merge conflict resolution with main

LICENSE

@@ -0,0 +1,176 @@
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README.md

@@ -398,4 +398,5 @@ If you use vLLM-MLX in your research or project, please cite:
 - [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) - Vision-language models
 - [mlx-audio](https://github.com/Blaizzy/mlx-audio) - Text-to-Speech and Speech-to-Text
 - [mlx-embeddings](https://github.com/Blaizzy/mlx-embeddings) - Text embeddings
+- [Rapid-MLX](https://github.com/raullenchai/Rapid-MLX) - Community fork of vllm-mlx
 - [vLLM](https://github.com/vllm-project/vllm) - High-throughput LLM serving

benchmarks/bench_reasoning_parser.py

@@ -0,0 +1,55 @@
+"""Benchmark: reasoning parser streaming performance.
+
+Measures per-token overhead of extract_reasoning_streaming() at various
+output lengths. Demonstrates the difference between O(N²) accumulated
+text scanning and O(1) state-machine tracking.
+
+Usage:
+    python benchmarks/bench_reasoning_parser.py
+"""
+
+import time
+
+from vllm_mlx.reasoning.qwen3_parser import Qwen3ReasoningParser
+
+
+def bench_streaming(parser, n_tokens: int, label: str) -> float:
+    """Simulate n_tokens of streaming through the parser. Returns total ms."""
+    parser.reset_state()
+
+    # Simulate: <think> + N reasoning tokens + </think> + 10 content tokens
+    tokens = ["<think>"]
+    tokens += [f"word{i} " for i in range(n_tokens)]
+    tokens += ["</think>"]
+    tokens += [f"answer{i} " for i in range(10)]
+
+    accumulated = ""
+    start = time.perf_counter()
+    for tok in tokens:
+        prev = accumulated
+        accumulated += tok
+        parser.extract_reasoning_streaming(prev, accumulated, tok)
+    elapsed = (time.perf_counter() - start) * 1000
+
+    print(f"  {label}: {n_tokens:>6} tokens -> {elapsed:>8.2f}ms "
+          f"({elapsed / (n_tokens + 11):.3f}ms/tok)")
+    return elapsed
+
+
+def main():
+    parser = Qwen3ReasoningParser()
+
+    print("Reasoning parser streaming benchmark")
+    print("=" * 60)
+    print()
+
+    for n in [50, 100, 200, 500, 1000, 2000, 5000]:
+        bench_streaming(parser, n, f"{n} tokens")
+
+    print()
+    print("At 50 tok/s, per-token budget is 20ms.")
+    print("Parser overhead should be <0.1ms/tok to be negligible.")
+
+
+if __name__ == "__main__":
+    main()

docs/reference/models.md

@@ -12,7 +12,7 @@ Browse thousands of pre-optimized models at: **https://huggingface.co/mlx-commun
 | Mistral / Devstral | 7B, Mixtral 8x7B | 4-bit, 8-bit |
 | Qwen2/Qwen3 | 0.5B to 72B | Various |
 | DeepSeek V3, R1 | 7B, 33B, 67B | 4-bit |
-| Gemma 2, 3 | 2B, 9B, 27B | 4-bit |
+| Gemma 2, 3, 4 | 2B, 9B, 27B | 4-bit |
 | GLM-4.7 | Flash, Base | 4-bit, 8-bit |
 | Kimi K2 | Various | 4-bit |
 | Phi-3 | 3.8B, 14B | 4-bit |
@@ -35,6 +35,7 @@ Browse thousands of pre-optimized models at: **https://huggingface.co/mlx-commun
 | **Qwen-VL** | `Qwen3-VL-4B-Instruct-3bit`, `Qwen3-VL-8B-Instruct-4bit`, `Qwen2-VL-2B/7B-Instruct-4bit` |
 | **LLaVA** | `llava-1.5-7b-4bit`, `llava-v1.6-mistral-7b-4bit`, `llava-llama-3-8b-v1_1-4bit` |
 | **Idefics** | `Idefics3-8B-Llama3-4bit`, `idefics2-8b-4bit` |
+| **Gemma 4** | `gemma-4-e2b-it-mxfp4` (vision + audio) |
 | **PaliGemma** | `paligemma2-3b-mix-224-4bit`, `paligemma-3b-mix-224-8bit` |
 | **Pixtral** | `pixtral-12b-4bit`, `pixtral-12b-8bit` |
 | **Molmo** | `Molmo-7B-D-0924-4bit`, `Molmo-7B-D-0924-8bit` |
@@ -72,7 +73,7 @@ vllm-mlx auto-detects multimodal models by name patterns:
 - Contains "VL", "Vision", "vision"
 - Contains "llava", "idefics", "paligemma"
 - Contains "pixtral", "molmo", "deepseek-vl"
-- Contains "MedGemma", "Gemma-3" (vision variants)
+- Contains "MedGemma", "Gemma-3", "Gemma-4" (multimodal variants)
 
 ## Using Models
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "vllm-mlx"
-version = "0.2.7"
+version = "0.2.8"
 description = "vLLM-like inference for Apple Silicon - GPU-accelerated Text, Image, Video & Audio on Mac"
 readme = "README.md"
 license = {text = "Apache-2.0"}
@@ -30,7 +30,7 @@ classifiers = [
 dependencies = [
     "mlx>=0.29.0",
     "mlx-lm>=0.31.0",  # 0.31+ required for ArraysCache native batching (hybrid models)
-    "mlx-vlm>=0.1.0",  # VLM support
+    "mlx-vlm>=0.4.3",  # 0.4.3+ required for Gemma 4 support
     "transformers>=5.0.0",  # mlx-lm 0.30.5+ requires transformers 5.0 (rc3 bug fixed in stable)
     "tokenizers>=0.19.0",
     "huggingface-hub>=0.23.0",
@@ -44,7 +44,7 @@ dependencies = [
     # Video processing for VLM
     "opencv-python>=4.8.0",
     # Vision processor (required for transformers AutoProcessor)
-    "torchvision>=0.18.0",
+    "torchvision>=0.21.0",
     # Resource monitoring
     "psutil>=5.9.0",
     # Server
@@ -75,7 +75,7 @@ vllm = [
 ]
 vision = [
     "torch>=2.3.0",
-    "torchvision>=0.18.0",
+    "torchvision>=0.21.0",
 ]
 # Audio dependencies for TTS/STT (mlx-audio)
 audio = [