feat(studio): MLX training tab on Apple Silicon (LoRA / full FT, VLM, export)

.gitignore

@@ -228,3 +228,4 @@ setup_leo.sh
 server.pid
 *.log
 package-lock.json
+llama.cpp/

install.sh

@@ -1721,6 +1721,12 @@ else
     fi
 fi
 
+# ── Install mlx-vlm on Apple Silicon (optional, for VLM training) ──
+if [ "$OS" = "macos" ] && [ "$_ARCH" = "arm64" ]; then
+    substep "installing mlx-vlm (VLM training support)..."
+    run_install_cmd "install mlx-vlm" uv pip install --python "$_VENV_PY" mlx-vlm
+fi
+
 # ── Run studio setup ──
 tauri_log "STEP" "Running Studio setup"
 # When --local, use the repo's own setup.sh directly.

pyproject.toml

@@ -89,7 +89,7 @@ huggingfacenotorch = [
 ]
 huggingface = [
     "unsloth[huggingfacenotorch]",
-    "unsloth_zoo>=2026.5.1",
+    "unsloth_zoo>=2026.4.8",
     "torchvision",
     "unsloth[triton]",
 ]
@@ -579,7 +579,7 @@ colab-ampere-torch220 = [
     "flash-attn>=2.6.3 ; ('linux' in sys_platform)",
 ]
 colab-new = [
-    "unsloth_zoo>=2026.5.1",
+    "unsloth_zoo>=2026.4.8",
     "packaging",
     "tyro",
     "transformers>=4.51.3,!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.0,!=4.57.4,!=4.57.5,!=5.0.0,!=5.1.0,<=5.5.0",

studio/backend/core/inference/mlx_inference.py

@@ -0,0 +1,354 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+"""MLX inference backend for Apple Silicon.
+
+Drop-in replacement for InferenceBackend — same interface, uses mlx-lm/mlx-vlm
+instead of torch/transformers for model loading and generation.
+"""
+
+import threading
+from typing import Optional, Generator
+from loggers import get_logger
+
+logger = get_logger(__name__)
+
+
+class MLXInferenceBackend:
+    def __init__(self):
+        self.models = {}
+        self.active_model_name = None
+        self.loading_models = set()
+        self.loaded_local_models = []
+        self.device = "mlx"
+        self._generation_lock = threading.Lock()
+
+        # MLX state
+        self._model = None
+        self._tokenizer = None
+        self._processor = None
+        self._is_vlm = False
+        self._config = {}
+
+    def load_model(
+        self,
+        config,
+        max_seq_length = 2048,
+        load_in_4bit = True,
+        hf_token = None,
+        trust_remote_code = False,
+        gpu_ids = None,
+        dtype = None,
+    ) -> bool:
+        import mlx.core as mx
+
+        model_name = config.identifier if hasattr(config, "identifier") else str(config)
+        is_vision = getattr(config, "is_vision", False)
+
+        if hf_token:
+            import os
+
+            os.environ["HF_TOKEN"] = hf_token
+
+        if mx.metal.is_available():
+            mx.set_wired_limit(mx.device_info()["max_recommended_working_set_size"])
+
+        is_lora = getattr(config, "is_lora", False)
+
+        logger.info(
+            "Loading %s via %s (is_lora=%s)",
+            model_name,
+            "mlx-vlm" if is_vision else "mlx-lm",
+            is_lora,
+        )
+
+        try:
+            from unsloth_zoo.mlx_loader import FastMLXModel
+        except ImportError as e:
+            raise ImportError(
+                "Unsloth: MLX inference requires unsloth-zoo with the MLX modules "
+                "(unsloth_zoo.mlx_loader). Reinstall via install.sh on Apple Silicon."
+            ) from e
+
+        model, tokenizer_or_processor = FastMLXModel.from_pretrained(
+            model_name,
+            max_seq_length = max_seq_length,
+            dtype = dtype,
+            load_in_4bit = load_in_4bit,
+            token = hf_token,
+            trust_remote_code = trust_remote_code,
+            text_only = False if is_vision else True,
+        )
+
+        if is_vision:
+            processor = tokenizer_or_processor
+            self._model = model
+            self._processor = processor
+            self._tokenizer = getattr(processor, "tokenizer", processor)
+            self._is_vlm = True
+        else:
+            tokenizer = tokenizer_or_processor
+            self._model = model
+            self._tokenizer = tokenizer
+            self._processor = None
+            self._is_vlm = False
+
+        self.active_model_name = model_name
+        self.models[model_name] = {
+            "model": self._model,
+            "tokenizer": self._tokenizer,
+            "processor": self._processor,
+            "is_vision": is_vision,
+            "is_lora": getattr(config, "is_lora", False),
+            "is_audio": False,
+            "audio_type": None,
+            "has_audio_input": False,
+        }
+
+        logger.info("Model %s loaded successfully", model_name)
+        return True
+
+    def unload_model(self, model_name: str) -> bool:
+        import mlx.core as mx
+        import gc
+
+        if model_name in self.models:
+            del self.models[model_name]
+        self._model = None
+        self._tokenizer = None
+        self._processor = None
+        if self.active_model_name == model_name:
+            self.active_model_name = None
+        gc.collect()
+        mx.clear_cache()
+        logger.info("Model %s unloaded", model_name)
+        return True
+
+    def generate_chat_response(
+        self,
+        messages,
+        system_prompt = "",
+        image = None,
+        temperature = 0.7,
+        top_p = 0.9,
+        top_k = 40,
+        min_p = 0.0,
+        max_new_tokens = 256,
+        repetition_penalty = 1.0,
+        cancel_event = None,
+    ) -> Generator[str, None, None]:
+        if self._model is None:
+            raise RuntimeError("No model loaded")
+
+        # Build messages with system prompt
+        full_messages = []
+        if system_prompt:
+            full_messages.append({"role": "system", "content": system_prompt})
+        full_messages.extend(messages)
+
+        # Inject image into the last user message for VLM
+        if self._is_vlm and image is not None:
+            for msg in reversed(full_messages):
+                if msg.get("role") == "user":
+                    content = msg.get("content", "")
+                    if isinstance(content, str):
+                        msg["content"] = [
+                            {"type": "image"},
+                            {"type": "text", "text": content},
+                        ]
+                    elif isinstance(content, list):
+                        # Prepend image if not already there
+                        has_image = any(
+                            p.get("type") == "image"
+                            for p in content
+                            if isinstance(p, dict)
+                        )
+                        if not has_image:
+                            content.insert(0, {"type": "image"})
+                    break
+
+        if self._is_vlm:
+            yield from self._generate_vlm(
+                full_messages,
+                image,
+                temperature,
+                top_p,
+                top_k,
+                min_p,
+                max_new_tokens,
+                repetition_penalty,
+                cancel_event,
+            )
+        else:
+            yield from self._generate_text(
+                full_messages,
+                temperature,
+                top_p,
+                top_k,
+                min_p,
+                max_new_tokens,
+                repetition_penalty,
+                cancel_event,
+            )
+
+    def _generate_text(
+        self,
+        messages,
+        temperature,
+        top_p,
+        top_k,
+        min_p,
+        max_new_tokens,
+        repetition_penalty,
+        cancel_event,
+    ):
+        from mlx_lm import stream_generate
+        from mlx_lm.sample_utils import make_sampler, make_logits_processors
+
+        prompt = self._tokenizer.apply_chat_template(
+            messages,
+            tokenize = False,
+            add_generation_prompt = True,
+        )
+        if prompt is None:
+            raise RuntimeError(
+                "apply_chat_template returned None — tokenizer may be incompatible"
+            )
+
+        sampler = make_sampler(
+            temp = temperature,
+            top_p = top_p,
+            top_k = int(top_k or 0),
+            min_p = float(min_p or 0.0),
+            min_tokens_to_keep = 1,
+        )
+        # Only build a logits processor when we actually have a non-trivial
+        # repetition penalty (1.0 is the no-op value).
+        logits_processors = None
+        if repetition_penalty is not None and float(repetition_penalty) not in (
+            0.0,
+            1.0,
+        ):
+            logits_processors = make_logits_processors(
+                repetition_penalty = float(repetition_penalty),
+            )
+
+        token_ids = []
+        logger.info(
+            "Generating: prompt_len=%d, max_tokens=%d, model=%s, tokenizer=%s",
+            len(prompt),
+            max_new_tokens,
+            type(self._model).__name__,
+            type(self._tokenizer).__name__,
+        )
+        with self._generation_lock:
+            try:
+                gen_kwargs = dict(
+                    prompt = prompt,
+                    max_tokens = max_new_tokens,
+                    sampler = sampler,
+                )
+                if logits_processors is not None:
+                    gen_kwargs["logits_processors"] = logits_processors
+                for response in stream_generate(
+                    self._model,
+                    self._tokenizer,
+                    **gen_kwargs,
+                ):
+                    token_ids.append(response.token)
+                    # Decode full sequence with skip_special_tokens — same as GPU
+                    cumulative = self._tokenizer.decode(
+                        token_ids,
+                        skip_special_tokens = True,
+                    )
+                    yield cumulative
+
+                    if cancel_event and cancel_event.is_set():
+                        break
+            except Exception as e:
+                import traceback
+
+                logger.error("stream_generate failed:\n%s", traceback.format_exc())
+                raise
+
+    def _generate_vlm(
+        self,
+        messages,
+        image,
+        temperature,
+        top_p,
+        top_k,
+        min_p,
+        max_new_tokens,
+        repetition_penalty,
+        cancel_event,
+    ):
+        from mlx_vlm import stream_generate as vlm_stream
+
+        # Apply chat template
+        chat_fn = getattr(self._processor, "apply_chat_template", None)
+        if (
+            chat_fn is None
+            or not hasattr(self._processor, "chat_template")
+            or self._processor.chat_template is None
+        ):
+            tok = getattr(self._processor, "tokenizer", self._processor)
+            chat_fn = tok.apply_chat_template
+
+        prompt = chat_fn(messages, tokenize = False, add_generation_prompt = True)
+
+        # For VLM: always use mlx_vlm's stream_generate which handles
+        # pixel_values properly (passes None for text-only, image for VLM)
+        images = [image] if image is not None else None
+
+        cumulative = ""
+        logger.info(
+            "VLM generating: prompt_len=%d, has_image=%s",
+            len(prompt),
+            image is not None,
+        )
+        # mlx_vlm.stream_generate forwards **kwargs into generate_step, which
+        # accepts temp/top_p/top_k/repetition_penalty (and builds the sampler
+        # + logits_processors internally). Pass them through.
+        # NOTE: mlx_vlm.generate_step expects ``temperature=`` (long form) —
+        # passing ``temp=`` silently falls into **kwargs and is ignored,
+        # leaving generation stuck at the default 0.0 (greedy).
+        vlm_kwargs = dict(
+            max_tokens = max_new_tokens,
+            temperature = temperature,
+            top_p = top_p,
+            top_k = int(top_k or 0),
+            min_p = float(min_p or 0.0),
+        )
+        if repetition_penalty is not None and float(repetition_penalty) not in (
+            0.0,
+            1.0,
+        ):
+            vlm_kwargs["repetition_penalty"] = float(repetition_penalty)
+
+        with self._generation_lock:
+            for response in vlm_stream(
+                self._model,
+                self._processor,
+                prompt,
+                images,
+                **vlm_kwargs,
+            ):
+                token_text = (
+                    response.text if hasattr(response, "text") else str(response)
+                )
+                cumulative += token_text
+                yield cumulative
+                if cancel_event and cancel_event.is_set():
+                    break
+
+    def generate_with_adapter_control(
+        self, use_adapter = None, cancel_event = None, **gen_kwargs
+    ) -> Generator[str, None, None]:
+        # MLX LoRA adapter toggling not yet supported — generate normally
+        yield from self.generate_chat_response(cancel_event = cancel_event, **gen_kwargs)
+
+    def reset_generation_state(self):
+        import mlx.core as mx
+        import gc
+
+        gc.collect()
+        mx.clear_cache()