokenLogprob]] = []
 
-    stop_sequences = [
-        tokenize(model_kit, sequence) for sequence in (stop_strings or [])
-    ]
-    stop_processor = StopProcessor(tokenizer, stop_sequences)
-    stop_processor_result = None
-
-    for (token, logprobs), n in zip(
-        mlx_lm.utils.generate_step(
-            generate_step_input, model_kit.model, **generate_args
-        ),
-        range(max_tokens),
+    tokenizer = model_kit.tokenizer
+
+    # Set up stop string processor if non-empty stop_strings are provided
+    stop_string_processor = None
+    if stop_strings is not None and len(stop_strings) > 0:
+        stop_string_processor = StopStringProcessor(stop_strings, tokenizer)
+    text = ""
+
+    def _handle_stop_string_detected(
+        tokenizer,
+        stop_string_processor_result: StopStringProcessorResult,
+        text: str,
+        token_buffer: List[TokenLogprob],
+        top_logprobs_buffer: List[List[TokenLogprob]],
+    ) -> GenerationResult:
+        """
+        Helper method to Handle completion of text generation when a stop string is
+        encountered.
+
+        Args:
+            tokenizer: The tokenizer instance
+            stop_string_processor_result: Result from stop string processor
+            text: Current generated text
+            token_buffer: Buffer of generated tokens
+            top_logprobs_buffer: Buffer of token probabilities
+
+        Returns:
+            GenerationResult: Final generation result including stop condition
+        """
+        # Finalize detokenizer to get remaining text
+        detokenizer = tokenizer.detokenizer
+        detokenizer.finalize()
+        text += detokenizer.last_segment
+
+        # Process stop string by trimming text segment where it begins
+        stop_string = stop_string_processor_result.stop_string
+        stop_string_start_pos = text.find(stop_string)
+
+        if stop_string_start_pos != -1:
+            text = text[:stop_string_start_pos]
+        else:
+            # this is known to happen when the eos token is a stop string
+            sys.stderr.write(
+                f"[mlx-engine] Stop string '{stop_string}' not found in final text segment, "
+                "even though a full stop was detected. Not trimming final segment."
+            )
+
+        stop_condition = GenerationStopCondition(
+            stop_reason="stop_string",
+            stop_string=stop_string,
+            stop_tokens=stop_string_processor_result.stop_tokens,
+        )
+
+        return GenerationResult(
+            text=text,
+            tokens=token_buffer,
+            stop_condition=stop_condition,
+            top_logprobs=top_logprobs_buffer,
+        )
+
+    for generation_result in mlx_lm.utils.stream_generate(
+        model=model_kit.model,
+        tokenizer=tokenizer,
+        prompt=stream_generate_input,
+        max_tokens=max_tokens,
+        **generate_args,
     ):
-        model_kit.record_generated_token(token)
-        detokenizer.add_token(token)
-        token_buffer.append(TokenLogprob(token, tokenizer.decode(token), float(logprobs[token])))
+        # Token processor
+        token = generation_result.token
+        text += generation_result.text
+        model_kit.update_cache_wrapper(token)
+
+        logprobs = generation_result.logprobs
+        token_buffer.append(
+            TokenLogprob(token, tokenizer.decode(token), float(logprobs[token]))
+        )
         if top_logprobs:
             top_logprobs_buffer.append(
                 summarize_top_logprobs(tokenizer, logprobs, top_logprobs)
             )
-        stop_processor_result = stop_processor.process_token(token)
-
-        if stop_processor_result.status == "full_stop":
-            break
-        # If we currently have generated a partial match with a stop sequence, generate new
-        # tokens until we know if the stop sequence is hit or not (i.e., make sure not to yield yet)
-        if stop_processor_result.status == "partial_match":
-            continue
-
-        # only yield a generation result the detokenizer has a segment to yield
-        new_text = detokenizer.last_segment
-        if new_text:
+
+        # Stop processor
+        if stop_string_processor is not None:
+            stop_string_processor_result = stop_string_processor.process_token(token)
+            if stop_string_processor_result.status == "full_stop":
+                yield _handle_stop_string_detected(
+                    tokenizer,
+                    stop_string_processor_result,
+                    text,
+                    token_buffer,
+                    top_logprobs_buffer,
+                )
+                break  # stop generation
+
+            # If we currently have generated a partial match with a stop sequence, or detected an
+            # in-progress multi-byte string, generate new tokens until we know if the stop sequence
+            # is hit or not (i.e., make sure not to yield yet)
+            if (
+                stop_string_processor_result.status == "partial_match"
+                or stop_string_processor_result.status == "multi_byte"
+            ):
+                continue
+
+        # Standard yield - only yield when a non-empty text segment is available
+        if text:
+            # populate stop_condition if we hit an eos token
+            stop_condition = None
+            if token == tokenizer.eos_token_id:
+                stop_condition = GenerationStopCondition(
+                    stop_reason="eos_token",
+                    stop_string=tokenizer.decode(token),
+                    stop_tokens=[token],
+                )
             yield GenerationResult(
-                text=new_text,
+                text=text,
                 tokens=token_buffer,
-                stop_condition=None,
+                stop_condition=stop_condition,
                 top_logprobs=top_logprobs_buffer,
             )
             token_buffer = []
             top_logprobs_buffer = []
-
-    # check is there any remaining text to send
-    detokenizer.finalize()
-    last_segment = detokenizer.last_segment
-    last_segment, generation_stop_condition = stop_processor.finalize(
-        last_segment, stop_processor_result
-    )
-    yield GenerationResult(
-        text=last_segment,
-        tokens=token_buffer,
-        stop_condition=generation_stop_condition,
-        top_logprobs=top_logprobs_buffer,
-    )
+            text = ""
 
 
 def tokenize(model_kit: ModelKit | VisionModelKit, prompt: str) -> List[int]:
@@ -177,10 +331,12 @@ def tokenize(model_kit: ModelKit | VisionModelKit, prompt: str) -> List[int]:
     Convert a text prompt into a list of token IDs using the model's tokenizer.
 
     Args:
-        model_kit (ModelKit | VisionModelKit): The model kit containing the tokenizer
-        prompt (str): The text prompt to tokenize
+        model_kit (ModelKit | VisionModelKit): The model kit instance containing the tokenizer
+            to use for tokenization
+        prompt (str): The raw text prompt to be tokenized
 
     Returns:
-        List[int]: List of token IDs representing the tokenized prompt
+        List[int]: A list of integer token IDs representing the tokenized prompt,
+            ready for model input
     """
     return model_kit.tokenize(prompt)
diff --git a/mlx_engine/model_kit.py b/mlx_engine/model_kit.py
index 3d796d7..32ffb47 100644
--- a/mlx_engine/model_kit.py
+++ b/mlx_engine/model_kit.py
@@ -18,12 +18,14 @@ class ModelKit:
     tokenizer: TokenizerWrapper = None
     detokenizer: StreamingDetokenizer = None
     cache_wrapper: Optional[CacheWrapper] = None
+    max_kv_size: int = None
 
     def __init__(self, model_path: Path, max_kv_size: int):
         self.model_path = model_path
         self.model, self.tokenizer = mlx_lm.utils.load(self.model_path)
         self.detokenizer = self.tokenizer.detokenizer
         self.cache_wrapper = CacheWrapper(self.model, max_kv_size)
+        self.max_kv_size = max_kv_size
 
     def tokenize(self, prompt: str) -> List[int]:
         ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(prompt))
@@ -32,7 +34,12 @@ def tokenize(self, prompt: str) -> List[int]:
         return ids
 
     def process_prompt(
-        self, prompt_tokens, img_b64, prompt_progress_callback, generate_args
+        self,
+        prompt_tokens,
+        img_b64,
+        prompt_progress_callback,
+        repetition_context_size,
+        generate_args,
     ) -> mx.array:
         """
         This method processes the prompt, adding its tokens to the cache history
@@ -45,12 +52,6 @@ def process_prompt(
         if len(prompt_tokens) == 0:
             raise ValueError("Prompt tokens must be non-empty")
 
-        if "repetition_context_size" not in generate_args:
-            generate_args["repetition_context_size"] = (
-                20  # default value for mlx_lm.utils.generate_step
-            )
-        repetition_context_size = generate_args["repetition_context_size"]
-
         # Check for common tokens with the previous cache and re-use the cache if possible
         prompt_tokens = self.cache_wrapper.update_cache(
             mx.array(prompt_tokens),
@@ -61,7 +62,7 @@ def process_prompt(
 
         return prompt_tokens
 
-    def record_generated_token(self, token: int) -> None:
+    def update_cache_wrapper(self, token: int) -> None:
         self.cache_wrapper.record_generated_token(token)
 
     @property
diff --git a/mlx_engine/stop_processor.py b/mlx_engine/stop_processor.py
deleted file mode 100644
index 77c2acf..0000000
--- a/mlx_engine/stop_processor.py
+++ /dev/null
@@ -1,159 +0,0 @@
-"""Module for processing and handling stop sequences in token generation."""
-
-from typing import List, Literal, NamedTuple, Optional, Sequence, Tuple
-
-
-StopReason = Literal["eos_token", "stop_string"]
-
-StopProcessorStatus = Literal["full_stop", "partial_match", "no_match"]
-
-
-class StopProcessorResult(NamedTuple):
-    """Result of stop sequence processing containing status and details."""
-
-    status: StopProcessorStatus
-    stop_reason: Optional[StopReason]
-    stop_tokens: Optional[List[int]]
-
-
-class GenerationStopCondition(NamedTuple):
-    stop_reason: StopReason
-    stop_string: str
-    stop_tokens: List[int]
-
-
-class StopProcessor:
-    """Statefully processes tokens to check for stop sequences during generation."""
-
-    def __init__(self, tokenizer, stop_sequences: List[List[int]]):
-        """
-        Args:
-            tokenizer: Tokenizer instance for encoding/decoding text
-            stop_sequences: List of token sequences that signal generation should stop
-        """
-        self.tokenizer = tokenizer
-        self.stop_sequences = stop_sequences
-        self.tokens_to_check_for_stopping = []
-        self.stop_sequence_suffix = None
-
-    def process_token(self, tokens: int) -> StopProcessorResult:
-        """Process a new token and check if generation should stop.
-
-        Args:
-            tokens: New token to process
-
-        Returns:
-            StopProcessorResult indicating if/how generation should stop
-        """
-        self.tokens_to_check_for_stopping.append(tokens)
-        stopping_criteria_result = stopping_criteria(
-            self.tokens_to_check_for_stopping,
-            self.stop_sequences,
-            self.tokenizer,
-        )
-        status = stopping_criteria_result.status
-        if status == "full_stop":
-            # on a full stop, save the stop sequence suffix to allow client to handle it
-            # how they'd like (i.e., trim, not trim, etc...)
-            if stopping_criteria_result.stop_tokens:
-                trim_length = len(stopping_criteria_result.stop_tokens)
-                self.stop_sequence_suffix = self.tokenizer.decode(
-                    self.tokens_to_check_for_stopping[-trim_length:]
-                )
-            return StopProcessorResult(
-                "full_stop",
-                stopping_criteria_result.stop_reason,
-                stopping_criteria_result.stop_tokens,
-            )
-        elif status == "partial_match":
-            return StopProcessorResult("partial_match", None, None)
-        else:
-            # if no partial match, can clear the buffer of tokens we need to check for stopping
-            self.tokens_to_check_for_stopping = []
-            return StopProcessorResult("no_match", None, None)
-
-    def finalize(
-        self, last_segment: str, stop_processor_result: StopProcessorResult
-    ) -> Tuple[str, Optional[GenerationStopCondition]]:
-        if last_segment:
-            if self.stop_sequence_suffix is not None:
-                last_segment = last_segment[: -len(self.stop_sequence_suffix)]
-
-        # build up the final generation stop condition safely, although stop_processor_result
-        # should always be set at this point
-        generation_stop_condition = None
-        if stop_processor_result and stop_processor_result.stop_reason:
-            stop_tokens = stop_processor_result.stop_tokens or []
-            stop_string = self.tokenizer.decode(stop_tokens) if stop_tokens else ""
-            generation_stop_condition = GenerationStopCondition(
-                stop_reason=stop_processor_result.stop_reason,
-                stop_string=stop_string,
-                stop_tokens=stop_tokens,
-            )
-        return last_segment, generation_stop_condition
-
-
-# Adapted from mlx_lm.utils.server.py
-# https://github.com/ml-explore/mlx-examples/blob/605c4854f1547e8eb0ef3f9c9d81c8aef3196c15/llms/mlx_lm/server.py#L43-L74
-def stopping_criteria(
-    tokens: List[int],
-    stop_id_sequences: List[List[int]],
-    tokenizer,
-) -> StopProcessorResult:
-    """Check if token generation should stop.
-
-    Checks three conditions in order:
-    1. End of sequence token from tokenizer
-    2. Exact token match with stop sequences
-    3. Full match between decoded text and any decoded stop sequence
-    4. Partial match with any decoded stop sequence
-    """
-    # Check for end of sequence token
-    if tokens and tokens[-1] == tokenizer.eos_token_id:
-        return StopProcessorResult(
-            status="full_stop",
-            stop_reason="eos_token",
-            stop_tokens=[tokenizer.eos_token_id],
-        )
-
-    # Check for exact token sequence matches
-    for stop_ids in stop_id_sequences:
-        if len(tokens) >= len(stop_ids) and tokens[-len(stop_ids) :] == stop_ids:
-            return StopProcessorResult(
-                status="full_stop", stop_reason="stop_string", stop_tokens=stop_ids
-            )
-
-    # Check for full matches in decoded text
-    tokens_str = tokenizer.decode(tokens)
-    for stop_ids in stop_id_sequences:
-        stop_str = tokenizer.decode(stop_ids)
-        if stop_str in tokens_str:
-            return StopProcessorResult(
-                status="full_stop", stop_reason="stop_string", stop_tokens=stop_ids
-            )
-
-    # Check for partial matches only if no full matches were found
-    for stop_ids in stop_id_sequences:
-        stop_str = tokenizer.decode(stop_ids)
-        if sequence_overlap(tokens_str, stop_str):
-            return StopProcessorResult(
-                status="partial_match", stop_reason=None, stop_tokens=None
-            )
-
-    # No matches found
-    return StopProcessorResult(status="no_match", stop_reason=None, stop_tokens=None)
-
-
-def sequence_overlap(s1: Sequence, s2: Sequence) -> bool:
-    """
-    Checks if a suffix of s1 has overlap with a prefix of s2
-
-    Args:
-        s1 (Sequence): The first sequence
-        s2 (Sequence): The second sequence
-
-    Returns:
-        bool: If the two sequences have overlap
-    """
-    max_overlap = min(len(s1), len(s2))
-    return any(s1[-i:] == s2[:i] for i in range(1, max_overlap + 1))
diff --git a/mlx_engine/stop_string_processor.py b/mlx_engine/stop_string_processor.py
new file mode 100644
index 0000000..0cffec9
--- /dev/null
+++ b/mlx_engine/stop_string_processor.py
@@ -0,0 +1,184 @@
+"""Module for processing and handling stop strings during token generation."""
+
+from typing import List, Literal, NamedTuple, Optional, Sequence
+
+StopStringProcessorStatus = Literal["full_stop", "partial_match", "no_match", "multi_byte"]
+
+REPLACEMENT_CHAR = "\ufffd"
+
+class StopStringProcessorResult(NamedTuple):
+    """Result of stop string processing containing status and details."""
+
+    status: StopStringProcessorStatus
+    stop_string: Optional[str] = None  # populated if status is "full_stop"
+    # sequence of tokens that the stop_string was found in
+    stop_tokens: Optional[List[int]] = None  # populated if status is "full_stop"
+
+
+class StopStringProcessor:
+    """State-fully processes tokens to check for stop strings during generation."""
+
+    def __init__(self, stop_strings: List[str], tokenizer):
+        """
+        Args:
+            stop_strings: List of strings that should stop generation if found
+            tokenizer: Tokenizer instance for encoding token IDs to text
+            
+        Raises:
+            ValueError: If stop_strings is empty or contains invalid values
+            TypeError: If stop_strings contains non-string values
+        """
+        if not stop_strings:
+            raise ValueError("Must provide at least one stop string")
+            
+        if not all(isinstance(s, str) for s in stop_strings):
+            raise TypeError("All stop strings must be strings")
+            
+        if any(not stop_string for stop_string in stop_strings):
+            raise ValueError("Stop strings cannot be empty")
+            
+        self.stop_strings = stop_strings
+        self.tokenizer = tokenizer
+        self.token_id_buffer = []
+
+    def process_token(self, token: int) -> StopStringProcessorResult:
+        """Process a new string segment and check how it relates to stop strings.
+
+        Args:
+            segment: The new string segment to process
+
+        Returns:
+            StopProcessorResult indicating the state of stop string detection
+        """
+        if len(self.stop_strings) == 0:
+            return StopStringProcessorResult(
+                status="no_match", stop_string=None, stop_tokens=None
+            )
+
+        self.token_id_buffer.append(token)
+
+        result = self._stopping_criteria(
+            string=self.tokenizer.decode(self.token_id_buffer),
+            stop_strings=self.stop_strings,
+        )
+
+        if result.status == "no_match":
+            # Can clear the buffer in no partial or full matches with stop sequences
+            self.token_id_buffer = []
+            return StopStringProcessorResult(
+                status="no_match", stop_string=None, stop_tokens=None
+            )
+
+        elif result.status == "partial_match":
+            return StopStringProcessorResult(
+                status="partial_match", stop_string=None, stop_tokens=None
+            )
+        
+        elif result.status == "multi_byte":
+            return StopStringProcessorResult(
+                status="multi_byte", stop_string=None, stop_tokens=None
+            )
+
+        elif result.status == "full_stop":
+            return StopStringProcessorResult(
+                status="full_stop",
+                stop_string=result.stop_string,
+                stop_tokens=self.token_id_buffer,
+            )
+
+        else:
+            raise ValueError(f"Unknown StopProcessorStatus: {result.status}")
+        
+    class _StoppingCriteriaResult(NamedTuple):
+        status: StopStringProcessorStatus
+        stop_string: Optional[str] = None  # populated if status is "full_stop"
+    
+    def _stopping_criteria(
+        self,
+    string: str,
+        stop_strings: List[str],
+    ) -> _StoppingCriteriaResult:
+        """Check if stop strings match or partially match the input string
+
+        Args:
+            string: The string to check for stop strings
+            stop_strings: List of strings that should stop generation if found
+
+        Returns:
+            StopStringProcessorResult indicating match status and stop string if matched
+
+        Checks stopping conditions in priority order:
+        1. Incomplete UTF-8 string
+        2. Exact stop string match
+        3. Partial stop string match
+        """
+
+        result = (
+            self._check_incomplete_utf8(string)
+            or self._check_full_text_match(string, stop_strings)
+            or self._check_partial_text_match(string, stop_strings)
+            or self._StoppingCriteriaResult(status="no_match", stop_string=None)
+        )
+
+        return result
+    
+    def _check_incomplete_utf8(self, string: str) -> Optional[_StoppingCriteriaResult]:
+        if string[-1] == REPLACEMENT_CHAR:
+            return self._StoppingCriteriaResult(
+                status="multi_byte", stop_string=None
+            )
+        return None
+        
+
+    def _check_full_text_match(
+        self, string: str, stop_strings: List[str]
+    ) -> Optional[_StoppingCriteriaResult]:
+        """Find earliest full text match of any stop sequence."""
+        earliest_match = {"position": float("inf"), "stop_string": None}
+
+        for stop_string in stop_strings:
+            position = string.find(stop_string)
+
+            if position != -1 and position < earliest_match["position"]:
+                earliest_match.update({"position": position, "stop_string": stop_string})
+
+        if earliest_match["stop_string"] is not None:
+            return self._StoppingCriteriaResult(
+                status="full_stop", stop_string=earliest_match["stop_string"]
+            )
+        return None
+
+
+    def check_partial_token_match(
+        self, token_sequence: List[int], stop_token_sequences: List[List[int]]
+    ) -> Optional[_StoppingCriteriaResult]:
+        """Check for partial matches with any stop sequence."""
+        for stop_token_sequence in stop_token_sequences:
+            if self._sequence_overlap(token_sequence, stop_token_sequence):
+                return self._StoppingCriteriaResult(status="partial_match", stop_string=None)
+        return None
+
+
+    def _check_partial_text_match(
+        self, string: str, stop_strings: List[str]
+    ) -> Optional[StopStringProcessorResult]:
+        """Check for partial matches with any stop sequence."""
+        for stop_string in stop_strings:
+            if self._sequence_overlap(string, stop_string):
+                return StopStringProcessorResult(status="partial_match", stop_string=None)
+        return None
+
+
+    def _sequence_overlap(self, s1: Sequence, s2: Sequence) -> bool:
+        """
+        Checks if a suffix of s1 has overlap with a prefix of s2
+
+        Args:
+            s1 (Sequence): The first sequence
+            s2 (Sequence): The second sequence
+
+        Returns:
+            bool: If the two sequences have overlap
+        """
+        max_overlap = min(len(s1), len(s2))
+        return any(s1[-i:] == s2[:i] for i in range(1, max_overlap + 1))
diff --git a/mlx_engine/utils/set_seed.py b/mlx_engine/utils/set_seed.py
index a3b8f30..2e0c6f5 100644
--- a/mlx_engine/utils/set_seed.py
+++ b/mlx_engine/utils/set_seed.py
@@ -13,9 +13,9 @@ def set_seed(seed: Optional[int]) -> None:
     PyTorch, and Python's built-in random module.
 
     Args:
-        seed: The seed value to initialize random number generators. If None, a seed will be 
-            automatically generated using the current nanosecond timestamp. The final seed 
-            value will be truncated to 32 bits for compatibility across all random number 
+        seed: The seed value to initialize random number generators. If None, a seed will be
+            automatically generated using the current nanosecond timestamp. The final seed
+            value will be truncated to 32 bits for compatibility across all random number
             generators.
 
     Raises:
@@ -33,8 +33,8 @@ def set_seed(seed: Optional[int]) -> None:
     """
     if seed is None:
         # Get nanosecond timestamp and use it as seed
-        seed = int(time.time_ns()) 
-    
+        seed = int(time.time_ns())
+
     if seed < 0:
         raise ValueError("Seed must be a non-negative integer.")
     seed = seed & (2**32 - 1)  # Ensure seed fits in 32 bits
diff --git a/mlx_engine/utils/top_logprobs.py b/mlx_engine/utils/top_logprobs.py
index 7bd1a53..6225a1b 100644
--- a/mlx_engine/utils/top_logprobs.py
+++ b/mlx_engine/utils/top_logprobs.py
@@ -24,9 +24,11 @@ def summarize_top_logprobs(
 
     # decode the top indices
     text_list = [tokenizer.decode(index) for index in top_indices.tolist()]
-    
+
     # return list of TokenLogprob with id (int), text (str), and logprob (float)
     return [
-        TokenLogprob(int(idx), txt, float(prob)) 
-        for idx, txt, prob in zip(top_indices.tolist(), text_list, top_logprobs.tolist())
+        TokenLogprob(int(idx), txt, float(prob))
+        for idx, txt, prob in zip(
+            top_indices.tolist(), text_list, top_logprobs.tolist()
+        )
     ]
diff --git a/mlx_engine/vision/vision_model_kit.py b/mlx_engine/vision/vision_model_kit.py
index 42d7e62..cd734a0 100644
--- a/mlx_engine/vision/vision_model_kit.py
+++ b/mlx_engine/vision/vision_model_kit.py
@@ -17,14 +17,16 @@ class VisionModelKit(ModelKit):
     config: dict = None
     trust_remote_code: bool = False
     model_path: Path = None
+    max_kv_size: int = None
 
     processor: Union[PreTrainedTokenizer, PreTrainedTokenizerFast] = None
     has_processed_prompt: bool = False
 
-    def __init__(self, model_path: Path, trust_remote_code: bool):
+    def __init__(self, model_path: Path, max_kv_size: int, trust_remote_code: bool):
         self.config = mlx_vlm.utils.load_config(model_path)
         self.trust_remote_code = trust_remote_code
         self.model_path = model_path
+        self.max_kv_size = max_kv_size
         self._initializer()
 
     def _initializer(self):
@@ -49,6 +51,7 @@ def process_prompt(
         prompt_tokens,
         images_b64: Optional[List[str]],
         prompt_progress_callback,
+        repetition_context_size,
         generate_args,
     ) -> mx.array:
         """
@@ -69,12 +72,15 @@ def process_prompt(
         # disable `prefill_step_size` prompt pre-processing in mlx_lm::generate_step
         generate_args["prefill_step_size"] = float("inf")
 
-        generate_step_input = self.model.input_ids[0]
+        generate_step_input = self.model.input_ids[None]
         return generate_step_input
 
-    def record_generated_token(self, token: int) -> None:
+    def update_cache_wrapper(self, token: int) -> None:
         pass
 
+    def record_sampled_token(self, token: int) -> None:
+        self.model.record_sampled_token(token)
+
     @property
     def language_model(self):
         return self.model.language_model
diff --git a/mlx_engine/vision/vision_model_wrapper.py b/mlx_engine/vision/vision_model_wrapper.py
index 6c28021..b3624d9 100644
--- a/mlx_engine/vision/vision_model_wrapper.py
+++ b/mlx_engine/vision/vision_model_wrapper.py
@@ -4,6 +4,7 @@
 from io import BytesIO
 import base64
 import PIL
+from mlx_vlm.models.base import KVCache, SimpleKVCache
 
 from typing import List, Optional
 
@@ -28,6 +29,7 @@ def __init__(self, model, image_processor):
             "pixel_values": None,
             "mask": None,
             "first_call": False,
+            "decoder_input_ids": None,
             "language_model_kwargs": {},
         }
 
@@ -64,6 +66,26 @@ def __call__(self, *args, **kwargs):
         """
         if self.pixel_values is not None and not self.first_call:
             self.first_call = True
+
+            # taken from here https://github.com/Blaizzy/mlx-vlm/blob/2974401/mlx_vlm/utils.py#L987
+            if hasattr(self.language_model, "make_cache"):
+                cache = self.language_model.make_cache()
+            else:
+                kv_heads = (
+                    [self.language_model.n_kv_heads] * len(self.language_model.layers)
+                    if isinstance(self.language_model.n_kv_heads, int)
+                    else self.language_model.n_kv_heads
+                )
+                if self.vision_model.config.model_type == "florence2":
+                    cache = [
+                        (SimpleKVCache(), SimpleKVCache()) for n in self.language_model.layers
+                    ]
+                else:
+                    cache = [KVCache(self.language_model.head_dim, n) for n in kv_heads]
+
+            # Replace the mlx_lm cache with the one we created
+            kwargs["cache"] = cache
+
             outputs = self.vision_model(
                 self.input_ids,
                 self.pixel_values,
@@ -73,10 +95,12 @@ def __call__(self, *args, **kwargs):
                 aspect_ratio_ids=self.aspect_ratio_ids,
                 aspect_ratio_mask=self.aspect_ratio_mask,
                 cross_attention_mask=self.cross_attention_mask,
+                image_input_idx=self.image_input_idx,
+                image_masks=self.image_masks,
                 **kwargs,
             )
 
-            # taken from here https://github.com/Blaizzy/mlx-vlm/blob/904023515d06543be28e3cfe4dd73fa2fe89cac9/mlx_vlm/utils.py#L916-L924
+            # taken from here https://github.com/Blaizzy/mlx-vlm/blob/2974401/mlx_vlm/utils.py#L1045-L1056
             if outputs.cross_attention_states is not None:
                 self.language_model_kwargs = {
                     k: v
@@ -84,11 +108,35 @@ def __call__(self, *args, **kwargs):
                         ["cross_attention_states"], [outputs.cross_attention_states]
                     )
                 }
+            elif outputs.encoder_outputs is not None:
+                self.decoder_input_ids = self.input_ids
+                self.language_model_kwargs = {
+                    "decoder_input_ids": self.decoder_input_ids,
+                    "encoder_outputs": outputs.encoder_outputs,
+                }
+            
+            # Add the cache we created here to the language model kwargs
+            self.language_model_kwargs["cache"] = cache
         else:
             try:
-                outputs = self.vision_model.language_model(
-                    *args, mask=self.mask, **kwargs, **self.language_model_kwargs
-                )
+                del kwargs["cache"]  # Use the cache from self.language_model_kwargs
+
+                # taken from here https://github.com/Blaizzy/mlx-vlm/blob/2974401/mlx_vlm/utils.py#L1009
+                if "decoder_input_ids" in self.language_model_kwargs:
+                    self.language_model_kwargs["decoder_input_ids"] = mx.array([self.decoder_input_ids])
+                    outputs = self.language_model(
+                        **kwargs,
+                        **self.language_model_kwargs,
+                    )
+                else:
+                    outputs = self.language_model(
+                        *args,
+                        mask=self.mask,
+                        **kwargs,
+                        **self.language_model_kwargs,
+                    )
+
+
             except ValueError as e:
                 # Create a friendly error message if a user tries to use mllama without images
                 if "Cross attention states must be provided for layer" in str(e):
@@ -96,7 +144,12 @@ def __call__(self, *args, **kwargs):
                         "Using this model without any images attached is not supported yet."
                     )
                 raise e
+
         return outputs.logits
+    
+    def record_sampled_token(self, token) -> None:
+        # Adapted from here https://github.com/Blaizzy/mlx-vlm/blob/2974401/mlx_vlm/utils.py#L1064
+        self.decoder_input_ids = token
 
     def process_prompt_with_images(
         self,
@@ -125,6 +178,8 @@ def process_prompt_with_images(
             self.aspect_ratio_ids,
             self.aspect_ratio_mask,
             self.cross_attention_mask,
+            self.image_input_idx,
+            self.image_masks,
         ) = self._prepare_inputs(images_b64, prompt, processor)
 
     def _prepare_inputs(self, images_b64: list, prompt: str, processor):
@@ -135,16 +190,21 @@ def _prepare_inputs(self, images_b64: list, prompt: str, processor):
         pil_images = self._convert_to_pil(images_b64)
         resized_images = self._custom_resize(pil_images)
 
+        image_token_index = self.vision_model.config.image_token_index if hasattr(self.vision_model.config, "image_token_index") else None
+
         mask = None
         image_grid_thw = None
         image_sizes = None
         aspect_ratio_ids = None
         aspect_ratio_mask = None
         cross_attention_mask = None
+        image_input_idx = None
         if len(images_b64) == 0:
             return (mx.array(processor(text=prompt).input_ids), *(None,) * 7)
 
         if self.image_processor is not None:
+            if not isinstance(prompt, list):
+                prompt = [prompt]
             processor.pad_token = processor.eos_token
             text_chunks = [
                 [processor(chunk).input_ids for chunk in prompt.split("<image>")]
@@ -156,7 +216,7 @@ def _prepare_inputs(self, images_b64: list, prompt: str, processor):
             input_ids = []
             for chunks in text_chunks:
                 ids = (
-                    chunks[0] + [self.vision_model.config.image_token_index] + chunks[1]
+                    chunks[0] + [image_token_index] + chunks[1]
                 )
                 padding = [processor.pad_token_id] * (max_length - len(ids))
                 input_ids.append(mx.array(ids + padding))
@@ -168,15 +228,32 @@ def _prepare_inputs(self, images_b64: list, prompt: str, processor):
             ).astype(mx.int32)
         else:
             processor.tokenizer.pad_token = processor.tokenizer.eos_token
-            inputs = processor(
-                text=[prompt], images=resized_images, padding=True, return_tensors="mlx"
-            )
+            if hasattr(processor, "process"):
+                inputs = processor.process(
+                    text=prompt, images=resized_images, padding=True, return_tensors="mlx"
+                )
+            else:
+                inputs = processor(
+                    text=prompt, images=resized_images, padding=True, return_tensors="mlx"
+                )
+
+            if "images" in inputs:
+                inputs["pixel_values"] = inputs["images"]
+                inputs.pop("images")
             if isinstance(inputs["pixel_values"], list):
                 pixel_values = inputs["pixel_values"]
             else:
                 pixel_values = mx.array(inputs["pixel_values"])
             input_ids = mx.array(inputs["input_ids"])
-            mask = mx.array(inputs["attention_mask"])
+            mask = (
+                mx.array(inputs["attention_mask"]) if "attention_mask" in inputs else None
+            )
+            image_input_idx = inputs.get("image_input_idx", None)
+            if image_input_idx is not None:
+                image_input_idx = mx.array(image_input_idx)
+            image_masks = inputs.get("image_masks", None)
+            if image_masks is not None:
+                image_masks = mx.array(image_masks)
             image_sizes = inputs.get("image_sizes", None)
             if image_sizes is not None:
                 image_sizes = mx.array(image_sizes)
@@ -202,6 +279,8 @@ def _prepare_inputs(self, images_b64: list, prompt: str, processor):
             aspect_ratio_ids,
             aspect_ratio_mask,
             cross_attention_mask,
+            image_input_idx,
+            image_masks,
         )
 
     def _convert_to_pil(self, images_b64: List[str]):
diff --git a/requirements.txt b/requirements.txt
index ae633d4..eceb19c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,98 +4,83 @@ aiofiles==23.2.1 \
 aiohappyeyeballs==2.4.3 \
     --hash=sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586 \
     --hash=sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572
-aiohttp==3.10.10 \
-    --hash=sha256:007ec22fbc573e5eb2fb7dec4198ef8f6bf2fe4ce20020798b2eb5d0abda6138 \
-    --hash=sha256:00819de9e45d42584bed046314c40ea7e9aea95411b38971082cad449392b08c \
-    --hash=sha256:01948b1d570f83ee7bbf5a60ea2375a89dfb09fd419170e7f5af029510033d24 \
-    --hash=sha256:038f514fe39e235e9fef6717fbf944057bfa24f9b3db9ee551a7ecf584b5b480 \
-    --hash=sha256:03a42ac7895406220124c88911ebee31ba8b2d24c98507f4a8bf826b2937c7f2 \
-    --hash=sha256:05646ebe6b94cc93407b3bf34b9eb26c20722384d068eb7339de802154d61bc5 \
-    --hash=sha256:0631dd7c9f0822cc61c88586ca76d5b5ada26538097d0f1df510b082bad3411a \
-    --hash=sha256:0b00807e2605f16e1e198f33a53ce3c4523114059b0c09c337209ae55e3823a8 \
-    --hash=sha256:0e1b370d8007c4ae31ee6db7f9a2fe801a42b146cec80a86766e7ad5c4a259cf \
-    --hash=sha256:15ecd889a709b0080f02721255b3f80bb261c2293d3c748151274dfea93ac871 \
-    --hash=sha256:1b66ccafef7336a1e1f0e389901f60c1d920102315a56df85e49552308fc0486 \
-    --hash=sha256:1bbb122c557a16fafc10354b9d99ebf2f2808a660d78202f10ba9d50786384b9 \
-    --hash=sha256:1eb89d3d29adaf533588f209768a9c02e44e4baf832b08118749c5fad191781d \
-    --hash=sha256:258c5dd01afc10015866114e210fb7365f0d02d9d059c3c3415382ab633fcbcb \
-    --hash=sha256:2609e9ab08474702cc67b7702dbb8a80e392c54613ebe80db7e8dbdb79837c68 \
-    --hash=sha256:274cfa632350225ce3fdeb318c23b4a10ec25c0e2c880eff951a3842cf358ac1 \
-    --hash=sha256:28529e08fde6f12eba8677f5a8608500ed33c086f974de68cc65ab218713a59d \
-    --hash=sha256:2b606353da03edcc71130b52388d25f9a30a126e04caef1fd637e31683033abd \
-    --hash=sha256:30ca7c3b94708a9d7ae76ff281b2f47d8eaf2579cd05971b5dc681db8caac6e1 \
-    --hash=sha256:333cf6cf8e65f6a1e06e9eb3e643a0c515bb850d470902274239fea02033e9a8 \
-    --hash=sha256:3455522392fb15ff549d92fbf4b73b559d5e43dc522588f7eb3e54c3f38beee7 \
-    --hash=sha256:362f641f9071e5f3ee6f8e7d37d5ed0d95aae656adf4ef578313ee585b585959 \
-    --hash=sha256:3bcd391d083f636c06a68715e69467963d1f9600f85ef556ea82e9ef25f043f7 \
-    --hash=sha256:3dffb610a30d643983aeb185ce134f97f290f8935f0abccdd32c77bed9388b42 \
-    --hash=sha256:3fe407bf93533a6fa82dece0e74dbcaaf5d684e5a51862887f9eaebe6372cd79 \
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-    --hash=sha256:ffe595f10566f8276b76dc3a11ae4bb7eba1aac8ddd75811736a15b0d5311414
+aiohttp==3.11.7 \
+    --hash=sha256:018f1b04883a12e77e7fc161934c0f298865d3a484aea536a6a2ca8d909f0ba0 \
+    --hash=sha256:01a8aca4af3da85cea5c90141d23f4b0eee3cbecfd33b029a45a80f28c66c668 \
+    --hash=sha256:04b0cc74d5a882c9dacaeeccc1444f0233212b6f5be8bc90833feef1e1ce14b9 \
+    --hash=sha256:0de6466b9d742b4ee56fe1b2440706e225eb48c77c63152b1584864a236e7a50 \
+    --hash=sha256:12724f3a211fa243570e601f65a8831372caf1a149d2f1859f68479f07efec3d \
+    --hash=sha256:12e4d45847a174f77b2b9919719203769f220058f642b08504cf8b1cf185dacf \
+    --hash=sha256:17829f37c0d31d89aa6b8b010475a10233774771f9b6dc2cc352ea4f8ce95d9a \
+    --hash=sha256:1a17f6a230f81eb53282503823f59d61dff14fb2a93847bf0399dc8e87817307 \
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+    --hash=sha256:24054fce8c6d6f33a3e35d1c603ef1b91bbcba73e3f04a22b4f2f27dac59b347 \
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+    --hash=sha256:9acfc7f652b31853eed3b92095b0acf06fd5597eeea42e939bd23a17137679d5 \
+    --hash=sha256:9d18a8b44ec8502a7fde91446cd9c9b95ce7c49f1eacc1fb2358b8907d4369fd \
+    --hash=sha256:9e67531370a3b07e49b280c1f8c2df67985c790ad2834d1b288a2f13cd341c5f \
+    --hash=sha256:9ee6a4cdcbf54b8083dc9723cdf5f41f722c00db40ccf9ec2616e27869151129 \
+    --hash=sha256:a7d9a606355655617fee25dd7e54d3af50804d002f1fd3118dd6312d26692d70 \
+    --hash=sha256:aa3705a8d14de39898da0fbad920b2a37b7547c3afd2a18b9b81f0223b7d0f68 \
+    --hash=sha256:b7215bf2b53bc6cb35808149980c2ae80a4ae4e273890ac85459c014d5aa60ac \
+    --hash=sha256:badda65ac99555791eed75e234afb94686ed2317670c68bff8a4498acdaee935 \
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+    --hash=sha256:c1f6490dd1862af5aae6cfcf2a274bffa9a5b32a8f5acb519a7ecf5a99a88866 \
+    --hash=sha256:c25b74a811dba37c7ea6a14d99eb9402d89c8d739d50748a75f3cf994cf19c43 \
+    --hash=sha256:c6095aaf852c34f42e1bd0cf0dc32d1e4b48a90bfb5054abdbb9d64b36acadcb \
+    --hash=sha256:c63f898f683d1379b9be5afc3dd139e20b30b0b1e0bf69a3fc3681f364cf1629 \
+    --hash=sha256:cd8d62cab363dfe713067027a5adb4907515861f1e4ce63e7be810b83668b847 \
+    --hash=sha256:ce91a24aac80de6be8512fb1c4838a9881aa713f44f4e91dd7bb3b34061b497d \
+    --hash=sha256:cea52d11e02123f125f9055dfe0ccf1c3857225fb879e4a944fae12989e2aef2 \
+    --hash=sha256:cf4efa2d01f697a7dbd0509891a286a4af0d86902fc594e20e3b1712c28c0106 \
+    --hash=sha256:d2fa6fc7cc865d26ff42480ac9b52b8c9b7da30a10a6442a9cdf429de840e949 \
+    --hash=sha256:d329300fb23e14ed1f8c6d688dfd867d1dcc3b1d7cd49b7f8c5b44e797ce0932 \
+    --hash=sha256:d6177077a31b1aecfc3c9070bd2f11419dbb4a70f30f4c65b124714f525c2e48 \
+    --hash=sha256:db37248535d1ae40735d15bdf26ad43be19e3d93ab3f3dad8507eb0f85bb8124 \
+    --hash=sha256:db70a47987e34494b451a334605bee57a126fe8d290511349e86810b4be53b01 \
+    --hash=sha256:dcefcf2915a2dbdbce37e2fc1622129a1918abfe3d06721ce9f6cdac9b6d2eaa \
+    --hash=sha256:dda3ed0a7869d2fa16aa41f9961ade73aa2c2e3b2fcb0a352524e7b744881889 \
+    --hash=sha256:e0bf378db07df0a713a1e32381a1b277e62ad106d0dbe17b5479e76ec706d720 \
+    --hash=sha256:e13a05db87d3b241c186d0936808d0e4e12decc267c617d54e9c643807e968b6 \
+    --hash=sha256:e143b0ef9cb1a2b4f74f56d4fbe50caa7c2bb93390aff52f9398d21d89bc73ea \
+    --hash=sha256:e22d1721c978a6494adc824e0916f9d187fa57baeda34b55140315fa2f740184 \
+    --hash=sha256:e5522ee72f95661e79db691310290c4618b86dff2d9b90baedf343fd7a08bf79 \
+    --hash=sha256:e993676c71288618eb07e20622572b1250d8713e7e00ab3aabae28cb70f3640d \
+    --hash=sha256:ee9afa1b0d2293c46954f47f33e150798ad68b78925e3710044e0d67a9487791 \
+    --hash=sha256:f1ac5462582d6561c1c1708853a9faf612ff4e5ea5e679e99be36143d6eabd8e \
+    --hash=sha256:f5022504adab881e2d801a88b748ea63f2a9d130e0b2c430824682a96f6534be \
+    --hash=sha256:f5b973cce96793725ef63eb449adfb74f99c043c718acb76e0d2a447ae369962 \
+    --hash=sha256:f7c58a240260822dc07f6ae32a0293dd5bccd618bb2d0f36d51c5dbd526f89c0 \
+    --hash=sha256:fc6da202068e0a268e298d7cd09b6e9f3997736cd9b060e2750963754552a0a9 \
+    --hash=sha256:fdadc3f6a32d6eca45f9a900a254757fd7855dfb2d8f8dcf0e88f0fae3ff8eb1
 aiosignal==1.3.1 \
     --hash=sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc \
     --hash=sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17
@@ -340,17 +325,17 @@ frozenlist==1.5.0 \
 fsspec==2024.9.0 \
     --hash=sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8 \
     --hash=sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b
-gradio==5.5.0 \
-    --hash=sha256:48ea11ae1376d9506eb295dbeb4d3ff962830a6e2d8aa9085e025cce3c04e544
-gradio-client==1.4.2 \
-    --hash=sha256:07c5184ad6385f50f9e6eae3a262ad6afcf9380b6a400d070652f76e14222dbd \
-    --hash=sha256:76a34996b4580939a3fbd10144fde04eced586c033b6cdcb315d69b1459136d1
+gradio==5.6.0 \
+    --hash=sha256:6eba135b32fb2fcb5d83fa845f6ad57c033894d5218fd3010de8fb4d735b9b6d
+gradio-client==1.4.3 \
+    --hash=sha256:55fd446ae80a5320f164b6913e8862b398fca9097d33b76bd98efb61cbd449c3 \
+    --hash=sha256:a76990277cab011f1c49391bdf33670bbbbb32b3503971ae5d91281e7946bdc1
 h11==0.14.0 \
     --hash=sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d \
     --hash=sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761
-httpcore==1.0.6 \
-    --hash=sha256:27b59625743b85577a8c0e10e55b50b5368a4f2cfe8cc7bcfa9cf00829c2682f \
-    --hash=sha256:73f6dbd6eb8c21bbf7ef8efad555481853f5f6acdeaff1edb0694289269ee17f
+httpcore==1.0.7 \
+    --hash=sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c \
+    --hash=sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd
 httpx==0.27.2 \
     --hash=sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0 \
     --hash=sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2
@@ -442,22 +427,23 @@ markupsafe==2.1.5 \
 mdurl==0.1.2 \
     --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
     --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
-mlx==0.20.0 \
-    --hash=sha256:0a289347e32526dfb101ddc1305af645804c2f5fae9e67f4b1ff9f362dbaa919 \
-    --hash=sha256:31fb25c159d1cbfbcfe2e4259d8759abde39fe132f03e4b8ffe0c32d5de4c6a8 \
-    --hash=sha256:342830592790a233e514ad30908739773da4526b6c4eb0d7d540e6450ebf415b \
-    --hash=sha256:7d6fc39d36464d07fca0469619b01eb08e6dda19610f26bbe03703079c445abf \
-    --hash=sha256:8630277a508c58d70193dc40b87df821f51093f9baa36ee4d8984aad9f0eed91 \
-    --hash=sha256:88c34853c34d669a6a5351e055cf98ef0b2c761f833003516797687c5f935bbb \
-    --hash=sha256:8ecf314db6ed2b702f85b20a3f2fc675e4511a1a4ec35b5f83f7db9adb039be4 \
-    --hash=sha256:97e43df023cb379e0c2b94a3a3f3ad92e1e41f69c0dbb9b94a503b28e00866dc \
-    --hash=sha256:ad037707762ea0ffe74bcf297d3a349cd151e981930b8465f951fd0fef11d365 \
-    --hash=sha256:d1274409c32e57959986a58199cf393ec7f75f5b01fc2da5928bc4cc76c3042a
-mlx-lm==0.19.3 \
-    --hash=sha256:1d2b2f26f6262f4b8eb816d2c39b9ecbe6d0334bde0e6b5db141e101a8e94a34 \
-    --hash=sha256:d59b92676b89c8c7dbffdce2c4214d67557a031989034752ae37e9f047f4c1fa
-mlx-vlm @ https://github.com/Blaizzy/mlx-vlm/archive/4b95eeeb4414e5baad71fec7cdfa6a9c372cce6b.zip \
-    --hash=sha256:532764fcccbaf8e7606c86451d56b128248ed4c5ea05fea1fe8d5e715d7a86c2
+mlx==0.21.0 \
+    --hash=sha256:18609008b0d51b22b400df967a702163c7501fecc8cc4b861d95ca147177cd1f \
+    --hash=sha256:310eabbb526f88567d2d931db8b9e830926046e3ad0b062f43f99131576f90c2 \
+    --hash=sha256:3155918a74c5cdb87839e999b069b887043166b1370946a4ef4c633b555861ae \
+    --hash=sha256:43fadf0c5975d9dacdaa54e34170705dd70706b086b1cb3aec56725373e703f7 \
+    --hash=sha256:7467bda3a107e278f87451b2b06cc58285c3e56124f52721ae2d7ebdadd6af59 \
+    --hash=sha256:75f80f3dd515b75dbc24609e1000e965b956a18acb1f461da615f2fcdffd6ea0 \
+    --hash=sha256:7ab901a29c7af9942b9139c6b0780ca5fdbb46210a3e1ef4c2ba3c33d717ab68 \
+    --hash=sha256:853848109ed5e6894ac72c2609cebe108f457597703154dcc0ac26945c89bcde \
+    --hash=sha256:86c8d7c89887826f074ab86a429f7c5508258eeda9fab4d02ca23c3e43d7e494 \
+    --hash=sha256:e7b6a7d9411cc9a9cfdffeb69a8367bb7ec01bd56c1a528e35895dea83d1ce89
+mlx-lm==0.20.1 \
+    --hash=sha256:a88d5ee6719d0baec55be4a08f7ba16f5590a47af5ce72263adaa4143b9f0e56 \
+    --hash=sha256:c3358d94de0c6a7548d2766a54eb7203097953cfcbd50e25cabdd029e7d544dc
+mlx-vlm==0.1.1 \
+    --hash=sha256:37aa06698ccb35f21afa716c4ebbe14bcc8b20b14feab4d49f216b40e56d806c \
+    --hash=sha256:f04a87ee5b7f7422d3f5b99dbc8adca619757b68ce672ff7ad7db62a76c2dccd
 mpmath==1.3.0 \
     --hash=sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f \
     --hash=sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c
@@ -610,101 +596,132 @@ numpy==1.26.4 \
     --hash=sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef \
     --hash=sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3 \
     --hash=sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f
-orjson==3.10.11 \
-    --hash=sha256:03246774131701de8e7059b2e382597da43144a9a7400f178b2a32feafc54bd5 \
-    --hash=sha256:0efabbf839388a1dab5b72b5d3baedbd6039ac83f3b55736eb9934ea5494d258 \
-    --hash=sha256:10f416b2a017c8bd17f325fb9dee1fb5cdd7a54e814284896b7c3f2763faa017 \
-    --hash=sha256:1444f9cb7c14055d595de1036f74ecd6ce15f04a715e73f33bb6326c9cef01b6 \
-    --hash=sha256:1789d9db7968d805f3d94aae2c25d04014aae3a2fa65b1443117cd462c6da647 \
-    --hash=sha256:19b3763e8bbf8ad797df6b6b5e0fc7c843ec2e2fc0621398534e0c6400098f87 \
-    --hash=sha256:1a1222ffcee8a09476bbdd5d4f6f33d06d0d6642df2a3d78b7a195ca880d669b \
-    --hash=sha256:1be83a13312e5e58d633580c5eb8d0495ae61f180da2722f20562974188af205 \
-    --hash=sha256:1f39728c7f7d766f1f5a769ce4d54b5aaa4c3f92d5b84817053cc9995b977acc \
-    --hash=sha256:360a4e2c0943da7c21505e47cf6bd725588962ff1d739b99b14e2f7f3545ba51 \
-    --hash=sha256:461311b693d3d0a060439aa669c74f3603264d4e7a08faa68c47ae5a863f352d \
-    --hash=sha256:496e2cb45de21c369079ef2d662670a4892c81573bcc143c4205cae98282ba97 \
-    --hash=sha256:4bfb30c891b530f3f80e801e3ad82ef150b964e5c38e1fb8482441c69c35c61c \
-    --hash=sha256:4d83f87582d223e54efb2242a79547611ba4ebae3af8bae1e80fa9a0af83bb7f \
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-    --hash=sha256:dfbb2d460a855c9744bbc8e36f9c3a997c4b27d842f3d5559ed54326e6911f9b \
-    --hash=sha256:e2f3b7c5803138e67028dde33450e054c87e0703afbe730c105f1fcd873496d5 \
-    --hash=sha256:e35b6d730de6384d5b2dab5fd23f0d76fae8bbc8c353c2f78210aa5fa4beb3ef \
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-    --hash=sha256:f91d9eb554310472bd09f5347950b24442600594c2edc1421403d7610a0998fd
-outlines==0.1.1 \
-    --hash=sha256:896aee7f8f0472955104bb30fb118e525bced6885f09e833bb848782394f2c17 \
-    --hash=sha256:9c5d3524ef21343bd681757e8ed9a5b1fcb335ee68f9b6b0889062ce23b561fc
-outlines-core==0.1.14 \
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-    --hash=sha256:fd8f1e1d91a206a520d1c577ce00136de2beb1d200ef93759fd4c9f45abe24d3
-packaging==24.1 \
-    --hash=sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002 \
-    --hash=sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124
+orjson==3.10.12 \
+    --hash=sha256:0000758ae7c7853e0a4a6063f534c61656ebff644391e1f81698c1b2d2fc8cd2 \
+    --hash=sha256:038d42c7bc0606443459b8fe2d1f121db474c49067d8d14c6a075bbea8bf14dd \
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+    --hash=sha256:f930e9c0bf5939047a76aee23cf984a83aaf859aedd988ae5249134ff8a2577b
+packaging==24.2 \
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 pandas==2.2.3 \
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@@ -1146,230 +1163,217 @@ pyyaml==6.0.2 \
 referencing==0.35.1 \
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+    --hash=sha256:f983e4c2f603c95dde63df633eec42955508eefd8d0f0e6d236d31a044c882d7 \
+    --hash=sha256:faa5e8496c530f9c71f2b4e1c49758b06e5f4055e17144906245c99fa6d45356 \
+    --hash=sha256:fed5dfefdf384d6fe975cc026886aece4f292feaf69d0eeb716cfd3c5a4dd8be
+ruff==0.8.0 \
+    --hash=sha256:0e55cce9aa93c5d0d4e3937e47b169035c7e91c8655b0974e61bb79cf398d49c \
+    --hash=sha256:295bb4c02d58ff2ef4378a1870c20af30723013f441c9d1637a008baaf928c8b \
+    --hash=sha256:2dabfd05b96b7b8f2da00d53c514eea842bff83e41e1cceb08ae1966254a51df \
+    --hash=sha256:3f4cd64916d8e732ce6b87f3f5296a8942d285bbbc161acee7fe561134af64f9 \
+    --hash=sha256:582891c57b96228d146725975fbb942e1f30a0c4ba19722e692ca3eb25cc9b4f \
+    --hash=sha256:5fdb6efecc3eb60bba5819679466471fd7d13c53487df7248d6e27146e985468 \
+    --hash=sha256:780d5d8523c04202184405e60c98d7595bdb498c3c6abba3b6d4cdf2ca2af426 \
+    --hash=sha256:7b1f1c76b47c18fa92ee78b60d2d20d7e866c55ee603e7d19c1e991fad933a9a \
+    --hash=sha256:812e2052121634cf13cd6fddf0c1871d0ead1aad40a1a258753c04c18bb71bbd \
+    --hash=sha256:83a55679c4cb449fa527b8497cadf54f076603cc36779b2170b24f704171ce70 \
+    --hash=sha256:85e654f0ded7befe2d61eeaf3d3b1e4ef3894469cd664ffa85006c7720f1e4a2 \
+    --hash=sha256:87a8e86bae0dbd749c815211ca11e3a7bd559b9710746c559ed63106d382bd9c \
+    --hash=sha256:a7ccfe6331bf8c8dad715753e157457faf7351c2b69f62f32c165c2dbcbacd44 \
+    --hash=sha256:ba93e6294e9a737cd726b74b09a6972e36bb511f9a102f1d9a7e1ce94dd206a6 \
+    --hash=sha256:c5c1466be2a2ebdf7c5450dd5d980cc87c8ba6976fb82582fea18823da6fa362 \
+    --hash=sha256:eb0d4f250a7711b67ad513fde67e8870109e5ce590a801c3722580fe98c33a99 \
+    --hash=sha256:facebdfe5a5af6b1588a1d26d170635ead6892d0e314477e80256ef4a8470cf3 \
+    --hash=sha256:fcb1bf2cc6706adae9d79c8d86478677e3bbd4ced796ccad106fd4776d395fea
 safehttpx==0.1.1 \
     --hash=sha256:1d93b64023c00d5c53ea70ea36e773b8a0dba5eaf1a1eb188856584a0a4cf4d1 \
     --hash=sha256:6e1bedf7767213300da5e4cb7e823e98edc934f17ca192d2e585111a2b899149
@@ -1716,15 +1720,15 @@ torch==2.5.1 \
     --hash=sha256:9b61edf3b4f6e3b0e0adda8b3960266b9009d02b37555971f4d1c8f7a05afed7 \
     --hash=sha256:de5b7d6740c4b636ef4db92be922f0edc425b65ed78c5076c43c42d362a45457 \
     --hash=sha256:ed231a4b3a5952177fafb661213d690a72caaad97d5824dd4fc17ab9e15cec03
-tqdm==4.66.6 \
-    --hash=sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63 \
-    --hash=sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090
-transformers==4.46.2 \
-    --hash=sha256:3d85410881e1c074be767877bf33c83231ec11529f274a6044ecb20c157ba14e \
-    --hash=sha256:c921f4406b78e6518c97b618c5acd1cf8a4f2315b6b727f4bf9e01496eef849c
-typer==0.12.5 \
-    --hash=sha256:62fe4e471711b147e3365034133904df3e235698399bc4de2b36c8579298d52b \
-    --hash=sha256:f592f089bedcc8ec1b974125d64851029c3b1af145f04aca64d69410f0c9b722
+tqdm==4.67.1 \
+    --hash=sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2 \
+    --hash=sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2
+transformers==4.46.3 \
+    --hash=sha256:8ee4b3ae943fe33e82afff8e837f4b052058b07ca9be3cb5b729ed31295f72cc \
+    --hash=sha256:a12ef6f52841fd190a3e5602145b542d03507222f2c64ebb7ee92e8788093aef
+typer==0.13.1 \
+    --hash=sha256:5b59580fd925e89463a29d363e0a43245ec02765bde9fb77d39e5d0f29dd7157 \
+    --hash=sha256:9d444cb96cc268ce6f8b94e13b4335084cef4c079998a9f4851a90229a3bd25c
 typing-extensions==4.12.2 \
     --hash=sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d \
     --hash=sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8
@@ -1734,9 +1738,9 @@ tzdata==2024.2 \
 urllib3==2.2.3 \
     --hash=sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac \
     --hash=sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9
-uvicorn==0.32.0 \
-    --hash=sha256:60b8f3a5ac027dcd31448f411ced12b5ef452c646f76f02f8cc3f25d8d26fd82 \
-    --hash=sha256:f78b36b143c16f54ccdb8190d0a26b5f1901fe5a3c777e1ab29f26391af8551e
+uvicorn==0.32.1 \
+    --hash=sha256:82ad92fd58da0d12af7482ecdb5f2470a04c9c9a53ced65b9bbb4a205377602e \
+    --hash=sha256:ee9519c246a72b1c084cea8d3b44ed6026e78a4a309cbedae9c37e4cb9fbb175
 websockets==12.0 \
     --hash=sha256:00700340c6c7ab788f176d118775202aadea7602c5cc6be6ae127761c16d6b0b \
     --hash=sha256:0bee75f400895aef54157b36ed6d3b308fcab62e5260703add87f44cee9c82a6 \
@@ -1934,89 +1938,1550 @@ xxhash==3.5.0 \
     --hash=sha256:fab81ef75003eda96239a23eda4e4543cedc22e34c373edcaf744e721a163986 \
     --hash=sha256:fd1b2281d01723f076df3c8188f43f2472248a6b63118b036e641243656b1b0f \
     --hash=sha256:fe1a92cfbaa0a1253e339ccec42dbe6db262615e52df591b68726ab10338003f
-yarl==1.17.1 \
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-    --hash=sha256:067a63fcfda82da6b198fa73079b1ca40b7c9b7994995b6ee38acda728b64d47 \
-    --hash=sha256:0b1794853124e2f663f0ea54efb0340b457f08d40a1cef78edfa086576179c91 \
-    --hash=sha256:0bdff5e0995522706c53078f531fb586f56de9c4c81c243865dd5c66c132c3b5 \
-    --hash=sha256:117ed8b3732528a1e41af3aa6d4e08483c2f0f2e3d3d7dca7cf538b3516d93df \
-    --hash=sha256:14bc88baa44e1f84164a392827b5defb4fa8e56b93fecac3d15315e7c8e5d8b3 \
-    --hash=sha256:1654ec814b18be1af2c857aa9000de7a601400bd4c9ca24629b18486c2e35463 \
-    --hash=sha256:16bca6678a83657dd48df84b51bd56a6c6bd401853aef6d09dc2506a78484c7b \
-    --hash=sha256:1a3b91c44efa29e6c8ef8a9a2b583347998e2ba52c5d8280dbd5919c02dfc3b5 \
-    --hash=sha256:1a52a1ffdd824fb1835272e125385c32fd8b17fbdefeedcb4d543cc23b332d74 \
-    --hash=sha256:1ce36ded585f45b1e9bb36d0ae94765c6608b43bd2e7f5f88079f7a85c61a4d3 \
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-    --hash=sha256:ff5c6771c7e3511a06555afa317879b7db8d640137ba55d6ab0d0c50425cab75
+yarl==1.18.0 \
+    --hash=sha256:01be8688fc211dc237e628fcc209dda412d35de7642453059a0553747018d075 \
+    --hash=sha256:039c299a0864d1f43c3e31570045635034ea7021db41bf4842693a72aca8df3a \
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+    --hash=sha256:14408cc4d34e202caba7b5ac9cc84700e3421a9e2d1b157d744d101b061a4a88 \
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+    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
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+    --hash=sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc
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+    --hash=sha256:61c722ed8d49ac9bc26c7071eeaa1f6ff24053d553146d5df031802deffd03da \
+    --hash=sha256:63107013578c8a730419adc05608756c3fa640bdc6abe806c3123a49fb829f43 \
+    --hash=sha256:683b94dbd1ca67557850b86423318a2e323511648f9f3f7b1840408a02b9a48c \
+    --hash=sha256:685c4f4e8c59837de103344eb1c8a3851f670309eb5c361f746805c5471b8c88 \
+    --hash=sha256:695735deeddfb35da1677dbc16a083445360e37ff46d8ac5c6fcd64917ff9ade \
+    --hash=sha256:6e5f70f6dca1d3b09bccb7daf4e087075ff776e3da9ac870f86ca316736bb4aa \
+    --hash=sha256:6e93a5ad22f434d7876665444a97e713a8f60b5b1a3521e8df11b98309bff833 \
+    --hash=sha256:6fa0b72f2423e2aa53077e54a61c28e181d23effeaafd73fcb9c494e60930c8e \
+    --hash=sha256:70dabf941dede727cca579e8c205e61121afc9b28516752fd65724be1355cc90 \
+    --hash=sha256:74752ecaa544657d88b1d1c94ae68031e364a4d47005a90288f3bab3da3c970f \
+    --hash=sha256:7a46e1d6d2817ba8024de44c4fd79913a90e5f7265434cef97026215b7d30df6 \
+    --hash=sha256:7c5d3e570ef46adaf93fc81b44aca6002b5a4d8ca11bd0580c07eac537f36680 \
+    --hash=sha256:7cb29a034301e2982df8b1fe6328a84f4b676106a13e9135a0d7e0c3e9f806da \
+    --hash=sha256:7ccb800c9418e438b44b060a32adeb8393764da7441eb52aa2aa195448935306 \
+    --hash=sha256:7ce379bcaa9fcc00f19affa7773084dd09f5b59947b3fb47a1ceb0179f91aaa1 \
+    --hash=sha256:7f85e0108d51092bdda90672476c7d909c04ada6923c14ff9d913c4f7dc8a3bc \
+    --hash=sha256:80babcc30e7a1a484eab952d76a4f4673ff601f54d5142c26826502740e70b43 \
+    --hash=sha256:82085c2abec437abebf457c1d12fccb30cc8b3774a0814872511f0f0562c768c \
+    --hash=sha256:82b833d5563fefd6fceafb1aed2f3f3ebe19f84760fdd289f8b926731c2e6e91 \
+    --hash=sha256:84f2caddf951c9cbf8dc2e22a89d4ccf5d86391ac6418fe81e3c67d0cf60b45f \
+    --hash=sha256:893074d651cf25c1cc14e3bea4fceefd67f2921b1bb8e40fcfeba56820de80c6 \
+    --hash=sha256:89997aa1c4b6a5b1e5b588979d1da048a3c6f15e55c11d117a56b75c84531f5a \
+    --hash=sha256:89e66ceed67b213dec5a773e2f7a9e8c58f64daeb38c7859d8815d2c89f39ad7 \
+    --hash=sha256:8d47ebd9f5d9607fd039c1fbf4994e3b071ea23eff42f4ecef246ab2b7334198 \
+    --hash=sha256:924361811732ddad75ff23e90efd9ccfda4f664132feecb90895bade6a1b4623 \
+    --hash=sha256:963be41bcd49f53af6d795f65c0da9b4cc518c0dd9c47145c98f61cb464f4839 \
+    --hash=sha256:97a662338797c660178e682f3bc180277b9569a59abfb5925e8620fba00b9fc5 \
+    --hash=sha256:9bed5144c6923cc902cd14bb8963f2d5e034def4486ab0bbe1f58f03f042f9a9 \
+    --hash=sha256:9c770750cc80e8694492244bca7251385188bc5597b6a39d98a9f30e8da984e0 \
+    --hash=sha256:9d32a592cac88d18cc09a89172e1c32d7f2a6e516c3dfde1b9adb90ab5df54a6 \
+    --hash=sha256:a5bc08f33c4966f4eb6590d6ff3ceae76151ad744576b5fc6c4ba8edd459fdec \
+    --hash=sha256:a606c8070ada8aa2a88e181773fa1ef17ba65ce5dd168b9d08038e2a61b33754 \
+    --hash=sha256:a6c50017518329ed65a9e4829154626f008916d36295b6a3ba336e2458824c8c \
+    --hash=sha256:a7b1d8315d9b5e9f89eb2933b73afae6ec9597a258d52190944437158b49d38e \
+    --hash=sha256:a8fb786fb754ef6ff8c120cb96629fb518f8eb5a61a16aac3a979a9dbd40a084 \
+    --hash=sha256:a905ad00ad1e1c34fe4e9d7c1d949ab09c6fa90c919860c1534ff479f40fd12d \
+    --hash=sha256:a9d360a792cbcce2fe7b66b8d51274ec297c53cbc423401480e53b26161a290d \
+    --hash=sha256:b150b8467852e1bd844387459aa6fbe11d7f38b56e901f9f3b3e6aba0d660240 \
+    --hash=sha256:b702f806693201ad6c0a05ddbbe4c8f359626d0b3305f766077d51388a6bac58 \
+    --hash=sha256:b96d559e0fcddd3343c510a0fe2b127fbff16bf346dd76280b82292567523442 \
+    --hash=sha256:bcd51708a633410737111e998ceb3b45d3dbc98c0931f743d9bb0a209033a326 \
+    --hash=sha256:bfc8cdd7f33d57f0468b0614ae634cc38ab9202c6957a60e31d285a71ebe0301 \
+    --hash=sha256:c0342aafd421795d740e514bc9858ebddfc705a75a8c5046ac56d85fe97bf196 \
+    --hash=sha256:c279f0d2b34ef15f922b77966640ade58b4ccdfef1c4d94b20f2a364617a493f \
+    --hash=sha256:c28b2fdcee797e1c1961cd3bcd3d545cab22ad202c846235197935e1df2f8ef7 \
+    --hash=sha256:c3bc7bf8cb8806f8d1c9bf149c18708cb1c406520097d6b0a73977460ea03602 \
+    --hash=sha256:c4dcb4120d0cc3cc448624147dba64e9021b278c63e34a38789b688fd0da9bf3 \
+    --hash=sha256:c8aa771ff2c13dd9cda8166d685d7333d389fae30a4d2bb39d63ab5775de8606 \
+    --hash=sha256:cc1276d369452040cbb943300dc8abeedab14245ea44056a2943183822513a18 \
+    --hash=sha256:cd2fd827b0ba763ac919440042302315c564fdb797294d86e8cdd4578e3bc7f3 \
+    --hash=sha256:d30bbc1644f726b825b3278764240f449d75f1a8bdda892e641d4a688b1494ae \
+    --hash=sha256:d5e9db7ef3ecbfc0b4733579cea45713a76852b002cf605420b12ef3ef1ec148 \
+    --hash=sha256:dbd2ecfbfee70bc1a4acb7461fa6af7748ec2ab08ac0fa298f281c51518f982c \
+    --hash=sha256:dd86b8e7f703ec6ff4f351cfdb9f428955859537125904aa8c963604f2e9d3e7 \
+    --hash=sha256:dee1316133c9b463aa81aca676bc506d3f80d8f65aeb0bba2b78d0b30c51d7bd \
+    --hash=sha256:e0c48b6300cd0b0106bf49169c3e0536408dfbeb1ccb53180068a18b03c662ab \
+    --hash=sha256:e5d0ddaca65ecca9c10dcf01730165fd858533d0be84c75c327487c37a906a27 \
+    --hash=sha256:e6a4dd644d72ab316b580a1c120b375890e4c52ec392d4aef3c63361ec4d77d1 \
+    --hash=sha256:eade977f5c96c677035ff39c56ac74d851b1cca7d607ab3d8f23c6b859379cab \
+    --hash=sha256:ec28adb204b759306a3d64358a5e5c07d7b1dd0ccbce04aa76cb9377b7b70296 \
+    --hash=sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212 \
+    --hash=sha256:eefc37f6138f522e771ac6db71a6d4838ec7933939676f3753eafd7d3f4c40bc \
+    --hash=sha256:f0b48edbebea1b7421a9c687c304f7b44d0677c46498a046079d445454504737 \
+    --hash=sha256:f1abffa122452481a61c3551ab3c89d72238e279e517705b8b03847b1d93d738 \
+    --hash=sha256:f2f2c61bee5844d41c3eb015ac652a0229e901074951ae48581d58bfb2ba01be \
+    --hash=sha256:f7b58d1fd3551b8c80a971199543379be1cee3d0d409e1f6d8b01c1a2eebf1f8 \
+    --hash=sha256:fa0cafd3a2af231b4e113fba24a65d7922af91aeb23774a8b78228e6cd785e3e \
+    --hash=sha256:fa9f547bd98f5553d03160967866a71056a60960be00356a15ecc44efb40ba8e \
+    --hash=sha256:fab81ef75003eda96239a23eda4e4543cedc22e34c373edcaf744e721a163986 \
+    --hash=sha256:fd1b2281d01723f076df3c8188f43f2472248a6b63118b036e641243656b1b0f \
+    --hash=sha256:fe1a92cfbaa0a1253e339ccec42dbe6db262615e52df591b68726ab10338003f
+yarl==1.18.0 \
+    --hash=sha256:01be8688fc211dc237e628fcc209dda412d35de7642453059a0553747018d075 \
+    --hash=sha256:039c299a0864d1f43c3e31570045635034ea7021db41bf4842693a72aca8df3a \
+    --hash=sha256:074fee89caab89a97e18ef5f29060ef61ba3cae6cd77673acc54bfdd3214b7b7 \
+    --hash=sha256:13aaf2bdbc8c86ddce48626b15f4987f22e80d898818d735b20bd58f17292ee8 \
+    --hash=sha256:14408cc4d34e202caba7b5ac9cc84700e3421a9e2d1b157d744d101b061a4a88 \
+    --hash=sha256:1db1537e9cb846eb0ff206eac667f627794be8b71368c1ab3207ec7b6f8c5afc \
+    --hash=sha256:1ece25e2251c28bab737bdf0519c88189b3dd9492dc086a1d77336d940c28ced \
+    --hash=sha256:1ff116f0285b5c8b3b9a2680aeca29a858b3b9e0402fc79fd850b32c2bcb9f8b \
+    --hash=sha256:205de377bd23365cd85562c9c6c33844050a93661640fda38e0567d2826b50df \
+    --hash=sha256:20d95535e7d833889982bfe7cc321b7f63bf8879788fee982c76ae2b24cfb715 \
+    --hash=sha256:20de4a8b04de70c49698dc2390b7fd2d18d424d3b876371f9b775e2b462d4b41 \
+    --hash=sha256:2d90f2e4d16a5b0915ee065218b435d2ef619dd228973b1b47d262a6f7cd8fa5 \
+    --hash=sha256:2e6b4466714a73f5251d84b471475850954f1fa6acce4d3f404da1d55d644c34 \
+    --hash=sha256:309f8d27d6f93ceeeb80aa6980e883aa57895270f7f41842b92247e65d7aeddf \
+    --hash=sha256:32141e13a1d5a48525e519c9197d3f4d9744d818d5c7d6547524cc9eccc8971e \
+    --hash=sha256:34176bfb082add67cb2a20abd85854165540891147f88b687a5ed0dc225750a0 \
+    --hash=sha256:38b39b7b3e692b6c92b986b00137a3891eddb66311b229d1940dcbd4f025083c \
+    --hash=sha256:3a3709450a574d61be6ac53d582496014342ea34876af8dc17cc16da32826c9a \
+    --hash=sha256:3adaaf9c6b1b4fc258584f4443f24d775a2086aee82d1387e48a8b4f3d6aecf6 \
+    --hash=sha256:3f576ed278860df2721a5d57da3381040176ef1d07def9688a385c8330db61a1 \
+    --hash=sha256:42ba84e2ac26a3f252715f8ec17e6fdc0cbf95b9617c5367579fafcd7fba50eb \
+    --hash=sha256:454902dc1830d935c90b5b53c863ba2a98dcde0fbaa31ca2ed1ad33b2a7171c6 \
+    --hash=sha256:466d31fd043ef9af822ee3f1df8fdff4e8c199a7f4012c2642006af240eade17 \
+    --hash=sha256:49a98ecadc5a241c9ba06de08127ee4796e1009555efd791bac514207862b43d \
+    --hash=sha256:4d26f1fa9fa2167bb238f6f4b20218eb4e88dd3ef21bb8f97439fa6b5313e30d \
+    --hash=sha256:52c136f348605974c9b1c878addd6b7a60e3bf2245833e370862009b86fa4689 \
+    --hash=sha256:536a7a8a53b75b2e98ff96edb2dfb91a26b81c4fed82782035767db5a465be46 \
+    --hash=sha256:576d258b21c1db4c6449b1c572c75d03f16a482eb380be8003682bdbe7db2f28 \
+    --hash=sha256:609ffd44fed2ed88d9b4ef62ee860cf86446cf066333ad4ce4123505b819e581 \
+    --hash=sha256:67b336c15e564d76869c9a21316f90edf546809a5796a083b8f57c845056bc01 \
+    --hash=sha256:685cc37f3f307c6a8e879986c6d85328f4c637f002e219f50e2ef66f7e062c1d \
+    --hash=sha256:6a49ad0102c0f0ba839628d0bf45973c86ce7b590cdedf7540d5b1833ddc6f00 \
+    --hash=sha256:6fb64dd45453225f57d82c4764818d7a205ee31ce193e9f0086e493916bd4f72 \
+    --hash=sha256:701bb4a8f4de191c8c0cc9a1e6d5142f4df880e9d1210e333b829ca9425570ed \
+    --hash=sha256:73553bbeea7d6ec88c08ad8027f4e992798f0abc459361bf06641c71972794dc \
+    --hash=sha256:7520e799b1f84e095cce919bd6c23c9d49472deeef25fe1ef960b04cca51c3fc \
+    --hash=sha256:7609b8462351c4836b3edce4201acb6dd46187b207c589b30a87ffd1813b48dc \
+    --hash=sha256:7db9584235895a1dffca17e1c634b13870852094f6389b68dcc6338086aa7b08 \
+    --hash=sha256:7fa7d37f2ada0f42e0723632993ed422f2a679af0e200874d9d861720a54f53e \
+    --hash=sha256:80741ec5b471fbdfb997821b2842c59660a1c930ceb42f8a84ba8ca0f25a66aa \
+    --hash=sha256:8254dbfce84ee5d1e81051ee7a0f1536c108ba294c0fdb5933476398df0654f3 \
+    --hash=sha256:8b8d3e4e014fb4274f1c5bf61511d2199e263909fb0b8bda2a7428b0894e8dc6 \
+    --hash=sha256:8e1c18890091aa3cc8a77967943476b729dc2016f4cfe11e45d89b12519d4a93 \
+    --hash=sha256:9106025c7f261f9f5144f9aa7681d43867eed06349a7cfb297a1bc804de2f0d1 \
+    --hash=sha256:91b8fb9427e33f83ca2ba9501221ffaac1ecf0407f758c4d2f283c523da185ee \
+    --hash=sha256:96404e8d5e1bbe36bdaa84ef89dc36f0e75939e060ca5cd45451aba01db02902 \
+    --hash=sha256:9b4c90c5363c6b0a54188122b61edb919c2cd1119684999d08cd5e538813a28e \
+    --hash=sha256:a0509475d714df8f6d498935b3f307cd122c4ca76f7d426c7e1bb791bcd87eda \
+    --hash=sha256:a173401d7821a2a81c7b47d4e7d5c4021375a1441af0c58611c1957445055056 \
+    --hash=sha256:a45d94075ac0647621eaaf693c8751813a3eccac455d423f473ffed38c8ac5c9 \
+    --hash=sha256:a5f72421246c21af6a92fbc8c13b6d4c5427dfd949049b937c3b731f2f9076bd \
+    --hash=sha256:a64619a9c47c25582190af38e9eb382279ad42e1f06034f14d794670796016c0 \
+    --hash=sha256:a7ee6884a8848792d58b854946b685521f41d8871afa65e0d4a774954e9c9e89 \
+    --hash=sha256:ae38bd86eae3ba3d2ce5636cc9e23c80c9db2e9cb557e40b98153ed102b5a736 \
+    --hash=sha256:b026cf2c32daf48d90c0c4e406815c3f8f4cfe0c6dfccb094a9add1ff6a0e41a \
+    --hash=sha256:b0a2074a37285570d54b55820687de3d2f2b9ecf1b714e482e48c9e7c0402038 \
+    --hash=sha256:b1a3297b9cad594e1ff0c040d2881d7d3a74124a3c73e00c3c71526a1234a9f7 \
+    --hash=sha256:b212452b80cae26cb767aa045b051740e464c5129b7bd739c58fbb7deb339e7b \
+    --hash=sha256:b234a4a9248a9f000b7a5dfe84b8cb6210ee5120ae70eb72a4dcbdb4c528f72f \
+    --hash=sha256:b4095c5019bb889aa866bf12ed4c85c0daea5aafcb7c20d1519f02a1e738f07f \
+    --hash=sha256:b8e8c516dc4e1a51d86ac975b0350735007e554c962281c432eaa5822aa9765c \
+    --hash=sha256:bd80ed29761490c622edde5dd70537ca8c992c2952eb62ed46984f8eff66d6e8 \
+    --hash=sha256:c083f6dd6951b86e484ebfc9c3524b49bcaa9c420cb4b2a78ef9f7a512bfcc85 \
+    --hash=sha256:c0f4808644baf0a434a3442df5e0bedf8d05208f0719cedcd499e168b23bfdc4 \
+    --hash=sha256:c4cb992d8090d5ae5f7afa6754d7211c578be0c45f54d3d94f7781c495d56716 \
+    --hash=sha256:c60e547c0a375c4bfcdd60eef82e7e0e8698bf84c239d715f5c1278a73050393 \
+    --hash=sha256:c73a6bbc97ba1b5a0c3c992ae93d721c395bdbb120492759b94cc1ac71bc6350 \
+    --hash=sha256:c893f8c1a6d48b25961e00922724732d00b39de8bb0b451307482dc87bddcd74 \
+    --hash=sha256:cd6ab7d6776c186f544f893b45ee0c883542b35e8a493db74665d2e594d3ca75 \
+    --hash=sha256:d89ae7de94631b60d468412c18290d358a9d805182373d804ec839978b120422 \
+    --hash=sha256:d9d4f5e471e8dc49b593a80766c2328257e405f943c56a3dc985c125732bc4cf \
+    --hash=sha256:da206d1ec78438a563c5429ab808a2b23ad7bc025c8adbf08540dde202be37d5 \
+    --hash=sha256:dbf53db46f7cf176ee01d8d98c39381440776fcda13779d269a8ba664f69bec0 \
+    --hash=sha256:dd21c0128e301851de51bc607b0a6da50e82dc34e9601f4b508d08cc89ee7929 \
+    --hash=sha256:e2580c1d7e66e6d29d6e11855e3b1c6381971e0edd9a5066e6c14d79bc8967af \
+    --hash=sha256:e3818eabaefb90adeb5e0f62f047310079d426387991106d4fbf3519eec7d90a \
+    --hash=sha256:ed69af4fe2a0949b1ea1d012bf065c77b4c7822bad4737f17807af2adb15a73c \
+    --hash=sha256:f172b8b2c72a13a06ea49225a9c47079549036ad1b34afa12d5491b881f5b993 \
+    --hash=sha256:f275ede6199d0f1ed4ea5d55a7b7573ccd40d97aee7808559e1298fe6efc8dbd \
+    --hash=sha256:f7edeb1dcc7f50a2c8e08b9dc13a413903b7817e72273f00878cb70e766bdb3b \
+    --hash=sha256:fa2c9cb607e0f660d48c54a63de7a9b36fef62f6b8bd50ff592ce1137e73ac7d \
+    --hash=sha256:fe94d1de77c4cd8caff1bd5480e22342dbd54c93929f5943495d9c1e8abe9f42
 annotated-types==0.7.0 \
     --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \
     --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89
diff --git a/tests/test_stop_string_processor.py b/tests/test_stop_string_processor.py
new file mode 100644
index 0000000..a65e9b5
--- /dev/null
+++ b/tests/test_stop_string_processor.py
@@ -0,0 +1,169 @@
+import unittest
+from pathlib import Path
+
+from huggingface_hub import snapshot_download
+from mlx_lm.tokenizer_utils import load_tokenizer
+
+from mlx_engine.stop_string_processor import StopStringProcessor
+
+
+class TestStopStringProcessor(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        """Set up any necessary resources that can be shared across all tests."""
+        # use Llama-3.1 tokenizer for testing
+        cls.tokenizer = cls.download_tokenizer(
+            "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+        )
+
+    # followed pattern from mlx-examples
+    # https://github.com/ml-explore/mlx-examples/blob/cfc29c29f45372c78876335a44b0c99ab6565ae0/llms/tests/test_tokenizers.py#L17
+    @staticmethod
+    def download_tokenizer(repo):
+        path = Path(
+            snapshot_download(
+                repo_id=repo,
+                allow_patterns=[
+                    "tokenizer.json",
+                    "tokenizer_config.json",
+                    "special_tokens_map.json",
+                    "tokenizer.model",
+                ],
+            )
+        )
+        return load_tokenizer(path)
+
+    def process_tokens(self, stop_strings, input_string):
+        """Helper method to process tokens and collect results"""
+        processor = StopStringProcessor(stop_strings, self.tokenizer)
+        input_tokens = self.tokenizer.encode(input_string)
+        results = []
+        for token in input_tokens:
+            result = processor.process_token(token)
+            results.append(result)
+            if result.status == "full_stop":
+                break
+        return results
+
+    def test_stop_string_processor_simple(self):
+        results = self.process_tokens(["of"], "The objective of chess")
+
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "of")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode(" of"))
+
+    def test_stop_string_at_start(self):
+        results = self.process_tokens(["Hello"], "Hello world")
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "Hello")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode("Hello"))
+
+    def test_stop_string_at_end(self):
+        results = self.process_tokens(["world"], "Hello world")
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "world")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode(" world"))
+
+    def test_case_sensitivity(self):
+        results = self.process_tokens(["Stop"], "This is a STOP sign")
+        self.assertEqual(results[-1].status, "no_match")
+
+    def test_stop_string_with_special_characters(self):
+        results = self.process_tokens(["\n"], "Hello\nworld")
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "\n")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode("\n"))
+
+    def test_unicode_stop_strings(self):
+        results = self.process_tokens(["é", "ñ", "北京"], "Hello 北京 é ñ")
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "北京")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode(" 北京"))
+
+    def test_stop_string_processor_no_match(self):
+        results = self.process_tokens(["other"], "The objective of chess")
+
+        for i, result in enumerate(results):
+            self.assertEqual(
+                result.status,
+                "no_match",
+                f"Result at position {i} has status '{result.status}' instead of 'no_match'",
+            )
+
+    def test_stop_string_processor_long_no_match(self):
+        results = self.process_tokens(
+            ["The objective of checkers"], "The objective of chess"
+        )
+
+        for i, result in enumerate(results[:-1]):
+            self.assertEqual(
+                result.status,
+                "partial_match",
+                f"Result at position {i} has status '{result.status}' instead of 'partial_match'",
+            )
+        self.assertEqual(results[-1].status, "no_match")
+
+    def test_stop_string_processor_mid_word(self):
+        results = self.process_tokens(["cti"], "The objective of chess")
+
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "cti")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode(" objective"))
+
+    def test_stop_string_processor_multi_token_multi_word(self):
+        results = self.process_tokens(["objective of"], "The objective of chess")
+
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "objective of")
+        self.assertEqual(
+            results[-1].stop_tokens, self.tokenizer.encode(" objective of")
+        )
+
+    def test_stop_string_processor_multi_token_multi_token_single_char(self):
+        results = self.process_tokens(["🌟"], "The objective 🌟 of chess")
+
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "🌟")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode(" 🌟"))
+
+        self.assertEqual(results[-3].status, "multi_byte")
+        self.assertEqual(results[-2].status, "multi_byte")
+
+    def test_multiple_stop_strings(self):
+        results = self.process_tokens(
+            ["of", "chess", "objective"], "The objective of chess"
+        )
+
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "objective")
+        self.assertEqual(results[-1].stop_tokens, self.tokenizer.encode(" objective"))
+
+    def test_overlapping_stop_strings(self):
+        results = self.process_tokens(
+            ["objective of", "of chess"], "The objective of chess"
+        )
+
+        self.assertEqual(results[-1].status, "full_stop")
+        self.assertEqual(results[-1].stop_string, "objective of")
+        self.assertEqual(
+            results[-1].stop_tokens, self.tokenizer.encode(" objective of")
+        )
+
+    def test_empty_stop_strings_list_raises(self):
+        with self.assertRaises(ValueError):
+            StopStringProcessor([], self.tokenizer)
+
+    def test_non_string_stop_string_raises(self):
+        stop_strings = ["valid", 123]
+        with self.assertRaises(TypeError):
+            StopStringProcessor(stop_strings, self.tokenizer)
+
+    def test_none_stop_string_raises(self):
+        stop_strings = ["valid", None]
+        with self.assertRaises(TypeError):
+            StopStringProcessor(stop_strings, self.tokenizer)
+
+    def test_empty_stop_string_raises(self):
+        stop_strings = ["valid", ""]
+        with self.assertRaises(ValueError):
+            StopStringProcessor(stop_strings, self.tokenizer)