feat: add tensor parallelism support for Step 3.5 Flash

Add Step3p5ShardingStrategy to auto_parallel.py following the
DeepSeek pattern (shared expert + routed experts). Shard attention
q/k/v/o projections across devices and MoE expert weights in-place
with all-reduce synchronization.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.github/workflows/pipeline.yml

@@ -142,4 +142,4 @@ jobs:
           # Run pytest outside sandbox (needs GPU access for MLX)
           export HOME="$RUNNER_TEMP"
           export EXO_TESTS=1
-          $TEST_ENV/bin/python -m pytest src -m "not slow" --import-mode=importlib
+          EXO_RESOURCES_DIR="$PWD/resources" $TEST_ENV/bin/python -m pytest src -m "not slow" --import-mode=importlib

app/EXO/EXO/ViewModels/InstanceViewModel.swift

@@ -216,7 +216,7 @@ struct InstanceTaskViewModel: Identifiable, Equatable {
     let promptPreview: String?
     let errorMessage: String?
     let subtitle: String?
-    let parameters: ChatCompletionTaskParameters?
+    let parameters: TextGenerationTaskParameters?
 
     var title: String {
         switch kind {

packaging/pyinstaller/exo.spec

@@ -10,6 +10,7 @@ PROJECT_ROOT = Path.cwd()
 SOURCE_ROOT = PROJECT_ROOT / "src"
 ENTRYPOINT = SOURCE_ROOT / "exo" / "__main__.py"
 DASHBOARD_DIR = PROJECT_ROOT / "dashboard" / "build"
+RESOURCES_DIR = PROJECT_ROOT / "resources"
 EXO_SHARED_MODELS_DIR = SOURCE_ROOT / "exo" / "shared" / "models"
 
 if not ENTRYPOINT.is_file():
@@ -18,6 +19,9 @@ if not ENTRYPOINT.is_file():
 if not DASHBOARD_DIR.is_dir():
     raise SystemExit(f"Dashboard assets are missing: {DASHBOARD_DIR}")
 
+if not RESOURCES_DIR.is_dir():
+    raise SystemExit(f"Resource assets are missing: {RESOURCES_DIR}")
+
 if not EXO_SHARED_MODELS_DIR.is_dir():
     raise SystemExit(f"Shared model assets are missing: {EXO_SHARED_MODELS_DIR}")
 
@@ -58,6 +62,7 @@ HIDDEN_IMPORTS = sorted(
 
 DATAS: list[tuple[str, str]] = [
     (str(DASHBOARD_DIR), "dashboard"),
+    (str(RESOURCES_DIR), "resources"),
     (str(MLX_LIB_DIR), "mlx/lib"),
     (str(EXO_SHARED_MODELS_DIR), "exo/shared/models"),
 ]

resources/image_model_cards/exolabs--FLUX.1-Krea-dev-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15475325472
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5950704160
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-Krea-dev-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21426029632
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11901408320
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-Krea-dev.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33327437952
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23802816640
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15475325472
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5950704160
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21426029632
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11901408320
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33327437952
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23802816640
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15470210592
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5945589280
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21415799872
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11891178560
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33306978432
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23782357120
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-4bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-4bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 26799533856
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 10215200544
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-8bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-8bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 37014734400
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 20430401088
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-4bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509-4bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 26799533856
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 10215200544
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-8bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509-8bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 37014734400
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 20430401088
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 57445135488
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 40860802176
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 57445135488
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 40860802176
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/inference_model_cards/mlx-community--DeepSeek-V3.1-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/DeepSeek-V3.1-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 405874409472

resources/inference_model_cards/mlx-community--DeepSeek-V3.1-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/DeepSeek-V3.1-8bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 765577920512

resources/inference_model_cards/mlx-community--GLM-4.5-Air-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.5-Air-8bit"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = false
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 122406567936

resources/inference_model_cards/mlx-community--GLM-4.5-Air-bf16.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.5-Air-bf16"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 229780750336

resources/inference_model_cards/mlx-community--GLM-4.7-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-4bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 198556925568

resources/inference_model_cards/mlx-community--GLM-4.7-6bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-6bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 286737579648

resources/inference_model_cards/mlx-community--GLM-4.7-8bit-gs32.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-8bit-gs32"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 396963397248

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-4bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 19327352832

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-5bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-5bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 22548578304

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-6bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-6bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 26843545600

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-8bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 34359738368

resources/inference_model_cards/mlx-community--Kimi-K2-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2-Instruct-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 620622774272

resources/inference_model_cards/mlx-community--Kimi-K2-Thinking.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2-Thinking"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 706522120192

resources/inference_model_cards/mlx-community--Kimi-K2.5.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2.5"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 662498705408

resources/inference_model_cards/mlx-community--Llama-3.2-1B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+n_layers = 16
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 729808896

resources/inference_model_cards/mlx-community--Llama-3.2-3B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 1863319552

resources/inference_model_cards/mlx-community--Llama-3.2-3B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-8bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 3501195264

resources/inference_model_cards/mlx-community--Llama-3.3-70B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 40652242944

resources/inference_model_cards/mlx-community--Llama-3.3-70B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-8bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 76799803392

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-70B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 40652242944

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 4637851648

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 8954839040

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-bf16.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 16882073600

resources/inference_model_cards/mlx-community--MiniMax-M2.1-3bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/MiniMax-M2.1-3bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 100086644736

resources/inference_model_cards/mlx-community--MiniMax-M2.1-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/MiniMax-M2.1-8bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 242986745856

resources/inference_model_cards/mlx-community--Qwen3-0.6B-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-0.6B-4bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 342884352

resources/inference_model_cards/mlx-community--Qwen3-0.6B-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-0.6B-8bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 698351616

resources/inference_model_cards/mlx-community--Qwen3-235B-A22B-Instruct-2507-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 141733920768

resources/inference_model_cards/mlx-community--Qwen3-235B-A22B-Instruct-2507-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 268435456000

resources/inference_model_cards/mlx-community--Qwen3-30B-A3B-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-30B-A3B-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 17612931072

resources/inference_model_cards/mlx-community--Qwen3-30B-A3B-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-30B-A3B-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 33279705088

resources/inference_model_cards/mlx-community--Qwen3-Coder-480B-A35B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 289910292480

resources/inference_model_cards/mlx-community--Qwen3-Coder-480B-A35B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 579820584960

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 46976204800

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 88814387200

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Thinking-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 47080074240

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Thinking-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 88814387200

resources/inference_model_cards/mlx-community--Step-3.5-Flash-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Step-3.5-Flash-4bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 114572190076

resources/inference_model_cards/mlx-community--Step-3.5-Flash-6bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Step-3.5-Flash-6bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 159039627774

resources/inference_model_cards/mlx-community--Step-3.5-Flash-8Bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Step-3.5-Flash-8Bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 209082699847

resources/inference_model_cards/mlx-community--gpt-oss-120b-MXFP4-Q8.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+n_layers = 36
+hidden_size = 2880
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 70652212224

resources/inference_model_cards/mlx-community--gpt-oss-20b-MXFP4-Q8.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+n_layers = 24
+hidden_size = 2880
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 12025908224

resources/inference_model_cards/mlx-community--llama-3.3-70b-instruct-fp16.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/llama-3.3-70b-instruct-fp16"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 144383672320

src/exo/download/impl_shard_downloader.py

@@ -7,7 +7,7 @@ from loguru import logger
 
 from exo.download.download_utils import RepoDownloadProgress, download_shard
 from exo.download.shard_downloader import ShardDownloader
-from exo.shared.models.model_cards import MODEL_CARDS, ModelCard, ModelId
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -160,7 +160,7 @@ class ResumableShardDownloader(ShardDownloader):
         # Kick off download status coroutines concurrently
         tasks = [
             asyncio.create_task(_status_for_model(model_card.model_id))
-            for model_card in MODEL_CARDS.values()
+            for model_card in await get_model_cards()
         ]
 
         for task in asyncio.as_completed(tasks):

src/exo/master/api.py

@@ -40,6 +40,7 @@ from exo.master.image_store import ImageStore
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
 from exo.shared.constants import (
+    DASHBOARD_DIR,
     EXO_IMAGE_CACHE_DIR,
     EXO_MAX_CHUNK_SIZE,
     EXO_TRACING_CACHE_DIR,
@@ -47,9 +48,9 @@ from exo.shared.constants import (
 from exo.shared.election import ElectionMessage
 from exo.shared.logging import InterceptLogger
 from exo.shared.models.model_cards import (
-    MODEL_CARDS,
     ModelCard,
     ModelId,
+    get_model_cards,
 )
 from exo.shared.tracing import TraceEvent, compute_stats, export_trace, load_trace_file
 from exo.shared.types.api import (
@@ -138,7 +139,6 @@ from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.banner import print_startup_banner
 from exo.utils.channels import Receiver, Sender, channel
-from exo.utils.dashboard_path import find_dashboard
 from exo.utils.event_buffer import OrderedBuffer
 
 
@@ -146,18 +146,6 @@ def _format_to_content_type(image_format: Literal["png", "jpeg", "webp"] | None)
     return f"image/{image_format or 'png'}"
 
 
-async def resolve_model_card(model_id: ModelId) -> ModelCard:
-    if model_id in MODEL_CARDS:
-        model_card = MODEL_CARDS[model_id]
-        return model_card
-
-    for card in MODEL_CARDS.values():
-        if card.model_id == ModelId(model_id):
-            return card
-
-    return await ModelCard.from_hf(model_id)
-
-
 class API:
     def __init__(
         self,
@@ -204,7 +192,7 @@ class API:
         self.app.mount(
             "/",
             StaticFiles(
-                directory=find_dashboard(),
+                directory=DASHBOARD_DIR,
                 html=True,
             ),
             name="dashboard",
@@ -381,10 +369,7 @@ class API:
         if len(list(self.state.topology.list_nodes())) == 0:
             return PlacementPreviewResponse(previews=[])
 
-        cards = [card for card in MODEL_CARDS.values() if card.model_id == model_id]
-        if not cards:
-            raise HTTPException(status_code=404, detail=f"Model {model_id} not found")
-
+        model_card = await ModelCard.load(model_id)
         instance_combinations: list[tuple[Sharding, InstanceMeta, int]] = []
         for sharding in (Sharding.Pipeline, Sharding.Tensor):
             for instance_meta in (InstanceMeta.MlxRing, InstanceMeta.MlxJaccl):
@@ -399,96 +384,93 @@ class API:
         # TODO: PDD
         # instance_combinations.append((Sharding.PrefillDecodeDisaggregation, InstanceMeta.MlxRing, 1))
 
-        for model_card in cards:
-            for sharding, instance_meta, min_nodes in instance_combinations:
-                try:
-                    placements = get_instance_placements(
-                        PlaceInstance(
-                            model_card=model_card,
-                            sharding=sharding,
-                            instance_meta=instance_meta,
-                            min_nodes=min_nodes,
-                        ),
-                        node_memory=self.state.node_memory,
-                        node_network=self.state.node_network,
-                        topology=self.state.topology,
-                        current_instances=self.state.instances,
-                        required_nodes=required_nodes,
-                    )
-                except ValueError as exc:
-                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
-                        previews.append(
-                            PlacementPreview(
-                                model_id=model_card.model_id,
-                                sharding=sharding,
-                                instance_meta=instance_meta,
-                                instance=None,
-                                error=str(exc),
-                            )
-                        )
-                    seen.add((model_card.model_id, sharding, instance_meta, 0))
-                    continue
-
-                current_ids = set(self.state.instances.keys())
-                new_instances = [
-                    instance
-                    for instance_id, instance in placements.items()
-                    if instance_id not in current_ids
-                ]
-
-                if len(new_instances) != 1:
-                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
-                        previews.append(
-                            PlacementPreview(
-                                model_id=model_card.model_id,
-                                sharding=sharding,
-                                instance_meta=instance_meta,
-                                instance=None,
-                                error="Expected exactly one new instance from placement",
-                            )
-                        )
-                    seen.add((model_card.model_id, sharding, instance_meta, 0))
-                    continue
-
-                instance = new_instances[0]
-                shard_assignments = instance.shard_assignments
-                placement_node_ids = list(shard_assignments.node_to_runner.keys())
-
-                memory_delta_by_node: dict[str, int] = {}
-                if placement_node_ids:
-                    total_bytes = model_card.storage_size.in_bytes
-                    per_node = total_bytes // len(placement_node_ids)
-                    remainder = total_bytes % len(placement_node_ids)
-                    for index, node_id in enumerate(
-                        sorted(placement_node_ids, key=str)
-                    ):
-                        extra = 1 if index < remainder else 0
-                        memory_delta_by_node[str(node_id)] = per_node + extra
-
-                if (
-                    model_card.model_id,
-                    sharding,
-                    instance_meta,
-                    len(placement_node_ids),
-                ) not in seen:
+        for sharding, instance_meta, min_nodes in instance_combinations:
+            try:
+                placements = get_instance_placements(
+                    PlaceInstance(
+                        model_card=model_card,
+                        sharding=sharding,
+                        instance_meta=instance_meta,
+                        min_nodes=min_nodes,
+                    ),
+                    node_memory=self.state.node_memory,
+                    node_network=self.state.node_network,
+                    topology=self.state.topology,
+                    current_instances=self.state.instances,
+                    required_nodes=required_nodes,
+                )
+            except ValueError as exc:
+                if (model_card.model_id, sharding, instance_meta, 0) not in seen:
                     previews.append(
                         PlacementPreview(
                             model_id=model_card.model_id,
                             sharding=sharding,
                             instance_meta=instance_meta,
-                            instance=instance,
-                            memory_delta_by_node=memory_delta_by_node or None,
-                            error=None,
+                            instance=None,
+                            error=str(exc),
                         )
                     )
-                seen.add(
-                    (
-                        model_card.model_id,
-                        sharding,
-                        instance_meta,
-                        len(placement_node_ids),
+                seen.add((model_card.model_id, sharding, instance_meta, 0))
+                continue
+
+            current_ids = set(self.state.instances.keys())
+            new_instances = [
+                instance
+                for instance_id, instance in placements.items()
+                if instance_id not in current_ids
+            ]
+
+            if len(new_instances) != 1:
+                if (model_card.model_id, sharding, instance_meta, 0) not in seen:
+                    previews.append(
+                        PlacementPreview(
+                            model_id=model_card.model_id,
+                            sharding=sharding,
+                            instance_meta=instance_meta,
+                            instance=None,
+                            error="Expected exactly one new instance from placement",
+                        )
+                    )
+                seen.add((model_card.model_id, sharding, instance_meta, 0))
+                continue
+
+            instance = new_instances[0]
+            shard_assignments = instance.shard_assignments
+            placement_node_ids = list(shard_assignments.node_to_runner.keys())
+
+            memory_delta_by_node: dict[str, int] = {}
+            if placement_node_ids:
+                total_bytes = model_card.storage_size.in_bytes
+                per_node = total_bytes // len(placement_node_ids)
+                remainder = total_bytes % len(placement_node_ids)
+                for index, node_id in enumerate(sorted(placement_node_ids, key=str)):
+                    extra = 1 if index < remainder else 0
+                    memory_delta_by_node[str(node_id)] = per_node + extra
+
+            if (
+                model_card.model_id,
+                sharding,
+                instance_meta,
+                len(placement_node_ids),
+            ) not in seen:
+                previews.append(
+                    PlacementPreview(
+                        model_id=model_card.model_id,
+                        sharding=sharding,
+                        instance_meta=instance_meta,
+                        instance=instance,
+                        memory_delta_by_node=memory_delta_by_node or None,
+                        error=None,
                     )
                 )
+            seen.add(
+                (
+                    model_card.model_id,
+                    sharding,
+                    instance_meta,
+                    len(placement_node_ids),
+                )
+            )
 
         return PlacementPreviewResponse(previews=previews)
 
@@ -652,23 +634,21 @@ class API:
         response = await self._collect_text_generation_with_stats(command.command_id)
         return response
 
-    async def _resolve_and_validate_text_model(self, model: ModelId) -> ModelId:
+    async def _resolve_and_validate_text_model(self, model_id: ModelId) -> ModelId:
         """Validate a text model exists and return the resolved model ID.
 
         Raises HTTPException 404 if no instance is found for the model.
         """
-        model_card = await resolve_model_card(model)
-        resolved = model_card.model_id
         if not any(
-            instance.shard_assignments.model_id == resolved
+            instance.shard_assignments.model_id == model_id
             for instance in self.state.instances.values()
         ):
-            await self._trigger_notify_user_to_download_model(resolved)
+            await self._trigger_notify_user_to_download_model(model_id)
             raise HTTPException(
                 status_code=404,
-                detail=f"No instance found for model {resolved}",
+                detail=f"No instance found for model {model_id}",
             )
-        return resolved
+        return model_id
 
     async def _validate_image_model(self, model: ModelId) -> ModelId:
         """Validate model exists and return resolved model ID.
@@ -1237,7 +1217,7 @@ class API:
                     supports_tensor=card.supports_tensor,
                     tasks=[task.value for task in card.tasks],
                 )
-                for card in MODEL_CARDS.values()
+                for card in await get_model_cards()
             ]
         )
 

src/exo/shared/constants.py

@@ -2,6 +2,8 @@ import os
 import sys
 from pathlib import Path
 
+from exo.utils.dashboard_path import find_dashboard, find_resources
+
 _EXO_HOME_ENV = os.environ.get("EXO_HOME", None)
 
 
@@ -31,6 +33,14 @@ EXO_MODELS_DIR = (
     if _EXO_MODELS_DIR_ENV is None
     else Path.home() / _EXO_MODELS_DIR_ENV
 )
+_RESOURCES_DIR_ENV = os.environ.get("EXO_RESOURCES_DIR", None)
+RESOURCES_DIR = (
+    find_resources() if _RESOURCES_DIR_ENV is None else Path.home() / _RESOURCES_DIR_ENV
+)
+_DASHBOARD_DIR_ENV = os.environ.get("EXO_DASHBOARD_DIR", None)
+DASHBOARD_DIR = (
+    find_dashboard() if _RESOURCES_DIR_ENV is None else Path.home() / _RESOURCES_DIR_ENV
+)
 
 # Log files (data/logs or cache)
 EXO_LOG = EXO_CACHE_HOME / "exo.log"

src/exo/shared/models/model_cards.py

@@ -12,16 +12,42 @@ from pydantic import (
     BaseModel,
     Field,
     PositiveInt,
+    ValidationError,
     field_validator,
     model_validator,
 )
+from tomlkit.exceptions import TOMLKitError
 
-from exo.shared.constants import EXO_ENABLE_IMAGE_MODELS
+from exo.shared.constants import EXO_ENABLE_IMAGE_MODELS, RESOURCES_DIR
 from exo.shared.types.common import ModelId
 from exo.shared.types.memory import Memory
 from exo.utils.pydantic_ext import CamelCaseModel
 
-_card_cache: dict[str, "ModelCard"] = {}
+# kinda ugly...
+# TODO: load search path from config.toml
+_csp = [Path(RESOURCES_DIR) / "inference_model_cards"]
+if EXO_ENABLE_IMAGE_MODELS:
+    _csp.append(Path(RESOURCES_DIR) / "image_model_cards")
+
+CARD_SEARCH_PATH = _csp
+
+_card_cache: dict[ModelId, "ModelCard"] = {}
+
+
+async def _refresh_card_cache():
+    for path in CARD_SEARCH_PATH:
+        async for toml_file in path.rglob("*.toml"):
+            try:
+                card = await ModelCard.load_from_path(toml_file)
+                _card_cache[card.model_id] = card
+            except (ValidationError, TOMLKitError):
+                pass
+
+
+async def get_model_cards() -> list["ModelCard"]:
+    if len(_card_cache) == 0:
+        await _refresh_card_cache()
+    return list(_card_cache.values())
 
 
 class ModelTask(str, Enum):
@@ -55,28 +81,33 @@ class ModelCard(CamelCaseModel):
 
     async def save(self, path: Path) -> None:
         async with await open_file(path, "w") as f:
-            py = self.model_dump()
+            py = self.model_dump(exclude_none=True)
             data = tomlkit.dumps(py)  # pyright: ignore[reportUnknownMemberType]
             await f.write(data)
 
+    async def save_to_default_path(self):
+        await self.save(Path(RESOURCES_DIR) / (self.model_id.normalize() + ".toml"))
+
     @staticmethod
     async def load_from_path(path: Path) -> "ModelCard":
         async with await open_file(path, "r") as f:
             py = tomlkit.loads(await f.read())
             return ModelCard.model_validate(py)
 
+    # Is it okay that model card.load defaults to network access if the card doesn't exist? do we want to be more explicit here?
     @staticmethod
     async def load(model_id: ModelId) -> "ModelCard":
-        for card in MODEL_CARDS.values():
-            if card.model_id == model_id:
-                return card
-        return await ModelCard.from_hf(model_id)
-
-    @staticmethod
-    async def from_hf(model_id: ModelId) -> "ModelCard":
-        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        if model_id not in _card_cache:
+            await _refresh_card_cache()
         if (mc := _card_cache.get(model_id)) is not None:
             return mc
+
+        return await ModelCard.fetch_from_hf(model_id)
+
+    @staticmethod
+    async def fetch_from_hf(model_id: ModelId) -> "ModelCard":
+        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        # TODO: failure if files do not exist
         config_data = await get_config_data(model_id)
         num_layers = config_data.layer_count
         mem_size_bytes = await get_safetensors_size(model_id)
@@ -89,544 +120,13 @@ class ModelCard(CamelCaseModel):
             supports_tensor=config_data.supports_tensor,
             tasks=[ModelTask.TextGeneration],
         )
+        await mc.save_to_default_path()
         _card_cache[model_id] = mc
         return mc
 
 
-MODEL_CARDS: dict[str, ModelCard] = {
-    # deepseek v3
-    "deepseek-v3.1-4bit": ModelCard(
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
-        storage_size=Memory.from_gb(378),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "deepseek-v3.1-8bit": ModelCard(
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-8bit"),
-        storage_size=Memory.from_gb(713),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # kimi k2
-    "kimi-k2-instruct-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2-Instruct-4bit"),
-        storage_size=Memory.from_gb(578),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "kimi-k2-thinking": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2-Thinking"),
-        storage_size=Memory.from_gb(658),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "kimi-k2.5": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2.5"),
-        storage_size=Memory.from_gb(617),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.1
-    "llama-3.1-8b": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"),
-        storage_size=Memory.from_mb(4423),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-8b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"),
-        storage_size=Memory.from_mb(8540),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-8b-bf16": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"),
-        storage_size=Memory.from_mb(16100),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-70b": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"),
-        storage_size=Memory.from_mb(38769),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.2
-    "llama-3.2-1b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-1B-Instruct-4bit"),
-        storage_size=Memory.from_mb(696),
-        n_layers=16,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.2-3b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-4bit"),
-        storage_size=Memory.from_mb(1777),
-        n_layers=28,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.2-3b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-8bit"),
-        storage_size=Memory.from_mb(3339),
-        n_layers=28,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.3
-    "llama-3.3-70b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-4bit"),
-        storage_size=Memory.from_mb(38769),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.3-70b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-8bit"),
-        storage_size=Memory.from_mb(73242),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.3-70b-fp16": ModelCard(
-        model_id=ModelId("mlx-community/llama-3.3-70b-instruct-fp16"),
-        storage_size=Memory.from_mb(137695),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # qwen3
-    "qwen3-0.6b": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-0.6B-4bit"),
-        storage_size=Memory.from_mb(327),
-        n_layers=28,
-        hidden_size=1024,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-0.6b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-0.6B-8bit"),
-        storage_size=Memory.from_mb(666),
-        n_layers=28,
-        hidden_size=1024,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-30b": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-4bit"),
-        storage_size=Memory.from_mb(16797),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-30b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-8bit"),
-        storage_size=Memory.from_mb(31738),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"),
-        storage_size=Memory.from_mb(44800),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"),
-        storage_size=Memory.from_mb(84700),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-thinking-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"),
-        storage_size=Memory.from_mb(44900),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-thinking-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"),
-        storage_size=Memory.from_mb(84700),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-235b-a22b-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"),
-        storage_size=Memory.from_gb(132),
-        n_layers=94,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-235b-a22b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"),
-        storage_size=Memory.from_gb(250),
-        n_layers=94,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-coder-480b-a35b-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"),
-        storage_size=Memory.from_gb(270),
-        n_layers=62,
-        hidden_size=6144,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-coder-480b-a35b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"),
-        storage_size=Memory.from_gb(540),
-        n_layers=62,
-        hidden_size=6144,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # gpt-oss
-    "gpt-oss-120b-MXFP4-Q8": ModelCard(
-        model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-        storage_size=Memory.from_kb(68_996_301),
-        n_layers=36,
-        hidden_size=2880,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "gpt-oss-20b-MXFP4-Q8": ModelCard(
-        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-        storage_size=Memory.from_kb(11_744_051),
-        n_layers=24,
-        hidden_size=2880,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.5
-    "glm-4.5-air-8bit": ModelCard(
-        # Needs to be quantized g32 or g16 to work with tensor parallel
-        model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
-        storage_size=Memory.from_gb(114),
-        n_layers=46,
-        hidden_size=4096,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.5-air-bf16": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.5-Air-bf16"),
-        storage_size=Memory.from_gb(214),
-        n_layers=46,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.7
-    "glm-4.7-4bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-4bit"),
-        storage_size=Memory.from_bytes(198556925568),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-6bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-6bit"),
-        storage_size=Memory.from_bytes(286737579648),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-8bit-gs32": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
-        storage_size=Memory.from_bytes(396963397248),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.7 flash
-    "glm-4.7-flash-4bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-4bit"),
-        storage_size=Memory.from_gb(18),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-5bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-5bit"),
-        storage_size=Memory.from_gb(21),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-6bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-6bit"),
-        storage_size=Memory.from_gb(25),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-8bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-8bit"),
-        storage_size=Memory.from_gb(32),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # minimax-m2
-    "minimax-m2.1-8bit": ModelCard(
-        model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
-        storage_size=Memory.from_bytes(242986745856),
-        n_layers=61,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "minimax-m2.1-3bit": ModelCard(
-        model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
-        storage_size=Memory.from_bytes(100086644736),
-        n_layers=61,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-}
-
-_IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
-    "flux1-schnell": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-schnell"),
-        storage_size=Memory.from_bytes(23782357120 + 9524621312),
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23782357120),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "flux1-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-dev"),
-        storage_size=Memory.from_bytes(23782357120 + 9524621312),
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "flux1-krea-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-Krea-dev"),
-        storage_size=Memory.from_bytes(23802816640 + 9524621312),  # Same as dev
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "qwen-image": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image"),
-        storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(40860802176),
-                n_layers=60,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "qwen-image-edit-2509": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image-Edit-2509"),
-        storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.ImageToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(40860802176),
-                n_layers=60,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-}
-
-
-def _generate_image_model_quant_variants(
+# TODO: quantizing and dynamically creating model cards
+def _generate_image_model_quant_variants(  # pyright: ignore[reportUnusedFunction]
     base_name: str,
     base_card: ModelCard,
 ) -> dict[str, ModelCard]:
@@ -706,15 +206,6 @@ def _generate_image_model_quant_variants(
     return variants
 
 
-_image_model_cards: dict[str, ModelCard] = {}
-for _base_name, _base_card in _IMAGE_BASE_MODEL_CARDS.items():
-    _image_model_cards |= _generate_image_model_quant_variants(_base_name, _base_card)
-_IMAGE_MODEL_CARDS = _image_model_cards
-
-if EXO_ENABLE_IMAGE_MODELS:
-    MODEL_CARDS.update(_IMAGE_MODEL_CARDS)
-
-
 class ConfigData(BaseModel):
     model_config = {"extra": "ignore"}  # Allow unknown fields
 
@@ -742,6 +233,7 @@ class ConfigData(BaseModel):
             ["MiniMaxM2ForCausalLM"],
             ["LlamaForCausalLM"],
             ["GptOssForCausalLM"],
+            ["Step3p5ForCausalLM"],
         ]
 
     @model_validator(mode="before")

src/exo/utils/dashboard_path.py

@@ -1,31 +1,45 @@
-import os
 import sys
 from pathlib import Path
 from typing import cast
 
 
+def find_resources() -> Path:
+    resources = _find_resources_in_repo() or _find_resources_in_bundle()
+    if resources is None:
+        raise FileNotFoundError(
+            "Unable to locate resources. Did you clone the repo properly?"
+        )
+    return resources
+
+
+def _find_resources_in_repo() -> Path | None:
+    current_module = Path(__file__).resolve()
+    for parent in current_module.parents:
+        build = parent / "resources"
+        if build.is_dir():
+            return build
+    return None
+
+
+def _find_resources_in_bundle() -> Path | None:
+    frozen_root = cast(str | None, getattr(sys, "_MEIPASS", None))
+    if frozen_root is None:
+        return None
+    candidate = Path(frozen_root) / "resources"
+    if candidate.is_dir():
+        return candidate
+    return None
+
+
 def find_dashboard() -> Path:
-    dashboard = (
-        _find_dashboard_in_env()
-        or _find_dashboard_in_repo()
-        or _find_dashboard_in_bundle()
-    )
+    dashboard = _find_dashboard_in_repo() or _find_dashboard_in_bundle()
     if not dashboard:
         raise FileNotFoundError(
-            "Unable to locate dashboard assets - make sure the dashboard has been built, or export DASHBOARD_DIR if you've built the dashboard elsewhere."
+            "Unable to locate dashboard assets - you probably forgot to run `cd dashboard && npm install && npm run build && cd ..`"
         )
     return dashboard
 
 
-def _find_dashboard_in_env() -> Path | None:
-    env = os.environ.get("DASHBOARD_DIR")
-    if not env:
-        return None
-    resolved_env = Path(env).expanduser().resolve()
-
-    return resolved_env
-
-
 def _find_dashboard_in_repo() -> Path | None:
     current_module = Path(__file__).resolve()
     for parent in current_module.parents:

src/exo/worker/engines/mlx/auto_parallel.py

@@ -31,6 +31,8 @@ from mlx_lm.models.qwen3_moe import Model as Qwen3MoeModel
 from mlx_lm.models.qwen3_moe import Qwen3MoeSparseMoeBlock
 from mlx_lm.models.qwen3_next import Model as Qwen3NextModel
 from mlx_lm.models.qwen3_next import Qwen3NextSparseMoeBlock
+from mlx_lm.models.step3p5 import Model as Step3p5Model
+from mlx_lm.models.step3p5 import Step3p5MLP
 
 from exo.shared.logging import logger
 from exo.shared.types.worker.shards import PipelineShardMetadata
@@ -380,6 +382,14 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
+    elif isinstance(model, Step3p5Model):
+        tensor_parallel_sharding_strategy = Step3p5ShardingStrategy(
+            group,
+            all_to_sharded_linear,
+            sharded_to_all_linear,
+            all_to_sharded_linear_in_place,
+            sharded_to_all_linear_in_place,
+        )
     elif isinstance(model, GptOssModel):
         tensor_parallel_sharding_strategy = GptOssShardingStrategy(
             group,
@@ -774,3 +784,57 @@ class ShardedGptOssMoE(CustomMlxLayer):
         if self.sharding_group is not None:
             y = mx.distributed.all_sum(y, group=self.sharding_group)
         return y
+
+
+class Step3p5ShardingStrategy(TensorParallelShardingStrategy):
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
+        model = cast(Step3p5Model, model)
+        for layer in model.layers:
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
+            # Shard attention
+            layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
+            layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
+            layer.self_attn.v_proj = self.all_to_sharded_linear(layer.self_attn.v_proj)
+            layer.self_attn.o_proj = self.sharded_to_all_linear(layer.self_attn.o_proj)
+            layer.self_attn.num_heads //= self.N  # pyright: ignore[reportUnknownMemberType]
+            layer.self_attn.num_kv_heads //= self.N  # pyright: ignore[reportUnknownMemberType]
+
+            if isinstance(layer.mlp, Step3p5MLP):
+                # Dense MLP layer
+                layer.mlp.gate_proj = self.all_to_sharded_linear(layer.mlp.gate_proj)
+                layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
+                layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
+            else:
+                # MoE layer: shared expert + routed experts
+                self.all_to_sharded_linear_in_place(layer.mlp.share_expert.gate_proj)
+                self.sharded_to_all_linear_in_place(layer.mlp.share_expert.down_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.share_expert.up_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.gate_proj)
+                self.sharded_to_all_linear_in_place(layer.mlp.switch_mlp.down_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.up_proj)
+                layer.mlp = ShardedStep3p5MoE(layer.mlp)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
+                layer.mlp.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
+
+            mx.eval(layer)
+        return model
+
+
+class ShardedStep3p5MoE(CustomMlxLayer):
+    def __init__(self, layer: _LayerCallable):
+        super().__init__(layer)
+        self.sharding_group: mx.distributed.Group | None = None
+
+    def __call__(self, x: mx.array) -> mx.array:
+        if self.sharding_group is not None:
+            x = sum_gradients(self.sharding_group)(x)
+        y = self.original_layer.__call__(x)
+        if self.sharding_group is not None:
+            y = mx.distributed.all_sum(y, group=self.sharding_group)
+        return y

src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py

@@ -16,7 +16,7 @@ from exo.download.download_utils import (
     ensure_models_dir,
     fetch_file_list_with_cache,
 )
-from exo.shared.models.model_cards import MODEL_CARDS, ModelCard, ModelId
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.worker.engines.mlx.utils_mlx import (
     get_eos_token_ids_for_model,
     load_tokenizer_for_model_id,
@@ -76,7 +76,7 @@ def get_test_models() -> list[ModelCard]:
     """Get a representative sample of models to test."""
     # Pick one model from each family to test
     families: dict[str, ModelCard] = {}
-    for card in MODEL_CARDS.values():
+    for card in asyncio.run(get_model_cards()):
         # Extract family name (e.g., "llama-3.1" from "llama-3.1-8b")
         parts = card.model_id.short().split("-")
         family = "-".join(parts[:2]) if len(parts) >= 2 else parts[0]
@@ -296,7 +296,7 @@ async def test_tokenizer_special_tokens(model_card: ModelCard) -> None:
 async def test_kimi_tokenizer_specifically():
     """Test Kimi tokenizer with its specific patches and quirks."""
     kimi_models = [
-        card for card in MODEL_CARDS.values() if "kimi" in card.model_id.lower()
+        card for card in await get_model_cards() if "kimi" in card.model_id.lower()
     ]
 
     if not kimi_models:
@@ -343,7 +343,7 @@ async def test_kimi_tokenizer_specifically():
 async def test_glm_tokenizer_specifically():
     """Test GLM tokenizer with its specific EOS tokens."""
     glm_model_cards = [
-        card for card in MODEL_CARDS.values() if "glm" in card.model_id.lower()
+        card for card in await get_model_cards() if "glm" in card.model_id.lower()
     ]
 
     if not glm_model_cards:

tests/headless_runner.py

@@ -1,25 +1,20 @@
-import multiprocessing as mp
 import socket
-import time
-import typing
+from typing import Literal
 
 import anyio
 from fastapi import FastAPI
-from fastapi.responses import StreamingResponse
+from fastapi.responses import Response, StreamingResponse
 from hypercorn import Config
 from hypercorn.asyncio import serve  # pyright: ignore[reportUnknownVariableType]
 from loguru import logger
 from pydantic import BaseModel
 
-from exo.download.impl_shard_downloader import (
-    build_full_shard,
-    exo_shard_downloader,
-)
-from exo.shared.logging import InterceptLogger, logger_setup
-from exo.shared.models.model_cards import MODEL_CARDS, ModelId
+from exo.shared.constants import EXO_MODELS_DIR
+from exo.shared.models.model_cards import ModelCard, ModelId
+from exo.shared.types.chunks import TokenChunk
 from exo.shared.types.commands import CommandId
 from exo.shared.types.common import Host, NodeId
-from exo.shared.types.events import Event
+from exo.shared.types.events import ChunkGenerated, Event, RunnerStatusUpdated
 from exo.shared.types.tasks import (
     ConnectToGroup,
     LoadModel,
@@ -36,9 +31,14 @@ from exo.shared.types.worker.instances import (
     MlxJacclInstance,
     MlxRingInstance,
 )
-from exo.shared.types.worker.runners import RunnerId, ShardAssignments
+from exo.shared.types.worker.runners import (
+    RunnerFailed,
+    RunnerId,
+    RunnerShutdown,
+    ShardAssignments,
+)
 from exo.shared.types.worker.shards import PipelineShardMetadata, TensorShardMetadata
-from exo.utils.channels import MpReceiver, MpSender, channel, mp_channel
+from exo.utils.channels import channel, mp_channel
 from exo.utils.info_gatherer.info_gatherer import GatheredInfo, InfoGatherer
 from exo.worker.runner.bootstrap import entrypoint
 
@@ -46,36 +46,37 @@ from exo.worker.runner.bootstrap import entrypoint
 class Tests(BaseModel):
     # list[hostname, ip addr]
     devs: list[list[str]]
-    model_id: str
-    kind: typing.Literal["init", "warmup", "inference"]
+    ibv_devs: list[list[str | None]] | None
+    model_id: ModelId
+    kind: Literal["ring", "jaccl", "both"]
 
 
-mp.set_start_method("spawn", force=True)
-logger_setup(None)
+iid = InstanceId("im testing here")
 
 
 async def main():
     logger.info("starting cool server majig")
-    await assert_downloads()
     cfg = Config()
-    cfg.bind = "0.0.0.0:52415"
+    cfg.bind = "0.0.0.0:52414"
     # nb: shared.logging needs updating if any of this changes
     cfg.accesslog = "-"
     cfg.errorlog = "-"
-    cfg.logger_class = InterceptLogger
+    ev = anyio.Event()
     app = FastAPI()
-    app.post("/ring")(ring_backend)
-    app.post("/jaccl")(jaccl_backend)
-    app.post("/tb_detection")(tb_detection)
-    shutdown = anyio.Event()
+    app.post("/run_test")(run_test)
+    app.post("/kill")(lambda: kill(ev))
+    app.get("/tb_detection")(tb_detection)
+    app.get("/models")(list_models)
     await serve(
         app,  # type: ignore
         cfg,
-        shutdown_trigger=lambda: shutdown.wait(),
+        shutdown_trigger=lambda: ev.wait(),
     )
-    await anyio.sleep_forever()
-    # gracefully shutdown the api
-    shutdown.set()
+
+
+def kill(ev: anyio.Event):
+    ev.set()
+    return Response(status_code=204)
 
 
 async def tb_detection():
@@ -87,29 +88,19 @@ async def tb_detection():
         return recv.collect()
 
 
-async def assert_downloads():
-    sd = exo_shard_downloader()
-    # await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-0.6b"].model_id))
-    await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["llama-3.1-8b-bf16"].model_id)
-    )
-    await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-30b"].model_id))
-    await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-120b-MXFP4-Q8"].model_id)
-    )
-    await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-20b-4bit"].model_id)
-    )
-    await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["glm-4.7-8bit-gs32"].model_id)
-    )
-    await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["minimax-m2.1-8bit"].model_id)
-    )
+def list_models():
+    sent = set[str]()
+    for path in EXO_MODELS_DIR.rglob("model-*.safetensors"):
+        if "--" not in path.parent.name:
+            continue
+        name = path.parent.name.replace("--", "/")
+        if name in sent:
+            continue
+        sent.add(name)
+        yield ModelId(path.parent.name.replace("--", "/"))
 
 
-async def ring_backend(test: Tests):
-    iid = InstanceId(str(hash(str(test.devs))))
+async def run_test(test: Tests):
     weird_hn = socket.gethostname()
     for dev in test.devs:
         if weird_hn.startswith(dev[0]) or dev[0].startswith(weird_hn):
@@ -117,31 +108,63 @@ async def ring_backend(test: Tests):
             break
     else:
         raise ValueError(f"{weird_hn} not in {test.devs}")
-    return await execute_test(test, ring_instance(test, iid, hn), hn)
+
+    async def run():
+        logger.info(f"testing {test.model_id}")
+
+        instances: list[Instance] = []
+        if test.kind in ["ring", "both"]:
+            i = await ring_instance(test, hn)
+            if i is None:
+                yield "no model found"
+                return
+            instances.append(i)
+        if test.kind in ["jaccl", "both"]:
+            i = await jaccl_instance(test)
+            if i is None:
+                yield "no model found"
+                return
+            instances.append(i)
+
+        for instance in instances:
+            recv = await execute_test(test, instance, hn)
+
+            str_out = ""
+
+            for item in recv:
+                if isinstance(item, ChunkGenerated):
+                    assert isinstance(item.chunk, TokenChunk)
+                    str_out += item.chunk.text
+
+                if isinstance(item, RunnerStatusUpdated) and isinstance(
+                    item.runner_status, (RunnerFailed, RunnerShutdown)
+                ):
+                    yield str_out + "\n"
+                    yield item.model_dump_json() + "\n"
+
+    return StreamingResponse(run())
 
 
-def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
-    hbn = [Host(ip="i dont care", port=52416) for _ in test.devs]
+async def ring_instance(test: Tests, hn: str) -> Instance | None:
+    hbn = [Host(ip="198.51.100.0", port=52417) for _ in test.devs]
     world_size = len(test.devs)
     for i in range(world_size):
         if test.devs[i][0] == hn:
             hn = test.devs[i][0]
-            if i - 1 >= 0:
-                hbn[i - 1] = Host(ip=test.devs[i - 1][1], port=52416)
-            if i + 1 < len(test.devs):
-                hbn[i + 1] = Host(ip=test.devs[i + 1][1], port=52416)
-            hbn[i] = Host(ip="0.0.0.0", port=52416)
-            break
+        hbn[(i - 1) % world_size] = Host(ip=test.devs[i - 1][1], port=52417)
+        hbn[(i + 1) % world_size] = Host(ip=test.devs[i + 1][1], port=52417)
+        hbn[i] = Host(ip="0.0.0.0", port=52417)
+        break
     else:
         raise ValueError(f"{hn} not in {test.devs}")
 
-    card = MODEL_CARDS[test.model_id]
+    card = await ModelCard.load(test.model_id)
     instance = MlxRingInstance(
         instance_id=iid,
-        ephemeral_port=52416,
+        ephemeral_port=52417,
         hosts_by_node={NodeId(hn): hbn},
         shard_assignments=ShardAssignments(
-            model_id=ModelId(test.model_id),
+            model_id=test.model_id,
             node_to_runner={NodeId(host[0]): RunnerId(host[0]) for host in test.devs},
             runner_to_shard={
                 RunnerId(test.devs[i][0]): PipelineShardMetadata(
@@ -163,113 +186,75 @@ def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
     return instance
 
 
-async def execute_test(test: Tests, instance: Instance, hn: str):
+async def execute_test(test: Tests, instance: Instance, hn: str) -> list[Event]:
     world_size = len(test.devs)
-    iid = InstanceId(str(hash(str(test.devs))))
-    _handle, recv, send = new_runner(instance, hn)
-    if world_size > 1:
-        send.send(ConnectToGroup(instance_id=iid))
-    send.send(LoadModel(instance_id=iid))
-
-    match test.kind:
-        case "init":
-            pass
-        case "warmup":
-            send.send(StartWarmup(instance_id=iid))
-        case "inference":
-            send.send(StartWarmup(instance_id=iid))
-            send.send(
-                TextGeneration(
-                    task_params=TextGenerationTaskParams(
-                        model=test.model_id,
-                        instructions="You are a helpful assistant",
-                        input="What is the capital of France?",
-                    ),
-                    command_id=CommandId("yo"),
-                    instance_id=iid,
-                )
+    commands: list[Task] = [
+        (LoadModel(instance_id=iid)),
+        (StartWarmup(instance_id=iid)),
+        (
+            TextGeneration(
+                task_params=TextGenerationTaskParams(
+                    model=test.model_id,
+                    instructions="You are a helpful assistant",
+                    input="What is the capital of France?",
+                ),
+                command_id=CommandId("yo"),
+                instance_id=iid,
             )
+        ),
+        (Shutdown(runner_id=RunnerId(hn), instance_id=iid)),
+    ]
+    if world_size > 1:
+        commands.insert(0, ConnectToGroup(instance_id=iid))
+    bound_instance = BoundInstance(
+        instance=instance, bound_runner_id=RunnerId(hn), bound_node_id=NodeId(hn)
+    )
+    ev_send, _ev_recv = mp_channel[Event]()
+    task_send, task_recv = mp_channel[Task]()
 
-    send.send(Shutdown(runner_id=RunnerId(hn), instance_id=iid))
+    for command in commands:
+        task_send.send(command)
 
-    async def map_recv():
-        with recv:
-            try:
-                async for item in recv:
-                    yield item.model_dump_json() + "\n"
-            except anyio.ClosedResourceError:
-                pass
+    entrypoint(
+        bound_instance,
+        ev_send,
+        task_recv,
+        logger,
+    )
 
-    ret = StreamingResponse(map_recv())
-    ret._pls_dont_gc = _handle  # type: ignore
-    return ret
+    # TODO(evan): return ev_recv.collect()
+    return []
 
 
-async def jaccl_backend(test: Tests):
-    iid = InstanceId(str(hash(str(test.devs))))
-    weird_hn = socket.gethostname()
-    for dev in test.devs:
-        if weird_hn.startswith(dev[0]) or dev[0].startswith(weird_hn):
-            hn = dev[0]
-            break
-    else:
-        raise ValueError(f"{weird_hn} not in {test.devs}")
-    return await execute_test(test, jaccl_instance(test, iid), hn)
-
-
-def jaccl_instance(test: Tests, iid: InstanceId):
-    card = MODEL_CARDS[test.model_id]
+async def jaccl_instance(test: Tests) -> MlxJacclInstance | None:
+    card = await ModelCard.load(test.model_id)
     world_size = len(test.devs)
+    assert test.ibv_devs
 
     return MlxJacclInstance(
         instance_id=iid,
-        jaccl_devices=[[None, "rdma_en3"], ["rdma_en3", None]],
+        jaccl_devices=test.ibv_devs,
         # rank 0 is always coordinator
         jaccl_coordinators={
-            NodeId(host[0]): test.devs[0][1] + ":52416" for host in test.devs
+            NodeId(host[0]): test.devs[0][1] + ":52417" for host in test.devs
         },
         shard_assignments=ShardAssignments(
-            model_id=ModelId(test.model_id),
+            model_id=test.model_id,
             node_to_runner={NodeId(host[0]): RunnerId(host[0]) for host in test.devs},
             runner_to_shard={
-                RunnerId(test.devs[i][0]): TensorShardMetadata(
+                RunnerId(host[0]): TensorShardMetadata(
                     model_card=card,
                     device_rank=i,
                     world_size=world_size,
-                    start_layer=card.n_layers,
+                    start_layer=0,
                     end_layer=card.n_layers,
                     n_layers=card.n_layers,
                 )
-                for i in range(world_size)
+                for i, host in enumerate(test.devs)
             },
         ),
     )
 
 
-def new_runner(
-    instance: Instance,
-    hn: str,
-) -> tuple[mp.Process, MpReceiver[Event], MpSender[Task]]:
-    bound_instance = BoundInstance(
-        instance=instance, bound_runner_id=RunnerId(hn), bound_node_id=NodeId(hn)
-    )
-    ev_send, ev_recv = mp_channel[Event]()
-    task_send, task_recv = mp_channel[Task]()
-
-    runner_process = mp.Process(
-        target=entrypoint,
-        args=(
-            bound_instance,
-            ev_send,
-            task_recv,
-            logger,
-        ),
-    )
-    runner_process._pls_dont_gc = (ev_send, task_recv)  # type: ignore
-    runner_process.start()
-    time.sleep(0.1)
-    return (runner_process, ev_recv, task_send)
-
-
 if __name__ == "__main__":
     anyio.run(main)

tests/run_exo_on.sh

@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+[ $# -lt 1 ] && {
+  echo "Usage: $0 host1 [host2 ...]"
+  exit 1
+}
+
+[ -z "$(git status --porcelain)" ] || {
+  echo "Uncommitted changes"
+  exit 1
+}
+
+commit=$(git rev-parse HEAD)
+git fetch -q origin
+git branch -r --contains "$commit" | grep -qE '^\s*origin/' || {
+  echo "Not pushed to origin"
+  exit 1
+}
+
+echo "Deploying $commit to $# hosts..."
+hosts=("$@")
+cleanup() {
+  for host in "${hosts[@]}"; do
+    ssh -T -o BatchMode=yes "$host@$host" "pkill -SIGINT -of exo-env" &
+  done
+  wait
+  jobs -pr | xargs -r kill 2>/dev/null || true
+}
+trap 'cleanup' EXIT INT TERM
+
+colours=($'\e[31m' $'\e[32m' $'\e[33m' $'\e[34m')
+reset=$'\e[0m'
+i=0
+for host; do
+  colour=${colours[i++ % 4]}
+  {
+    ssh -T -o BatchMode=yes -o ServerAliveInterval=30 "$host@$host" \
+      "/nix/var/nix/profiles/default/bin/nix shell nixpkgs#git -c bash -s -- '$commit'" \
+      2>&1 | awk -v p="${colour}[${host}]${reset}" '{ print p $0; fflush() }' &
+  } <<'EOF'
+        set -euo pipefail
+        cd exo
+        git fetch -q origin
+        git checkout -q "$1"
+        EXO_LIBP2P_NAMESPACE="$1" /nix/var/nix/profiles/default/bin/nix run .#exo
+EOF
+done
+
+for host; do
+  echo "Waiting for $host..."
+  until curl -sf "http://$host:52415/models"; do sleep 1; done
+done
+wait

tests/start_distributed_test.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+import itertools
+import json
+import subprocess
+import sys
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any, cast
+from urllib.request import Request, urlopen
+
+if not (args := sys.argv[1:]):
+    sys.exit(
+        f"USAGE: {sys.argv[0]} <kind> [host1] [host2] ...\nkind is optional, and should be jaccl or ring"
+    )
+
+kind = args[0] if args[0] in ("jaccl", "ring") else "both"
+hosts = args[1:] if kind != "both" else args
+ts = subprocess.run(
+    ["tailscale", "status"], check=True, text=True, capture_output=True
+).stdout.splitlines()
+ip = {sl[1]: sl[0] for line in ts if len(sl := line.split()) >= 2}
+ips = [ip[h] for h in hosts]
+devs = [[h, ip[h]] for h in hosts]
+n = len(hosts)
+
+
+def get_tb(a: str) -> list[dict[str, Any]]:
+    with urlopen(f"http://{a}:52414/tb_detection", timeout=5) as r:  # pyright: ignore[reportAny]
+        return json.loads(r.read())  # pyright: ignore[reportAny]
+
+
+def get_models(a: str) -> set[str]:
+    with urlopen(f"http://{a}:52414/models", timeout=5) as r:  # pyright: ignore[reportAny]
+        return set(json.loads(r.read()))  # pyright: ignore[reportAny]
+
+
+def run(h: str, a: str, body: bytes) -> None:
+    with urlopen(
+        Request(
+            f"http://{a}:52414/run_test",
+            data=body,
+            method="POST",
+            headers={"Content-Type": "application/json"},
+        ),
+        timeout=300,
+    ) as r:  # pyright: ignore[reportAny]
+        for line in r.read().decode(errors="replace").splitlines():  # pyright: ignore[reportAny]
+            print(f"\n{h}@{a}: {line}", flush=True)
+
+
+with ThreadPoolExecutor(n) as exctr:
+    if kind in ("jaccl", "both"):
+        payloads = list(exctr.map(get_tb, ips))
+
+        u2e = {
+            ident["domainUuid"]: (i, ident["rdmaInterface"])
+            for i, p in enumerate(payloads)
+            for d in p
+            for ident in cast(
+                list[dict[str, str]],
+                d.get("MacThunderboltIdentifiers", {}).get("idents", []),  # pyright: ignore[reportAny]
+            )
+        }
+        edges = {
+            (u2e[s][0], u2e[t][0]): u2e[t][1]
+            for p in payloads
+            for d in p
+            for c in d.get("MacThunderboltConnections", {}).get("conns", [])  # pyright: ignore[reportAny]
+            if (s := c["sourceUuid"]) in u2e and (t := c["sinkUuid"]) in u2e  # pyright: ignore[reportAny]
+        }
+        ibv_devs = [[edges.get((i, j)) for j in range(n)] for i in range(n)]
+    else:
+        ibv_devs = None
+
+    models = set[str].intersection(*exctr.map(get_models, ips))
+
+    print("\n")
+    print("=" * 70)
+    print(f"Starting test with {models}")
+    print("=" * 70)
+    print("\n")
+    for model in models:
+        body = json.dumps(
+            {"devs": devs, "model_id": model, "ibv_devs": ibv_devs, "kind": kind}
+        ).encode()
+        list(exctr.map(run, hosts, ips, itertools.repeat(body)))

tests/start_distributed_test.sh

@@ -1,54 +0,0 @@
-#!/usr/bin/env bash
-
-set -euo pipefail
-
-query() {
-  tailscale status | awk -v find="$1" '$2 == find { print $1 }'
-}
-
-if [[ $# -lt 2 ]]; then
-  echo "USAGE: $0 <test kind> [host1] [host2] ..."
-  exit 1
-fi
-
-kind=$1
-shift
-
-test_kinds="ring jaccl"
-
-if ! echo "$test_kinds" | grep -q "$kind"; then
-  printf "%s is not a known test kind.\nCurrent test kinds are %s" "$kind" "$test_kinds"
-  exit 1
-fi
-
-hostnames=("$@")
-weaved=()
-ips=()
-for name in "${hostnames[@]}"; do
-  ip=$(query "$name")
-  ips+=("$ip")
-  weaved+=("$name" "$ip")
-done
-
-devs_raw=$(printf '["%s", "%s"], ' "${weaved[@]}")
-devs="[${devs_raw%, }]"
-
-model_ids=("qwen3-30b" "gpt-oss-120b-MXFP4-Q8" "kimi-k2-thinking")
-
-for model_id in "${model_ids[@]}"; do
-  for i in "${!ips[@]}"; do
-    {
-      req="{
-        \"model_id\": \"${model_id}\",
-        \"devs\": ${devs},
-        \"kind\": \"inference\"
-       }"
-      echo "req $req"
-      curl -sN \
-        -X POST "http://${ips[$i]}:52415/${kind}" \
-        -H "Content-Type: application/json" -d "$req" \
-        2>&1 | sed "s/^/\n${hostnames[$i]}@${ips[$i]}: /" || echo "curl to ${hostnames[$i]} failed" && exit 1
-    } &
-  done
-  wait
-done

uv.lock

@@ -1072,8 +1072,8 @@ wheels = [
 
 [[package]]
 name = "mlx-lm"
-version = "0.30.5"
-source = { git = "https://github.com/ml-explore/mlx-lm?branch=main#96699e6dadb13b82b28285bb131a0741997d19ae" }
+version = "0.30.6"
+source = { git = "https://github.com/ml-explore/mlx-lm?branch=main#ab050d1fac2ef1d7bea6b8d870f1e5717d7f59f5" }
 dependencies = [
     { name = "jinja2", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mlx", marker = "sys_platform == 'darwin'" },