Fix Gemma 4 sanitize() not stripping KV projections for shared layers (#1240)

Co-authored-by: Tristan Stahnke <tristan@melchior.lan>
Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>

mlx_lm/_version.py

@@ -1,3 +1,3 @@
 # Copyright © 2023-2025 Apple Inc.
 
-__version__ = "0.31.2"
+__version__ = "0.31.3"

mlx_lm/generate.py

@@ -223,7 +223,7 @@ def setup_arg_parser():
 
 
 # A stream on the default device just for generation
-generation_stream = mx.new_stream(mx.default_device())
+generation_stream = mx.new_thread_local_stream(mx.default_device())
 
 
 @contextlib.contextmanager
@@ -1497,6 +1497,7 @@ class BatchGenerator:
     def __init__(
         self,
         model: nn.Module,
+        *,
         max_tokens: int = 128,
         stop_tokens: Optional[Sequence[Sequence[int]]] = None,
         sampler: Optional[Callable[[mx.array], mx.array]] = None,
@@ -1507,6 +1508,7 @@ class BatchGenerator:
         prefill_batch_size: int = 8,
         prefill_step_size: int = 2048,
         max_kv_size: Optional[int] = None,
+        stream=None,
     ):
         self.model = model
         self.max_tokens = max_tokens
@@ -1518,6 +1520,8 @@ class BatchGenerator:
         self.completion_batch_size = max(completion_batch_size, prefill_batch_size)
         self.max_kv_size = max_kv_size
 
+        self._stream = stream or generation_stream
+
         self._default_state_machine = SequenceStateMachine(
             {"normal": [(seq, None) for seq in stop_tokens]} if stop_tokens else {},
             initial="normal",
@@ -1544,9 +1548,13 @@ class BatchGenerator:
         else:
             self._old_wired_limit = None
 
+    @property
+    def stream(self):
+        return self._stream
+
     def close(self):
         if self._old_wired_limit is not None:
-            mx.synchronize(generation_stream)
+            mx.synchronize(self._stream)
             mx.set_wired_limit(self._old_wired_limit)
             self._old_wired_limit = None
 
@@ -1843,7 +1851,7 @@ class BatchGenerator:
         Returns:
             Tuple of prompt processing responses and generation responses.
         """
-        with mx.stream(generation_stream):
+        with mx.stream(self._stream):
             return self._next()
 
     def next_generated(self):
@@ -1853,7 +1861,7 @@ class BatchGenerator:
         Returns:
             List of GenerationBatch.Response objects
         """
-        with mx.stream(generation_stream):
+        with mx.stream(self._stream):
             while True:
                 prompt_responses, generation_responses = self._next()
                 if not generation_responses and prompt_responses:

mlx_lm/models/apertus.py

@@ -167,7 +167,8 @@ class Model(nn.Module):
         self.args = args
         self.model_type = args.model_type
         self.model = ApertusModel(args)
-        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
 
     def __call__(
         self,
@@ -175,12 +176,18 @@ class Model(nn.Module):
         cache: Optional[Any] = None,
     ) -> mx.array:
         out = self.model(inputs, cache)
-        return self.lm_head(out)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
 
     def sanitize(self, weights):
         for k, v in weights.items():
             if k.endswith("alpha_p") or k.endswith("alpha_n"):
                 weights[k] = v.squeeze()
+        if self.args.tie_word_embeddings:
+            weights.pop("lm_head.weight", None)
         return weights
 
     @property

mlx_lm/models/cache.py

@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional
 
 import mlx.core as mx
 import mlx.nn as nn
-from mlx.utils import tree_flatten, tree_map, tree_unflatten
+from mlx.utils import tree_flatten, tree_map, tree_reduce, tree_unflatten
 
 from .base import create_causal_mask
 
@@ -603,6 +603,18 @@ class ArraysCache(_BaseCache):
         if left_padding:
             self.left_padding = mx.array(left_padding)
 
+    @property
+    def batch_size(self):
+        for c in self.cache:
+            if c is not None:
+                return c.shape[0]
+        if self.left_padding is not None:
+            return self.left_padding.size
+        elif self.lengths is not None:
+            return self.lengths.size
+        else:
+            return 1
+
     def __setitem__(self, idx, value):
         self.cache[idx] = value
 
@@ -622,6 +634,8 @@ class ArraysCache(_BaseCache):
         In-place filter to keep just the given indices in the cache.
         """
         self.cache = [c[batch_indices] if c is not None else None for c in self.cache]
+        if self.left_padding is not None:
+            self.left_padding = self.left_padding[batch_indices]
         if self.lengths is not None:
             self.lengths = self.lengths[batch_indices]
 
@@ -630,14 +644,31 @@ class ArraysCache(_BaseCache):
         In-place extend this cache with the other cache.
         """
 
+        a_batch = self.batch_size
+        b_batch = other.batch_size
+
         def cat(a, b):
+            shape = dtype = None
+            if a is not None:
+                shape = a.shape
+                dtype = a.dtype
+            if b is not None:
+                shape = b.shape
+                dtype = b.dtype
+
+            if shape is None:
+                return None
+
             if a is None:
-                return b
+                a = mx.zeros((a_batch,) + shape[1:], dtype=dtype)
             if b is None:
-                return a
+                b = mx.zeros((b_batch,) + shape[1:], dtype=dtype)
+
             return mx.concatenate([a, b])
 
         self.cache = [cat(c, o) for c, o in zip(self.cache, other.cache)]
+        self.left_padding = cat(self.left_padding, other.left_padding)
+        self.lengths = cat(self.lengths, other.lengths)
 
     def extract(self, idx):
         cache = ArraysCache(len(self.cache))
@@ -675,6 +706,7 @@ class ArraysCache(_BaseCache):
 
         # All caches are empty so return early
         if all(c.empty() for c in caches):
+            cache.left_padding = mx.array([0] * B)
             return cache
 
         for e in range(n_state):
@@ -1024,8 +1056,9 @@ class BatchKVCache(_BaseCache):
         def pad(c):
             k, v = c.keys, c.values
             if k is None:
-                k = mx.array([]).reshape(B, H, 0, D)
-                v = mx.array([]).reshape(B, H, 0, M)
+                Bc = c.offset.shape[0]
+                k = mx.array([]).reshape(Bc, H, 0, D)
+                v = mx.array([]).reshape(Bc, H, 0, M)
             left = max_idx - c._idx
             right = max_size - k.shape[2] - left
             if right < 0:
@@ -1360,8 +1393,9 @@ class BatchRotatingKVCache(_BaseCache):
             left = max_idx - c._idx
             k, v = c.keys, c.values
             if k is None:
-                k = mx.array([]).reshape(B, H, 0, D)
-                v = mx.array([]).reshape(B, H, 0, M)
+                Bc = c.offset.shape[0]
+                k = mx.array([]).reshape(Bc, H, 0, D)
+                v = mx.array([]).reshape(Bc, H, 0, M)
             right = max_size - k.shape[2] - left
             if right < 0:
                 k = k[..., :right, :]

mlx_lm/models/gemma4_text.py

@@ -180,6 +180,7 @@ class Attention(nn.Module):
         self.layer_idx = layer_idx
         self.layer_type = config.layer_types[layer_idx]
         self.is_sliding = self.layer_type == "sliding_attention"
+        self.has_kv = layer_idx < config.num_hidden_layers - config.num_kv_shared_layers
 
         self.head_dim = (
             config.global_head_dim
@@ -202,14 +203,18 @@ class Attention(nn.Module):
         self.scale = 1.0
 
         self.q_proj = nn.Linear(dim, self.n_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False)
-        if not self.use_k_eq_v:
-            self.v_proj = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False)
+        if self.has_kv:
+            self.k_proj = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False)
+            if not self.use_k_eq_v:
+                self.v_proj = nn.Linear(
+                    dim, self.n_kv_heads * self.head_dim, bias=False
+                )
         self.o_proj = nn.Linear(self.n_heads * self.head_dim, dim, bias=False)
 
         self.q_norm = nn.RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        self.k_norm = nn.RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        self.v_norm = RMSNormNoScale(self.head_dim, eps=config.rms_norm_eps)
+        if self.has_kv:
+            self.k_norm = nn.RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.v_norm = RMSNormNoScale(self.head_dim, eps=config.rms_norm_eps)
 
         # RoPE (with partial rotation support)
         layer_key = "sliding_attention" if self.is_sliding else "full_attention"
@@ -238,6 +243,10 @@ class Attention(nn.Module):
 
         if shared_kv is not None:
             keys, values = shared_kv
+        elif not self.has_kv:
+            raise ValueError(
+                f"Layer {self.layer_idx} is a KV-shared layer but received no shared_kv"
+            )
         else:
             keys = self.k_proj(x).reshape(B, L, self.n_kv_heads, self.head_dim)
             values = keys
@@ -600,6 +609,7 @@ class Model(nn.Module):
 
     def sanitize(self, weights):
         sanitized = {}
+        first_kv_shared = self.args.num_hidden_layers - self.args.num_kv_shared_layers
         for k, v in weights.items():
             if any(
                 s in k
@@ -613,6 +623,18 @@ class Model(nn.Module):
             ):
                 continue
 
+            # KV-shared layers reuse K/V from earlier layers — drop their projections
+            if any(
+                s in k
+                for s in (".self_attn.k_proj", ".self_attn.v_proj", ".self_attn.k_norm")
+            ):
+                try:
+                    layer_idx = int(k.split("layers.")[1].split(".")[0])
+                    if layer_idx >= first_kv_shared:
+                        continue
+                except (IndexError, ValueError):
+                    pass
+
             if k.endswith(".experts.gate_up_proj"):
                 base = k.removesuffix(".gate_up_proj")
                 gate, up = map(mx.contiguous, mx.split(v, 2, axis=-2))

mlx_lm/quant/dwq.py

@@ -314,7 +314,11 @@ def main():
 
     if args.target_dir is not None:
         target_dir = Path(args.target_dir)
-        has_targets = target_dir.exists()
+        has_targets = (
+            target_dir.is_dir()
+            and any((target_dir / "train").glob("*.safetensors"))
+            and any((target_dir / "valid").glob("*.safetensors"))
+        )
     else:
         has_targets = False
         target_dir = None

mlx_lm/server.py

@@ -10,7 +10,7 @@ import time
 import uuid
 import warnings
 from collections import deque
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 from pathlib import Path
 from queue import Empty as QueueEmpty
@@ -36,7 +36,6 @@ from ._version import __version__
 from .generate import (
     BatchGenerator,
     SequenceStateMachine,
-    generation_stream,
     stream_generate,
 )
 from .models.cache import (
@@ -78,7 +77,14 @@ class ToolCallFormatter:
 
         result = []
         for tool_text in tool_calls:
-            parsed = self._tool_parser(tool_text, self._tools)
+            try:
+                parsed = self._tool_parser(tool_text, self._tools)
+            except (ValueError, json.JSONDecodeError) as e:
+                logging.warning(
+                    f"Failed to parse tool call ({type(e).__name__}: {e}) — "
+                    f"tool text was likely truncated mid-generation."
+                )
+                continue
             if not isinstance(parsed, list):
                 parsed = [parsed]
             result.extend(self._format(tc) for tc in parsed)
@@ -227,6 +233,22 @@ class Response:
     top_tokens: Tuple[Dict[str, Any]]
 
 
+def _process_control_tokens(ctx, token_stream):
+    buffer_size = max(len(s) for s in ctx.sequences)
+    buffered_stream = deque()
+
+    for tok in token_stream:
+        buffered_stream.append(tok)
+        if tok.match is not None:
+            popped = [buffered_stream.pop() for _ in tok.match]
+            for t in reversed(popped):
+                buffered_stream.append(replace(t, text=""))
+        if len(buffered_stream) >= buffer_size:
+            yield buffered_stream.popleft()
+    while len(buffered_stream) > 0:
+        yield buffered_stream.popleft()
+
+
 class TimeBudget:
     def __init__(self, budget=0.5, iterations=25, sync_frequency=10):
         self._is_distributed = mx.distributed.init().size() > 1
@@ -256,8 +278,7 @@ class TimeBudget:
         self._loops += 1
         self._time_spent += time.time() - self._start
         if self._loops % self._sync_frequency == 0:
-            with mx.stream(generation_stream):
-                loop_time = mx.distributed.all_sum(self._time_spent).item()
+            loop_time = mx.distributed.all_sum(self._time_spent).item()
             avg_loop_time = loop_time / (
                 mx.distributed.init().size() * self._sync_frequency
             )
@@ -285,94 +306,92 @@ class ModelProvider:
         )
         self.is_distributed = group.size() > 1
 
-        # Preload the default model if it is provided
-        self.default_model_map = {}
-        if self.cli_args.model is not None:
-            self.default_model_map[self.cli_args.model] = "default_model"
-            self.load(self.cli_args.model, draft_model_path="default_model")
+        # Maps model and adapter paths the actual paths to be used. Used to
+        # map 'default_model' to the provided model by cli argument but could
+        # be used for more in the future.
+        self._model_map = {}
+        self._adapter_map = {}
+        self._draft_model_map = {}
+        self._model_map["default_model"] = self.cli_args.model
+        self._adapter_map["default_model"] = self.cli_args.adapter_path
+        self._draft_model_map["default_model"] = self.cli_args.draft_model
 
-    # Added in adapter_path to load dynamically
-    def load(self, model_path, adapter_path=None, draft_model_path=None):
-        model_path = self.default_model_map.get(model_path, model_path)
-        if self.model_key == (model_path, adapter_path, draft_model_path):
-            return self.model, self.tokenizer
+        # Build the tokenizer config for later use in load
+        self._tokenizer_config = {
+            "trust_remote_code": True if cli_args.trust_remote_code else None
+        }
+        if cli_args.chat_template:
+            self._tokenizer_config["chat_template"] = cli_args.chat_template
+
+    def _load(self, model_path, adapter_path=None, draft_model_path=None):
+        if self.is_distributed and (
+            adapter_path is not None or draft_model_path is not None
+        ):
+            raise ValueError(
+                "Loading with adapters or draft models not supported in distributed mode"
+            )
 
         # Remove the old model if it exists.
+        self.model_key = None
         self.model = None
         self.tokenizer = None
-        self.model_key = None
         self.draft_model = None
 
-        # Building tokenizer_config
-        tokenizer_config = {
-            "trust_remote_code": True if self.cli_args.trust_remote_code else None
-        }
-        if self.cli_args.chat_template:
-            tokenizer_config["chat_template"] = self.cli_args.chat_template
-
-        if model_path == "default_model":
-            if self.cli_args.model is None:
-                raise ValueError(
-                    "A model path has to be given as a CLI "
-                    "argument or in the HTTP request"
-                )
-            adapter_path = adapter_path or self.cli_args.adapter_path
-            # TODO: Generalize distributed load
-            if self.is_distributed:
-                model, tokenizer = sharded_load(
-                    self.cli_args.model, self.pipeline_group, self.tensor_group
-                )
-            else:
-                model, tokenizer = load(
-                    self.cli_args.model,
-                    adapter_path=adapter_path,
-                    tokenizer_config=tokenizer_config,
-                )
+        # Load the model and tokenizer
+        if self.is_distributed:
+            model, tokenizer = sharded_load(
+                model_path,
+                pipeline_group=self.pipeline_group,
+                tensor_group=self.tensor_group,
+                tokenizer_config=self._tokenizer_config,
+            )
         else:
-            # TODO: Generalize distributed load
-            if self.is_distributed:
-                model, tokenizer = sharded_load(
-                    model_path, self.pipeline_group, self.tensor_group
-                )
-            else:
-                model, tokenizer = load(
-                    model_path,
-                    adapter_path=adapter_path,
-                    tokenizer_config=tokenizer_config,
-                )
+            model, tokenizer = load(
+                model_path,
+                adapter_path=adapter_path,
+                tokenizer_config=self._tokenizer_config,
+            )
 
+        # Use the default chat template if needed
         if self.cli_args.use_default_chat_template:
             if tokenizer.chat_template is None:
                 tokenizer.chat_template = tokenizer.default_chat_template
 
-        self.model_key = (model_path, adapter_path, draft_model_path)
-        self.model = model
-        self.tokenizer = tokenizer
-
-        def validate_draft_tokenizer(draft_tokenizer):
-            # Check if tokenizers are compatible
+        # Load the draft model for speculative decoding
+        draft_model = None
+        if draft_model_path is not None:
+            draft_model, draft_tokenizer = load(draft_model_path)
             if draft_tokenizer.vocab_size != tokenizer.vocab_size:
                 logging.warning(
                     "Draft model tokenizer does not match model tokenizer. "
                     "Speculative decoding may not work as expected."
                 )
 
-        # Load draft model if specified
-        if (
-            draft_model_path == "default_model"
-            and self.cli_args.draft_model is not None
-        ):
-            self.draft_model, draft_tokenizer = load(self.cli_args.draft_model)
-            validate_draft_tokenizer(draft_tokenizer)
+        # Compute batchability
+        is_batchable = draft_model is None
+        is_batchable = is_batchable and all(
+            hasattr(c, "merge") for c in make_prompt_cache(model)
+        )
 
-        elif draft_model_path is not None and draft_model_path != "default_model":
-            self.draft_model, draft_tokenizer = load(draft_model_path)
-            validate_draft_tokenizer(draft_tokenizer)
+        # Update the member variables
+        self.model_key = (model_path, adapter_path, draft_model_path)
+        self.model = model
+        self.tokenizer = tokenizer
+        self.draft_model = draft_model
+        self.is_batchable = is_batchable
 
-        if self.draft_model is None:
-            self.is_batchable = all(
-                hasattr(c, "merge") for c in make_prompt_cache(self.model)
-            )
+    def load_default(self):
+        if self._model_map["default_model"] is not None:
+            self.load("default_model", None, "default_model")
+
+    def load(self, model_path, adapter_path=None, draft_model_path=None):
+        model_path = self._model_map.get(model_path, model_path)
+        adapter_path = self._adapter_map.get(model_path, adapter_path)
+        draft_model_path = self._draft_model_map.get(draft_model_path, draft_model_path)
+
+        model_key = (model_path, adapter_path, draft_model_path)
+        if self.model_key != model_key:
+            self._load(*model_key)
 
         return self.model, self.tokenizer
 
@@ -466,22 +485,21 @@ class ResponseGenerator:
         if not self._is_distributed:
             return obj
 
-        with mx.stream(generation_stream):
-            if self._rank == 0:
-                if obj is None:
-                    mx.eval(mx.distributed.all_sum(0))
-                    return None
-                data = mx.array(pickle.dumps(obj))
-                mx.eval(mx.distributed.all_sum(data.size))
-                mx.eval(mx.distributed.all_sum(data))
-                return obj
-            else:
-                size = mx.distributed.all_sum(0).item()
-                if size == 0:
-                    return None
-                data = mx.zeros(size, dtype=mx.uint8)
-                data = mx.distributed.all_sum(data)
-                return pickle.loads(data)
+        if self._rank == 0:
+            if obj is None:
+                mx.eval(mx.distributed.all_sum(0))
+                return None
+            data = mx.array(pickle.dumps(obj))
+            mx.eval(mx.distributed.all_sum(data.size))
+            mx.eval(mx.distributed.all_sum(data))
+            return obj
+        else:
+            size = mx.distributed.all_sum(0).item()
+            if size == 0:
+                return None
+            data = mx.zeros(size, dtype=mx.uint8)
+            data = mx.distributed.all_sum(data)
+            return pickle.loads(data)
 
     def _share_request(self, request):
         if not self._is_distributed:
@@ -651,10 +669,11 @@ class ResponseGenerator:
             ts = tokenizer.tool_call_start_tokens
             te = tokenizer.tool_call_end_tokens
             transitions["normal"].append((ts, "tool"))
-            transitions["tool"] = [(te, "normal")]
+            transitions["tool"] = [(te, "normal")] if te else []
             transitions["tool"].extend(common_stops)
             sequences[ts] = tokenizer.tool_call_start
-            sequences[te] = tokenizer.tool_call_end
+            if te:
+                sequences[te] = tokenizer.tool_call_end
 
         sm = SequenceStateMachine(transitions, initial=initial_state)
         if len(self._state_machine_cache) > 100:
@@ -667,6 +686,14 @@ class ResponseGenerator:
         return self.model_provider.is_batchable and args.seed is None
 
     def _generate(self):
+        # Local thread stream that we 'll pass to the BatchGenerator to make
+        # sure that all generation runs in the same stream as the
+        # synchronization messages.
+        generation_stream = mx.default_stream(mx.default_device())
+
+        # Load the default model if it is given
+        self.model_provider.load_default()
+
         current_model = None
         current_sampling = None
         current_tokenizer = None
@@ -796,6 +823,7 @@ class ResponseGenerator:
                         completion_batch_size=self.cli_args.decode_concurrency,
                         prefill_batch_size=self.cli_args.prompt_concurrency,
                         prefill_step_size=self.cli_args.prefill_step_size,
+                        stream=generation_stream,
                     )
                     unprocessed_requests.append((rqueue, request, args))
                     continue
@@ -885,12 +913,11 @@ class ResponseGenerator:
 
                 uids_to_remove = self._share_object(uids_to_remove)
                 if uids_to_remove:
-                    with mx.stream(generation_stream):
-                        batch_generator.remove(uids_to_remove)
-                        for uid in uids_to_remove:
-                            # It may have already been removed during
-                            # generation
-                            batch_results.pop(uid, None)
+                    batch_generator.remove(uids_to_remove)
+                    for uid in uids_to_remove:
+                        # It may have already been removed during
+                        # generation
+                        batch_results.pop(uid, None)
 
     def _serve_single(self, request):
         rqueue, request, args = request
@@ -1018,20 +1045,6 @@ class ResponseGenerator:
                     continue
                 yield response
 
-        def _process_control_tokens(ctx, token_stream):
-            buffer_size = max(len(s) for s in ctx.sequences)
-            buffered_stream = deque()
-
-            for tok in token_stream:
-                buffered_stream.append(tok)
-                if tok.match is not None:
-                    for _ in tok.match:
-                        buffered_stream.pop()
-                if len(buffered_stream) >= buffer_size:
-                    yield buffered_stream.popleft()
-            while len(buffered_stream) > 0:
-                yield buffered_stream.popleft()
-
         ctx = response_queue.get()
         if isinstance(ctx, Exception):
             raise ctx

mlx_lm/tokenizer_utils.py

@@ -397,6 +397,8 @@ class TokenizerWrapper:
 
     @property
     def think_start_id(self):
+        if self._think_start_tokens is None:
+            return None
         if len(self._think_start_tokens) > 1:
             raise ValueError("The start thinking sequence is more than 1 token")
         return self._think_start_tokens[0]
@@ -411,6 +413,8 @@ class TokenizerWrapper:
 
     @property
     def think_end_id(self):
+        if self._think_end_tokens is None:
+            return None
         if len(self._think_end_tokens) > 1:
             raise ValueError("The end thinking sequence is more than 1 token")
         return self._think_end_tokens[0]

mlx_lm/tool_parsers/gemma4.py

@@ -5,10 +5,19 @@ from typing import Any, Optional
 
 import regex as re
 
+# Matches <|"|>...<|"|> string literals (Gemma 4's string delimiter).
+_GEMMA4_STR = r'<\|"\|>(?:(?!<\|"\|>)[\s\S])*?<\|"\|>'
+
 # Matches call:name{...} with balanced braces via the regex module's
-# recursive (?R)-style support.  (\{(?:[^{}]|(?2))*\}) recurses on the
-# second capture group so nested objects like {a:{b:1}} are captured whole.
-_tool_call_regex = re.compile(r"call:(\w+)(\{(?:[^{}]|(?2))*\})", re.DOTALL)
+# recursive (?R)-style support.  The inner alternatives handle:
+#   [^{}<]          – any char that is not a brace or start of <|"|>
+#   <(?!\|"\|>)     – a lone '<' that is NOT the start of <|"|>
+#   <|"|>...<|"|>   – a complete string literal (braces inside are ignored)
+#   (?2)            – recursively balanced nested brace group
+_tool_call_regex = re.compile(
+    r"call:([\w-]+)(\{(?:[^{}<]|<(?!\|\"\|>)|" + _GEMMA4_STR + r"|(?2))*\})",
+    re.DOTALL,
+)
 
 
 def _gemma4_args_to_json(text: str) -> str:

mlx_lm/tool_parsers/minimax_m2.py

@@ -157,43 +157,44 @@ def _get_param_types_from_config(param_name: str, param_config: dict) -> list[st
 
 
 def parse_tool_call(text: str, tools: list | None = None):
-    invoke_match = _invoke_complete_regex.findall(text)
-    if not invoke_match:
+    invoke_matches = _invoke_complete_regex.findall(text)
+    if not invoke_matches:
         raise ValueError("No tool call found")
-    invoke_text = invoke_match[0]
 
-    name_match = re.search(r"^([^>]+)", invoke_text)
-    if not name_match:
-        return None
-
-    function_name = _extract_name(name_match.group(1))
-
-    # Get parameter configuration
-    param_config = {}
+    param_config_for = {}
     if tools:
         for tool in tools:
             if func := tool.get("function", False):
-                if func["name"] != function_name:
-                    continue
                 if params := func.get("parameters", False):
-                    param_config = params.get("properties", {})
+                    param_config_for[func["name"]] = params.get("properties", {})
 
-    # Extract parameters
-    param_dict = {}
-    for match in _parameter_complete_regex.findall(invoke_text):
-        param_match = re.search(r"^([^>]+)>(.*)", match, re.DOTALL)
-        if param_match:
-            param_name = _extract_name(param_match.group(1))
-            param_value = param_match.group(2).strip()
-            if param_value.startswith("\n"):
-                param_value = param_value[1:]
-            if param_value.endswith("\n"):
-                param_value = param_value[:-1]
+    calls = []
+    for invoke_text in invoke_matches:
+        name_match = re.search(r"^([^>]+)", invoke_text)
+        if not name_match:
+            continue
+        function_name = _extract_name(name_match.group(1))
+        param_config = param_config_for.get(function_name, {})
 
-            param_type = _get_param_types_from_config(param_name, param_config)
+        param_dict = {}
+        for match in _parameter_complete_regex.findall(invoke_text):
+            param_match = re.search(r"^([^>]+)>(.*)", match, re.DOTALL)
+            if param_match:
+                param_name = _extract_name(param_match.group(1))
+                param_value = param_match.group(2).strip()
+                if param_value.startswith("\n"):
+                    param_value = param_value[1:]
+                if param_value.endswith("\n"):
+                    param_value = param_value[:-1]
 
-            param_dict[param_name] = _convert_param_value_with_types(
-                param_value, param_type
-            )
+                param_type = _get_param_types_from_config(param_name, param_config)
 
-    return dict(name=function_name, arguments=param_dict)
+                param_dict[param_name] = _convert_param_value_with_types(
+                    param_value, param_type
+                )
+
+        calls.append(dict(name=function_name, arguments=param_dict))
+
+    if len(calls) == 1:
+        return calls[0]
+    return calls

mlx_lm/utils.py

@@ -507,6 +507,8 @@ def sharded_load(
     pipeline_group: Optional[mx.distributed.Group] = None,
     tensor_group: Optional[mx.distributed.Group] = None,
     return_config: bool = False,
+    *,
+    tokenizer_config: Optional[Dict[str, Any]] = None,
 ):
     # Get model path with everything but weight safetensors
     model_path = _download(
@@ -571,7 +573,7 @@ def sharded_load(
     # Load and shard the model, and load the weights
     tokenizer = load_tokenizer(
         model_path,
-        {"trust_remote_code": True},
+        tokenizer_config or {"trust_remote_code": True},
         eos_token_ids=config.get("eos_token_id", None),
     )
     model, _ = load_model(model_path, lazy=True, strict=False)

setup.py

@@ -10,7 +10,7 @@ sys.path.append(str(package_dir))
 
 from _version import __version__
 
-MIN_MLX_VERSION = "0.30.4"
+MIN_MLX_VERSION = "0.31.2"
 
 setup(
     name="mlx-lm",

tests/test_gguf.py

@@ -35,6 +35,9 @@ class TestConvertToGGUFWithoutMocks(unittest.TestCase):
         mock_tokenizer.get_vocab.return_value = {"<pad>": 0, "hello": 1, "world": 2}
         mock_tokenizer.all_special_tokens = ["<pad>"]
         mock_tokenizer.all_special_ids = [0]
+        mock_tokenizer.bos_token_id = None
+        mock_tokenizer.eos_token_id = None
+        mock_tokenizer.unk_token_id = None
         mock_from_pretrained.return_value = mock_tokenizer
 
         model_path = Path(self.test_dir)

tests/test_models.py

@@ -5,7 +5,7 @@ import unittest
 
 import mlx.core as mx
 import mlx.nn as nn
-from mlx.utils import tree_map
+from mlx.utils import tree_flatten, tree_map
 
 from mlx_lm.models import rope_utils
 from mlx_lm.models.base import create_causal_mask, scaled_dot_product_attention
@@ -1547,6 +1547,78 @@ class TestModels(unittest.TestCase):
             mx.allclose(logits, mx.ones((1, 1, 4), dtype=mx.float32) * 32.0)
         )
 
+    def test_gemma4_kv_shared_layers_omit_kv_projections(self):
+        """KV-shared layers must not create k_proj/v_proj/k_norm/v_norm so that
+        models saved without redundant weights (e.g. via transformers
+        save_pretrained) can be loaded with strict=True."""
+        from mlx_lm.models import gemma4_text
+
+        args = gemma4_text.ModelArgs(
+            model_type="gemma4_text",
+            hidden_size=128,
+            num_hidden_layers=10,
+            intermediate_size=256,
+            num_attention_heads=4,
+            head_dim=32,
+            global_head_dim=64,
+            rms_norm_eps=1e-6,
+            vocab_size=1000,
+            vocab_size_per_layer_input=1000,
+            num_key_value_heads=1,
+            num_kv_shared_layers=4,
+            hidden_size_per_layer_input=32,
+            sliding_window=8,
+            sliding_window_pattern=5,
+            final_logit_softcapping=30.0,
+            layer_types=[
+                "sliding_attention",
+                "sliding_attention",
+                "sliding_attention",
+                "sliding_attention",
+                "full_attention",
+                "sliding_attention",
+                "sliding_attention",
+                "sliding_attention",
+                "sliding_attention",
+                "full_attention",
+            ],
+            rope_parameters={
+                "full_attention": {
+                    "partial_rotary_factor": 0.25,
+                    "rope_theta": 1000000.0,
+                },
+                "sliding_attention": {
+                    "rope_theta": 10000.0,
+                },
+            },
+        )
+        model = gemma4_text.Model(args)
+
+        # Non-shared layers (0-5) should have KV projections
+        for i in range(6):
+            attn = model.model.layers[i].self_attn
+            self.assertTrue(attn.has_kv)
+            self.assertTrue(hasattr(attn, "k_proj"))
+            self.assertTrue(hasattr(attn, "k_norm"))
+
+        # Shared layers (6-9) should NOT have KV projections
+        for i in range(6, 10):
+            attn = model.model.layers[i].self_attn
+            self.assertFalse(attn.has_kv)
+            self.assertFalse(hasattr(attn, "k_proj"))
+            self.assertFalse(hasattr(attn, "k_norm"))
+            self.assertFalse(hasattr(attn, "v_proj"))
+
+        # Verify the model can load weights that omit shared-layer KV params
+        weights = dict(tree_flatten(model.parameters()))
+        kv_keys = [
+            k for k in weights if "k_proj" in k or "v_proj" in k or "k_norm" in k
+        ]
+        for k in kv_keys:
+            # All KV keys should belong to non-shared layers (0-5)
+            layer_idx = int(k.split("layers.")[1].split(".")[0])
+            self.assertLess(layer_idx, 6)
+
     def test_gemma4_input_embeddings_reconstruct_per_layer_inputs(self):
         from mlx_lm.models import gemma4_text
 

tests/test_prompt_cache.py

@@ -662,6 +662,102 @@ class TestPromptCache(unittest.TestCase):
         c_out = KVCache.merge((c1, c2))
         self.assertEqual(c_out.keys.shape, (2, 4, 4, 4))
 
+    def test_extend_with_empty_and_nonempty_batch_caches(self):
+        """Extending a batch cache when one side has keys=None should use the
+        correct batch size for the placeholder, not the batch size from the
+        non-None side. Regression test for broadcast error in dynamic_roll."""
+        H, D = 8, 64
+        max_size = 512
+
+        # -- BatchRotatingKVCache --
+        # Create 2 caches with content and 3 empty caches
+        c1 = RotatingKVCache(max_size=max_size)
+        c2 = RotatingKVCache(max_size=max_size)
+        c1.update_and_fetch(mx.ones((1, H, 5, D)), mx.ones((1, H, 5, D)))
+        c2.update_and_fetch(mx.ones((1, H, 3, D)), mx.ones((1, H, 3, D)))
+        batch_full = BatchRotatingKVCache.merge([c1, c2])
+
+        empty_caches = [RotatingKVCache(max_size=max_size) for _ in range(3)]
+        batch_empty = BatchRotatingKVCache.merge(empty_caches)
+
+        # Extend non-empty with empty (different batch sizes)
+        batch_full.extend(batch_empty)
+        self.assertEqual(batch_full.keys.shape[0], 5)
+        self.assertEqual(batch_full.offset.shape[0], 5)
+
+        # Prompt processing with right padding should not crash
+        batch_full.prepare(lengths=[10, 8, 12, 7, 11], right_padding=[2, 4, 0, 5, 1])
+        new_kv = mx.ones((5, H, 12, D))
+        batch_full.update_and_fetch(new_kv, new_kv)
+
+        # Also test empty extending non-empty
+        batch_full2 = BatchRotatingKVCache.merge(
+            [RotatingKVCache(max_size=max_size) for _ in range(3)]
+        )
+        c3 = RotatingKVCache(max_size=max_size)
+        c4 = RotatingKVCache(max_size=max_size)
+        c3.update_and_fetch(mx.ones((1, H, 4, D)), mx.ones((1, H, 4, D)))
+        c4.update_and_fetch(mx.ones((1, H, 6, D)), mx.ones((1, H, 6, D)))
+        batch_content = BatchRotatingKVCache.merge([c3, c4])
+        batch_full2.extend(batch_content)
+        self.assertEqual(batch_full2.keys.shape[0], 5)
+        self.assertEqual(batch_full2.offset.shape[0], 5)
+
+        # -- BatchKVCache --
+        c1 = KVCache()
+        c2 = KVCache()
+        c1.update_and_fetch(mx.ones((1, H, 5, D)), mx.ones((1, H, 5, D)))
+        c2.update_and_fetch(mx.ones((1, H, 3, D)), mx.ones((1, H, 3, D)))
+        batch_full = BatchKVCache.merge([c1, c2])
+
+        empty_caches = [KVCache() for _ in range(3)]
+        batch_empty = BatchKVCache.merge(empty_caches)
+
+        batch_full.extend(batch_empty)
+        self.assertEqual(batch_full.keys.shape[0], 5)
+        self.assertEqual(batch_full.offset.shape[0], 5)
+
+    def test_arrays_cache_extend_with_empty(self):
+        # test simple merge
+        c1 = ArraysCache(2)
+        c2 = ArraysCache(2)
+        c1[0] = mx.zeros((1, 4, 8))
+        c1[1] = mx.zeros((1, 4))
+        c2[0] = mx.zeros((1, 4, 8))
+        c2[1] = mx.zeros((1, 4))
+        full = ArraysCache.merge((c1, c2))
+        self.assertEqual(full[0].shape, (2, 4, 8))
+
+        # extend with empty
+        empty = ArraysCache.merge((ArraysCache(2),))
+        full.extend(empty)
+        self.assertEqual(full[0].shape, (3, 4, 8))
+        self.assertEqual(full[1].shape, (3, 4))
+        self.assertTrue(mx.all(full[0][2:] == 0))
+
+        # making an empty cache with 2 sequences and merging it with
+        # another one with 2 sequences
+        empty2 = ArraysCache.merge((ArraysCache(2), ArraysCache(2)))
+        content = ArraysCache.merge((c1, c2))
+        empty2.extend(content)
+        self.assertEqual(empty2[0].shape, (4, 4, 8))
+        self.assertEqual(empty2[1].shape, (4, 4))
+
+        # Extend content with empty
+        content = ArraysCache.merge((c1, c2))
+        empty2 = ArraysCache.merge((ArraysCache(2), ArraysCache(2)))
+        content.extend(empty2)
+        self.assertEqual(content[0].shape, (4, 4, 8))
+        self.assertEqual(content[1].shape, (4, 4))
+        self.assertEqual(content.make_mask(10).shape, (4, 10))
+
+        # multiple empty extensions accumulate correctly
+        stepwise = ArraysCache.merge((c1,))
+        stepwise.extend(ArraysCache(2))
+        stepwise.extend(ArraysCache.merge((ArraysCache(2), ArraysCache(2))))
+        self.assertEqual(stepwise[0].shape, (4, 4, 8))
+        self.assertEqual(stepwise[1].shape, (4, 4))
+
     def test_window_mask_with_full_kv_cache(self):
         c = KVCache()
         kv = mx.zeros((1, 1, 32, 128))

tests/test_server.py

@@ -4,13 +4,20 @@ import http
 import io
 import json
 import threading
+import types
 import unittest
 
 import mlx.core as mx
 import requests
 
 from mlx_lm.models.cache import KVCache
-from mlx_lm.server import APIHandler, LRUPromptCache, ResponseGenerator
+from mlx_lm.server import (
+    APIHandler,
+    LRUPromptCache,
+    Response,
+    ResponseGenerator,
+    _process_control_tokens,
+)
 from mlx_lm.utils import load
 
 
@@ -61,6 +68,9 @@ class DummyModelProvider:
         assert model in ["default_model", "chat_model"]
         return self.model, self.tokenizer
 
+    def load_default(self):
+        return self.load("default_model", None, "default_model")
+
 
 class MockCache:
     def __init__(self, value, is_trimmable: bool = True):
@@ -82,6 +92,71 @@ class MockCache:
         return n
 
 
+class TestProcessControlTokens(unittest.TestCase):
+    @staticmethod
+    def _r(text, state, match=None):
+        return Response(text, 0, state, match, 0.0, None, ())
+
+    def test_single_tool_call_passes_body_with_open_and_close_crossings(self):
+        r = self._r
+        stream = [
+            r("hi ", "normal"),
+            r("<tool_call>", "tool", match=(0,)),
+            r("body", "tool"),
+            r("</tool_call>", "normal", match=(1,)),
+            r(" bye", "normal"),
+        ]
+        ctx = types.SimpleNamespace(
+            sequences={(0,): "<tool_call>", (1,): "</tool_call>"}
+        )
+        out = list(_process_control_tokens(ctx, iter(stream)))
+
+        self.assertEqual("".join(t.text for t in out), "hi body bye")
+        states = [t.state for t in out]
+        self.assertEqual(sum(1 for a, b in zip(states, states[1:]) if a != b), 2)
+
+    def test_back_to_back_tool_calls_emit_state_crossings(self):
+        r = self._r
+        stream = [
+            r("<tool_call>", "tool", match=(0,)),
+            r("call1_body", "tool"),
+            r("</tool_call>", "normal", match=(1,)),
+            r("<tool_call>", "tool", match=(0,)),
+            r("call2_body", "tool"),
+            r("</tool_call>", "normal", match=(1,)),
+        ]
+        ctx = types.SimpleNamespace(
+            sequences={(0,): "<tool_call>", (1,): "</tool_call>"}
+        )
+        out = list(_process_control_tokens(ctx, iter(stream)))
+
+        self.assertEqual("".join(t.text for t in out), "call1_bodycall2_body")
+        states = [t.state for t in out]
+        crossings = sum(
+            1 for a, b in zip(states, states[1:]) if a == "tool" and b == "normal"
+        )
+        self.assertEqual(crossings, 2)
+
+    def test_multi_token_match_preserves_order(self):
+        r = self._r
+        match = (10, 11, 12)
+        stream = [
+            r("body", "tool"),
+            r("</", "tool"),
+            r("tool", "tool"),
+            r("_call>", "normal", match=match),
+            r(" ok", "normal"),
+        ]
+        ctx = types.SimpleNamespace(sequences={match: "</tool_call>"})
+        out = list(_process_control_tokens(ctx, iter(stream)))
+
+        self.assertEqual([t.text for t in out], ["body", "", "", "", " ok"])
+        self.assertEqual(
+            [t.state for t in out],
+            ["tool", "tool", "tool", "normal", "normal"],
+        )
+
+
 class TestServer(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
@@ -205,6 +280,33 @@ class TestServer(unittest.TestCase):
         self.assertIn("id", response_body)
         self.assertIn("choices", response_body)
 
+    def test_make_state_machine_empty_tool_call_end(self):
+        class FakeTokenizer:
+            has_thinking = False
+            has_tool_calling = True
+            tool_call_start = "[TOOL_CALLS]"
+            tool_call_end = ""
+            tool_call_start_tokens = (100,)
+            tool_call_end_tokens = ()
+            eos_token_ids = [2]
+
+            def convert_ids_to_tokens(self, t):
+                return f"<eos{t}>"
+
+        sm, _ = self.response_generator._make_state_machine(
+            ("fake-empty-end", None, None),
+            FakeTokenizer(),
+            stop_words=[],
+        )
+        state = sm.make_state()
+        state, _, s = sm.match(state, 100)
+        self.assertEqual(s, "tool")
+        for tok in [42, 43, 44]:
+            state, _, s = sm.match(state, tok)
+            self.assertEqual(s, "tool")
+        state, _, s = sm.match(state, 2)
+        self.assertIsNone(s)
+
     def test_handle_models(self):
         url = f"http://localhost:{self.port}/v1/models"
         response = requests.get(url)

tests/test_tokenizers.py

@@ -101,6 +101,14 @@ class TestTokenizers(unittest.TestCase):
         self.assertEqual(tokenizer.think_start, "<think>")
         self.assertEqual(tokenizer.think_end, "</think>")
 
+        tokenizer_repo = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+        tokenizer = load_tokenizer(tokenizer_repo)
+        self.assertFalse(tokenizer.has_thinking)
+        self.assertIsNone(tokenizer.think_start)
+        self.assertIsNone(tokenizer.think_end)
+        self.assertIsNone(tokenizer.think_start_id)
+        self.assertIsNone(tokenizer.think_end_id)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/test_tool_parsing.py

@@ -254,6 +254,27 @@ class TestToolParsing(unittest.TestCase):
             {"settings": {"enabled": True, "name": "test"}},
         )
 
+        # Hyphenated function name (e.g. manim-video)
+        test_case = (
+            'call:manim-video{mode:<|"|>plan<|"|>,prompt:<|"|>explain KV caching<|"|>}'
+        )
+        tool_call = gemma4.parse_tool_call(test_case, None)
+        self.assertEqual(tool_call["name"], "manim-video")
+        self.assertEqual(
+            tool_call["arguments"],
+            {"mode": "plan", "prompt": "explain KV caching"},
+        )
+
+        # Braces inside a string argument (e.g. code snippets or markdown in content)
+        test_case = (
+            'call:skill_manage{action:<|"|>create<|"|>,'
+            'content:<|"|>use a dict like {key: value} in your code<|"|>}'
+        )
+        tool_call = gemma4.parse_tool_call(test_case, None)
+        self.assertEqual(tool_call["name"], "skill_manage")
+        self.assertEqual(tool_call["arguments"]["action"], "create")
+        self.assertIn("{", tool_call["arguments"]["content"])
+
     def test_kimi_k2(self):
         # Single tool call
         test_case = (
@@ -292,6 +313,22 @@ class TestToolParsing(unittest.TestCase):
         ]
         self.assertEqual(tool_calls, expected)
 
+    def test_minimax_m2(self):
+        test_case = (
+            '<invoke name="search">\n'
+            '<parameter name="query">weather</parameter>\n'
+            "</invoke>\n"
+            '<invoke name="read_file">\n'
+            '<parameter name="path">/tmp/test.txt</parameter>\n'
+            "</invoke>"
+        )
+        expected = [
+            {"name": "search", "arguments": {"query": "weather"}},
+            {"name": "read_file", "arguments": {"path": "/tmp/test.txt"}},
+        ]
+        tool_calls = minimax_m2.parse_tool_call(test_case, None)
+        self.assertEqual(expected, tool_calls)
+
 
 if __name__ == "__main__":
     unittest.main()