migrate model cards to .toml files

this happens every time i run npm install - lets upstream it

## testing
dashboard builds and renders

Cargo.lock

@@ -4340,25 +4340,6 @@ dependencies = [
  "libc",
 ]
 
-[[package]]
-name = "system_custodian"
-version = "0.0.1"
-dependencies = [
- "delegate",
- "derive_more",
- "either",
- "extend",
- "futures",
- "futures-timer",
- "impl-trait-for-tuples",
- "keccak-const",
- "log",
- "thiserror 2.0.17",
- "tokio",
- "tracing-subscriber",
- "util",
-]
-
 [[package]]
 name = "tagptr"
 version = "0.2.0"

Cargo.toml

@@ -3,7 +3,6 @@ resolver = "3"
 members = [
     "rust/networking",
     "rust/exo_pyo3_bindings",
-    "rust/system_custodian",
     "rust/util",
 ]
 
@@ -25,7 +24,6 @@ opt-level = 3
 [workspace.dependencies]
 ## Crate members as common dependencies
 networking = { path = "rust/networking" }
-system_custodian = { path = "rust/system_custodian" }
 util = { path = "rust/util" }
 
 # Proc-macro authoring tools

dashboard/package-lock.json

@@ -863,6 +863,7 @@
 			"integrity": "sha512-oH8tXw7EZnie8FdOWYrF7Yn4IKrqTFHhXvl8YxXxbKwTMcD/5NNCryUSEXRk2ZR4ojnub0P8rNrsVGHXWqIDtA==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@standard-schema/spec": "^1.0.0",
 				"@sveltejs/acorn-typescript": "^1.0.5",
@@ -902,6 +903,7 @@
 			"integrity": "sha512-Y1Cs7hhTc+a5E9Va/xwKlAJoariQyHY+5zBgCZg4PFWNYQ1nMN9sjK1zhw1gK69DuqVP++sht/1GZg1aRwmAXQ==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@sveltejs/vite-plugin-svelte-inspector": "^4.0.1",
 				"debug": "^4.4.1",
@@ -1518,6 +1520,7 @@
 			"integrity": "sha512-LCCV0HdSZZZb34qifBsyWlUmok6W7ouER+oQIGBScS8EsZsQbrtFTUrDX4hOl+CS6p7cnNC4td+qrSVGSCTUfQ==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"undici-types": "~6.21.0"
 			}
@@ -1527,6 +1530,7 @@
 			"resolved": "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz",
 			"integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
 			"license": "MIT",
+			"peer": true,
 			"bin": {
 				"acorn": "bin/acorn"
 			},
@@ -1939,6 +1943,7 @@
 			"integrity": "sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==",
 			"dev": true,
 			"license": "ISC",
+			"peer": true,
 			"engines": {
 				"node": ">=12"
 			}
@@ -2646,6 +2651,7 @@
 			"integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"engines": {
 				"node": ">=12"
 			},
@@ -2833,6 +2839,7 @@
 			"resolved": "https://registry.npmjs.org/svelte/-/svelte-5.45.3.tgz",
 			"integrity": "sha512-ngKXNhNvwPzF43QqEhDOue7TQTrG09em1sd4HBxVF0Wr2gopAmdEWan+rgbdgK4fhBtSOTJO8bYU4chUG7VXZQ==",
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@jridgewell/remapping": "^2.3.4",
 				"@jridgewell/sourcemap-codec": "^1.5.0",
@@ -2977,6 +2984,7 @@
 			"integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
 			"dev": true,
 			"license": "Apache-2.0",
+			"peer": true,
 			"bin": {
 				"tsc": "bin/tsc",
 				"tsserver": "bin/tsserver"
@@ -2998,6 +3006,7 @@
 			"integrity": "sha512-+Oxm7q9hDoLMyJOYfUYBuHQo+dkAloi33apOPP56pzj+vsdJDzr+j1NISE5pyaAuKL4A3UD34qd0lx5+kfKp2g==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"esbuild": "^0.25.0",
 				"fdir": "^6.4.4",

pyproject.toml

@@ -23,13 +23,13 @@ dependencies = [
     "tiktoken>=0.12.0", # required for kimi k2 tokenizer
     "hypercorn>=0.18.0",
     "openai-harmony>=0.0.8",
+    "tomlkit>=0.14.0",
 ]
 
 [project.scripts]
 exo-master = "exo.master.main:main"
 exo-worker = "exo.worker.main:main"
 exo = "exo.main:main"
-exo-rsh = "exo.rsh.client:main"
 
 # dependencies only required for development
 [dependency-groups]

resources/model_cards/deepseek-v3.1-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "deepseek-v3.1-4bit"
+model_id = "mlx-community/DeepSeek-V3.1-4bit"
+name = "DeepSeek V3.1 (4-bit)"
+description = "DeepSeek V3.1 is a large language model trained on the DeepSeek V3.1 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/DeepSeek-V3.1-4bit"
+pretty_name = "DeepSeek V3.1 (4-bit)"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 405874409472

resources/model_cards/deepseek-v3.1-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "deepseek-v3.1-8bit"
+model_id = "mlx-community/DeepSeek-V3.1-8bit"
+name = "DeepSeek V3.1 (8-bit)"
+description = "DeepSeek V3.1 is a large language model trained on the DeepSeek V3.1 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/DeepSeek-V3.1-8bit"
+pretty_name = "DeepSeek V3.1 (8-bit)"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 765577920512

resources/model_cards/glm-4.5-air-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "glm-4.5-air-8bit"
+model_id = "mlx-community/GLM-4.5-Air-8bit"
+name = "GLM 4.5 Air 8bit"
+description = "GLM 4.5 Air 8bit"
+tags = []
+
+[metadata]
+model_id = "mlx-community/GLM-4.5-Air-8bit"
+pretty_name = "GLM 4.5 Air 8bit"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = false
+
+[metadata.storage_size]
+in_bytes = 122406567936

resources/model_cards/glm-4.5-air-bf16.toml

@@ -0,0 +1,15 @@
+short_id = "glm-4.5-air-bf16"
+model_id = "mlx-community/GLM-4.5-Air-bf16"
+name = "GLM 4.5 Air bf16"
+description = "GLM 4.5 Air bf16"
+tags = []
+
+[metadata]
+model_id = "mlx-community/GLM-4.5-Air-bf16"
+pretty_name = "GLM 4.5 Air bf16"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 229780750336

resources/model_cards/glm-4.7-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "glm-4.7-4bit"
+model_id = "mlx-community/GLM-4.7-4bit"
+name = "GLM 4.7 4bit"
+description = "GLM 4.7 4bit"
+tags = []
+
+[metadata]
+model_id = "mlx-community/GLM-4.7-4bit"
+pretty_name = "GLM 4.7 4bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 198556925568

resources/model_cards/glm-4.7-6bit.toml

@@ -0,0 +1,15 @@
+short_id = "glm-4.7-6bit"
+model_id = "mlx-community/GLM-4.7-6bit"
+name = "GLM 4.7 6bit"
+description = "GLM 4.7 6bit"
+tags = []
+
+[metadata]
+model_id = "mlx-community/GLM-4.7-6bit"
+pretty_name = "GLM 4.7 6bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 286737579648

resources/model_cards/glm-4.7-8bit-gs32.toml

@@ -0,0 +1,15 @@
+short_id = "glm-4.7-8bit-gs32"
+model_id = "mlx-community/GLM-4.7-8bit-gs32"
+name = "GLM 4.7 8bit (gs32)"
+description = "GLM 4.7 8bit (gs32)"
+tags = []
+
+[metadata]
+model_id = "mlx-community/GLM-4.7-8bit-gs32"
+pretty_name = "GLM 4.7 8bit (gs32)"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 396963397248

resources/model_cards/gpt-oss-120b-MXFP4-Q8.toml

@@ -0,0 +1,15 @@
+short_id = "gpt-oss-120b-MXFP4-Q8"
+model_id = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+name = "GPT-OSS 120B (MXFP4-Q8, MLX)"
+description = "OpenAI's GPT-OSS 120B is a 117B-parameter Mixture-of-Experts model designed for high-reasoning and general-purpose use; this variant is a 4-bit MLX conversion for Apple Silicon."
+tags = []
+
+[metadata]
+model_id = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+pretty_name = "GPT-OSS 120B (MXFP4-Q8, MLX)"
+n_layers = 36
+hidden_size = 2880
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 70652212224

resources/model_cards/gpt-oss-20b-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "gpt-oss-20b-4bit"
+model_id = "mlx-community/gpt-oss-20b-MXFP4-Q4"
+name = "GPT-OSS 20B (MXFP4-Q4, MLX)"
+description = "OpenAI's GPT-OSS 20B is a medium-sized MoE model for lower-latency and local or specialized use cases; this MLX variant uses MXFP4 4-bit quantization."
+tags = []
+
+[metadata]
+model_id = "mlx-community/gpt-oss-20b-MXFP4-Q4"
+pretty_name = "GPT-OSS 20B (MXFP4-Q4, MLX)"
+n_layers = 24
+hidden_size = 2880
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 12025908224

resources/model_cards/kimi-k2-instruct-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "kimi-k2-instruct-4bit"
+model_id = "mlx-community/Kimi-K2-Instruct-4bit"
+name = "Kimi K2 Instruct (4-bit)"
+description = "Kimi K2 is a large language model trained on the Kimi K2 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Kimi-K2-Instruct-4bit"
+pretty_name = "Kimi K2 Instruct (4-bit)"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 620622774272

resources/model_cards/kimi-k2-thinking.toml

@@ -0,0 +1,15 @@
+short_id = "kimi-k2-thinking"
+model_id = "mlx-community/Kimi-K2-Thinking"
+name = "Kimi K2 Thinking (4-bit)"
+description = "Kimi K2 Thinking is the latest, most capable version of open-source thinking model."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Kimi-K2-Thinking"
+pretty_name = "Kimi K2 Thinking (4-bit)"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 706522120192

resources/model_cards/llama-3.1-70b.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.1-70b"
+model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+name = "Llama 3.1 70B (4-bit)"
+description = "Llama 3.1 is a large language model trained on the Llama 3.1 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+pretty_name = "Llama 3.1 70B (4-bit)"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 40652242944

resources/model_cards/llama-3.1-8b-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.1-8b-8bit"
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
+name = "Llama 3.1 8B (8-bit)"
+description = "Llama 3.1 is a large language model trained on the Llama 3.1 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
+pretty_name = "Llama 3.1 8B (8-bit)"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 8954839040

resources/model_cards/llama-3.1-8b-bf16.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.1-8b-bf16"
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
+name = "Llama 3.1 8B (BF16)"
+description = "Llama 3.1 is a large language model trained on the Llama 3.1 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
+pretty_name = "Llama 3.1 8B (BF16)"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 16882073600

resources/model_cards/llama-3.1-8b.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.1-8b"
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+name = "Llama 3.1 8B (4-bit)"
+description = "Llama 3.1 is a large language model trained on the Llama 3.1 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+pretty_name = "Llama 3.1 8B (4-bit)"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 4637851648

resources/model_cards/llama-3.2-1b.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.2-1b"
+model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+name = "Llama 3.2 1B (4-bit)"
+description = "Llama 3.2 is a large language model trained on the Llama 3.2 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+pretty_name = "Llama 3.2 1B (4-bit)"
+n_layers = 16
+hidden_size = 2048
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 729808896

resources/model_cards/llama-3.2-3b-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.2-3b-8bit"
+model_id = "mlx-community/Llama-3.2-3B-Instruct-8bit"
+name = "Llama 3.2 3B (8-bit)"
+description = "Llama 3.2 is a large language model trained on the Llama 3.2 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Llama-3.2-3B-Instruct-8bit"
+pretty_name = "Llama 3.2 3B (8-bit)"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 3501195264

resources/model_cards/llama-3.2-3b.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.2-3b"
+model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+name = "Llama 3.2 3B (4-bit)"
+description = "Llama 3.2 is a large language model trained on the Llama 3.2 dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+pretty_name = "Llama 3.2 3B (4-bit)"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 1863319552

resources/model_cards/llama-3.3-70b-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.3-70b-8bit"
+model_id = "mlx-community/Llama-3.3-70B-Instruct-8bit"
+name = "Llama 3.3 70B (8-bit)"
+description = "The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)"
+tags = []
+
+[metadata]
+model_id = "mlx-community/Llama-3.3-70B-Instruct-8bit"
+pretty_name = "Llama 3.3 70B (8-bit)"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 76799803392

resources/model_cards/llama-3.3-70b-fp16.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.3-70b-fp16"
+model_id = "mlx-community/llama-3.3-70b-instruct-fp16"
+name = "Llama 3.3 70B (FP16)"
+description = "The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)"
+tags = []
+
+[metadata]
+model_id = "mlx-community/llama-3.3-70b-instruct-fp16"
+pretty_name = "Llama 3.3 70B (FP16)"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 144383672320

resources/model_cards/llama-3.3-70b.toml

@@ -0,0 +1,15 @@
+short_id = "llama-3.3-70b"
+model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+name = "Llama 3.3 70B (4-bit)"
+description = "The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)"
+tags = []
+
+[metadata]
+model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+pretty_name = "Llama 3.3 70B"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 40652242944

resources/model_cards/minimax-m2.1-3bit.toml

@@ -0,0 +1,15 @@
+short_id = "minimax-m2.1-3bit"
+model_id = "mlx-community/MiniMax-M2.1-3bit"
+name = "MiniMax M2.1 3bit"
+description = "MiniMax M2.1 3bit"
+tags = []
+
+[metadata]
+model_id = "mlx-community/MiniMax-M2.1-3bit"
+pretty_name = "MiniMax M2.1 3bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 100086644736

resources/model_cards/minimax-m2.1-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "minimax-m2.1-8bit"
+model_id = "mlx-community/MiniMax-M2.1-8bit"
+name = "MiniMax M2.1 8bit"
+description = "MiniMax M2.1 8bit"
+tags = []
+
+[metadata]
+model_id = "mlx-community/MiniMax-M2.1-8bit"
+pretty_name = "MiniMax M2.1 8bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 242986745856

resources/model_cards/qwen3-0.6b-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-0.6b-8bit"
+model_id = "mlx-community/Qwen3-0.6B-8bit"
+name = "Qwen3 0.6B (8-bit)"
+description = "Qwen3 0.6B is a large language model trained on the Qwen3 0.6B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-0.6B-8bit"
+pretty_name = "Qwen3 0.6B (8-bit)"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+
+[metadata.storage_size]
+in_bytes = 698351616

resources/model_cards/qwen3-0.6b.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-0.6b"
+model_id = "mlx-community/Qwen3-0.6B-4bit"
+name = "Qwen3 0.6B (4-bit)"
+description = "Qwen3 0.6B is a large language model trained on the Qwen3 0.6B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-0.6B-4bit"
+pretty_name = "Qwen3 0.6B (4-bit)"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+
+[metadata.storage_size]
+in_bytes = 342884352

resources/model_cards/qwen3-235b-a22b-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-235b-a22b-4bit"
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+name = "Qwen3 235B A22B (4-bit)"
+description = "Qwen3 235B (Active 22B) is a large language model trained on the Qwen3 235B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+pretty_name = "Qwen3 235B A22B (4-bit)"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 141733920768

resources/model_cards/qwen3-235b-a22b-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-235b-a22b-8bit"
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
+name = "Qwen3 235B A22B (8-bit)"
+description = "Qwen3 235B (Active 22B) is a large language model trained on the Qwen3 235B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
+pretty_name = "Qwen3 235B A22B (8-bit)"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 268435456000

resources/model_cards/qwen3-30b-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-30b-8bit"
+model_id = "mlx-community/Qwen3-30B-A3B-8bit"
+name = "Qwen3 30B A3B (8-bit)"
+description = "Qwen3 30B is a large language model trained on the Qwen3 30B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-30B-A3B-8bit"
+pretty_name = "Qwen3 30B A3B (8-bit)"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 33279705088

resources/model_cards/qwen3-30b.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-30b"
+model_id = "mlx-community/Qwen3-30B-A3B-4bit"
+name = "Qwen3 30B A3B (4-bit)"
+description = "Qwen3 30B is a large language model trained on the Qwen3 30B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-30B-A3B-4bit"
+pretty_name = "Qwen3 30B A3B (4-bit)"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 17612931072

resources/model_cards/qwen3-80b-a3B-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-80b-a3B-4bit"
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+name = "Qwen3 80B A3B (4-bit)"
+description = "Qwen3 80B"
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+pretty_name = "Qwen3 80B A3B (4-bit)"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 46976204800

resources/model_cards/qwen3-80b-a3B-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-80b-a3B-8bit"
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
+name = "Qwen3 80B A3B (8-bit)"
+description = "Qwen3 80B"
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
+pretty_name = "Qwen3 80B A3B (8-bit)"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 88814387200

resources/model_cards/qwen3-80b-a3B-thinking-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-80b-a3B-thinking-4bit"
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+name = "Qwen3 80B A3B Thinking (4-bit)"
+description = "Qwen3 80B Reasoning model"
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+pretty_name = "Qwen3 80B A3B (4-bit)"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 88814387200

resources/model_cards/qwen3-80b-a3B-thinking-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-80b-a3B-thinking-8bit"
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
+name = "Qwen3 80B A3B Thinking (8-bit)"
+description = "Qwen3 80B Reasoning model"
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
+pretty_name = "Qwen3 80B A3B (8-bit)"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 88814387200

resources/model_cards/qwen3-coder-480b-a35b-4bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-coder-480b-a35b-4bit"
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+name = "Qwen3 Coder 480B A35B (4-bit)"
+description = "Qwen3 Coder 480B (Active 35B) is a large language model trained on the Qwen3 Coder 480B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+pretty_name = "Qwen3 Coder 480B A35B (4-bit)"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 289910292480

resources/model_cards/qwen3-coder-480b-a35b-8bit.toml

@@ -0,0 +1,15 @@
+short_id = "qwen3-coder-480b-a35b-8bit"
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"
+name = "Qwen3 Coder 480B A35B (8-bit)"
+description = "Qwen3 Coder 480B (Active 35B) is a large language model trained on the Qwen3 Coder 480B dataset."
+tags = []
+
+[metadata]
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"
+pretty_name = "Qwen3 Coder 480B A35B (8-bit)"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+
+[metadata.storage_size]
+in_bytes = 579820584960

rust/parts.nix

@@ -81,20 +81,6 @@
 
       config = {
         packages = {
-          # The system_custodian binary
-          system_custodian = craneLib.buildPackage (
-            commonArgs
-            // {
-              inherit cargoArtifacts;
-              cargoExtraArgs = "-p system_custodian";
-
-              meta = {
-                description = "System custodian daemon for exo";
-                mainProgram = "system_custodian";
-              };
-            }
-          );
-
           # Python bindings wheel via maturin
           exo_pyo3_bindings = craneLib.buildPackage (
             commonArgs

rust/system_custodian/Cargo.toml

@@ -1,47 +0,0 @@
-[package]
-name = "system_custodian"
-version = { workspace = true }
-edition = { workspace = true }
-publish = false
-
-[lib]
-doctest = false
-name = "system_custodian"
-path = "src/lib.rs"
-
-[[bin]]
-path = "src/bin/main.rs"
-name = "system_custodian"
-doc = false
-
-[lints]
-workspace = true
-
-[dependencies]
-# datastructures
-either = { workspace = true }
-
-# macro dependencies
-extend = { workspace = true }
-delegate = { workspace = true }
-impl-trait-for-tuples = { workspace = true }
-derive_more = { workspace = true }
-
-# async
-tokio = { workspace = true, features = ["full"] }
-futures = { workspace = true }
-futures-timer = { workspace = true }
-
-# utility dependencies
-util = { workspace = true }
-thiserror = { workspace = true }
-#internment = { workspace = true }
-#recursion = { workspace = true }
-#generativity = { workspace = true }
-#itertools = { workspace = true }
-tracing-subscriber = { version = "0.3.19", features = ["default", "env-filter"] }
-keccak-const = { workspace = true }
-
-# tracing/logging
-log = { workspace = true }
-

rust/system_custodian/src/bin/main.rs

@@ -1,4 +0,0 @@
-//! TODO: documentation
-//!
-
-fn main() {}

rust/system_custodian/src/lib.rs

@@ -1,69 +0,0 @@
-//! This crate defines the logic of, and ways to interact with, Exo's **_System Custodian_** daemon.
-//!
-//! The **_System Custodian_** daemon is supposed to be a long-living process that precedes the
-//! launch of the Exo application, and responsible for ensuring the system (configuration, settings,
-//! etc.) is in an appropriate state to facilitate the running of Exo application.
-//! The **_System Custodian_** daemon shall expose a [D-Bus](https://www.freedesktop.org/wiki/Software/dbus/)
-//! service which Exo application use to _control & query_ it.
-//!
-//! # Lifecycle
-//! When the Exo application starts, it will _wake_ the **_System Custodian_** daemon for the
-//! duration of its lifetime, and after it has terminated the daemon will go back to sleep. When
-//! the daemon wakes up, it will configure the system into a state suitable for the Exo Application;
-//! When the daemon goes to sleep, it will revert those changes as much as it can in case they were
-//! destructive to the user's pre-existing configurations.
-//!
-//! # Responsibilities
-//! TODO: these are purely on MacOS, but change to be more broad
-//! The **_System Custodian_** daemon is responsible for using System Configuration framework to
-//!  1. duplicate the current network set
-//!  2. modify existing services to turn on IPv6 if not there
-//!  3. remove any bridge services & add any missing services that AREN'T bridge
-//! TODO: In the future:
-//!  1. run a dummy AWDL service to [allow for macOS peer-to-peer wireless networking](https://yggdrasil-network.github.io/2019/08/19/awdl.html)
-//!  2. toggle some GPU/memory configurations to speed up GPU (ask Alex what those configurations are)
-//!  3. if we ever decide to provide our **own network interfaces** that abstract over some userland
-//!     logic, this would be the place to spin that up.
-//!
-//! Then it will watch the SCDynamicStore for:
-//!  1. all __actual__ network interfaces -> collect information on them e.g. their BSD name, MAC
-//!     address, MTU, IPv6 addresses, etc. -> and set up watchers/notifiers to inform the DBus
-//!     interface of any changes
-//!  2. watch for any __undesirable__ changes to configuration and revert it
-//!
-//! It should somehow (probably through system sockets and/or BSD interface) trigger IPv6 NDP on
-//! each of the interfaces & also listen to/query for any changes on the OS routing cache??
-//! Basically emulate the `ping6 ff02::1%enX` and `ndp -an` commands BUT BETTER!!!
-//!  1. all that info should coalesce back to the overall state colleted -> should be queryable
-//!     over D-Bus
-//! TODO:
-//!  1. we might potentially add to this step a handshake of some kind...? To ensure that we can
-//!     ACTUALLY communicate with that machine over that link over e.g. TCP, UDP, etc. Will the
-//!     handshake require to know Node ID? Will the handshake require heartbeats? Who knows...
-//!  2. if we ever decide to write proprietary L2/L3 protocols for quicker communication,
-//!     e.g. [AF_NDRV](https://www.zerotier.com/blog/how-zerotier-eliminated-kernel-extensions-on-macos/)
-//!     for raw ethernet frame communication, or even a [custom thunderbolt PCIe driver](https://developer.apple.com/documentation/pcidriverkit/creating-custom-pcie-drivers-for-thunderbolt-devices),
-//!     then this would be the place to carry out discovery and propper handshakes with devices
-//!     on the other end of the link.
-//!
-
-// enable Rust-unstable features for convenience
-#![feature(trait_alias)]
-#![feature(stmt_expr_attributes)]
-#![feature(type_alias_impl_trait)]
-#![feature(specialization)]
-#![feature(unboxed_closures)]
-#![feature(const_trait_impl)]
-#![feature(fn_traits)]
-
-pub(crate) mod private {
-    // sealed traits support
-    pub trait Sealed {}
-    impl<T: ?Sized> Sealed for T {}
-}
-
-/// Namespace for all the type/trait aliases used by this crate.
-pub(crate) mod alias {}
-
-/// Namespace for crate-wide extension traits/methods
-pub(crate) mod ext {}

src/exo/master/api.py

@@ -1,8 +1,6 @@
-import asyncio
-import os
 import time
 from collections.abc import AsyncGenerator
-from typing import Any, Optional, cast
+from typing import cast
 
 import anyio
 from anyio import create_task_group
@@ -21,7 +19,6 @@ from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
     StreamableParser,
     load_harmony_encoding,
 )
-from pydantic import BaseModel
 
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
@@ -54,9 +51,7 @@ from exo.shared.types.commands import (
     CreateInstance,
     DeleteInstance,
     ForwarderCommand,
-    LaunchFLASH,
     PlaceInstance,
-    StopFLASH,
     TaskFinished,
 )
 from exo.shared.types.common import CommandId, NodeId, SessionId
@@ -65,12 +60,7 @@ from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.state import State
 from exo.shared.types.tasks import ChatCompletionTaskParams
-from exo.shared.types.worker.instances import (
-    FLASHInstance,
-    Instance,
-    InstanceId,
-    InstanceMeta,
-)
+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
@@ -80,22 +70,6 @@ from exo.utils.event_buffer import OrderedBuffer
 encoding = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)
 
 
-class ExecuteRequest(BaseModel):
-    """Request to execute a command."""
-
-    command: list[str]
-    cwd: Optional[str] = None
-    env: Optional[dict[str, str]] = None
-
-
-class ExecuteResponse(BaseModel):
-    """Response from command execution."""
-
-    exit_code: int
-    stdout: str
-    stderr: str
-
-
 def chunk_to_response(
     chunk: TokenChunk, command_id: CommandId
 ) -> ChatCompletionResponse:
@@ -204,12 +178,6 @@ class API:
         self.app.post("/bench/chat/completions")(self.bench_chat_completions)
         self.app.get("/state")(lambda: self.state)
         self.app.get("/events")(lambda: self._event_log)
-        # FLASH simulation endpoints
-        self.app.post("/flash/launch")(self.launch_flash)
-        self.app.delete("/flash/{instance_id}")(self.stop_flash)
-        self.app.get("/flash/instances")(self.list_flash_instances)
-        # Remote execution endpoint (used by exo-rsh for MPI)
-        self.app.post("/execute")(self.execute)
 
     async def place_instance(self, payload: PlaceInstanceParams):
         command = PlaceInstance(
@@ -654,145 +622,6 @@ class API:
             ]
         )
 
-    async def launch_flash(
-        self,
-        simulation_name: str,
-        flash_executable_path: str,
-        working_directory: str,
-        parameter_file_path: str = "",
-        ranks_per_node: int = 1,
-        min_nodes: int = 1,
-        hosts: str = "",
-    ) -> dict[str, str]:
-        """Launch a FLASH MPI simulation across the cluster.
-
-        Args:
-            hosts: Optional comma-separated hostnames (e.g., "s14,james21-1").
-                   If not provided, IPs are discovered from topology edges.
-        """
-        command = LaunchFLASH(
-            simulation_name=simulation_name,
-            flash_executable_path=flash_executable_path,
-            parameter_file_path=parameter_file_path,
-            working_directory=working_directory,
-            ranks_per_node=ranks_per_node,
-            min_nodes=min_nodes,
-            hosts=hosts,
-        )
-        await self._send(command)
-
-        return {
-            "message": "FLASH launch command received",
-            "command_id": str(command.command_id),
-            "simulation_name": simulation_name,
-        }
-
-    async def stop_flash(self, instance_id: InstanceId) -> dict[str, str]:
-        """Stop a running FLASH simulation."""
-        if instance_id not in self.state.instances:
-            raise HTTPException(status_code=404, detail="Instance not found")
-
-        instance = self.state.instances[instance_id]
-        if not isinstance(instance, FLASHInstance):
-            raise HTTPException(
-                status_code=400, detail="Instance is not a FLASH simulation"
-            )
-
-        command = StopFLASH(instance_id=instance_id)
-        await self._send(command)
-
-        return {
-            "message": "Stop command received",
-            "command_id": str(command.command_id),
-            "instance_id": str(instance_id),
-        }
-
-    async def list_flash_instances(self) -> list[dict[str, Any]]:
-        """List all FLASH simulation instances."""
-        flash_instances: list[dict[str, Any]] = []
-        for instance_id, instance in self.state.instances.items():
-            if isinstance(instance, FLASHInstance):
-                # Get runner statuses for this instance
-                runner_statuses: dict[str, str | None] = {}
-                for (
-                    node_id,
-                    runner_id,
-                ) in instance.shard_assignments.node_to_runner.items():
-                    runner_status = self.state.runners.get(runner_id)
-                    runner_statuses[str(node_id)] = (
-                        str(runner_status) if runner_status else None
-                    )
-
-                flash_instances.append(
-                    {
-                        "instance_id": str(instance_id),
-                        "simulation_name": instance.simulation_name,
-                        "total_ranks": instance.total_ranks,
-                        "working_directory": instance.working_directory,
-                        "runner_statuses": runner_statuses,
-                    }
-                )
-        return flash_instances
-
-    async def execute(self, request: ExecuteRequest) -> ExecuteResponse:
-        """Execute a command locally. Used by exo-rsh for MPI remote execution."""
-        cmd_str = " ".join(request.command)
-        logger.info(f"Executing: {cmd_str}")
-
-        try:
-            # Build environment
-            env = os.environ.copy()
-            if request.env:
-                env.update(request.env)
-
-            # Check if command contains shell metacharacters
-            # If so, run through shell. mpirun sends complex commands like:
-            # "VAR=value;export VAR;/path/to/prted --args"
-            needs_shell = any(c in cmd_str for c in ";|&$`")
-
-            if needs_shell:
-                process = await asyncio.create_subprocess_shell(
-                    cmd_str,
-                    stdout=asyncio.subprocess.PIPE,
-                    stderr=asyncio.subprocess.PIPE,
-                    cwd=request.cwd,
-                    env=env,
-                )
-            else:
-                process = await asyncio.create_subprocess_exec(
-                    *request.command,
-                    stdout=asyncio.subprocess.PIPE,
-                    stderr=asyncio.subprocess.PIPE,
-                    cwd=request.cwd,
-                    env=env,
-                )
-
-            stdout, stderr = await process.communicate()
-            exit_code = process.returncode or 0
-
-            logger.info(f"Command completed with exit code {exit_code}")
-
-            return ExecuteResponse(
-                exit_code=exit_code,
-                stdout=stdout.decode("utf-8", errors="replace"),
-                stderr=stderr.decode("utf-8", errors="replace"),
-            )
-
-        except FileNotFoundError:
-            logger.error(f"Command not found: {request.command[0]}")
-            return ExecuteResponse(
-                exit_code=127,
-                stdout="",
-                stderr=f"Command not found: {request.command[0]}",
-            )
-        except Exception as e:
-            logger.error(f"Execution error: {e}")
-            return ExecuteResponse(
-                exit_code=1,
-                stdout="",
-                stderr=str(e),
-            )
-
     async def run(self):
         cfg = Config()
         cfg.bind = f"0.0.0.0:{self.port}"

src/exo/master/main.py

@@ -8,7 +8,6 @@ from exo.master.placement import (
     add_instance_to_placements,
     delete_instance,
     get_transition_events,
-    place_flash_instance,
     place_instance,
 )
 from exo.shared.apply import apply
@@ -17,10 +16,8 @@ from exo.shared.types.commands import (
     CreateInstance,
     DeleteInstance,
     ForwarderCommand,
-    LaunchFLASH,
     PlaceInstance,
     RequestEventLog,
-    StopFLASH,
     TaskFinished,
     TestCommand,
 )
@@ -176,26 +173,6 @@ class Master:
                                 self.state.instances, placement
                             )
                             generated_events.extend(transition_events)
-                        case LaunchFLASH():
-                            placement = place_flash_instance(
-                                command,
-                                self.state.topology,
-                                self.state.instances,
-                            )
-                            transition_events = get_transition_events(
-                                self.state.instances, placement
-                            )
-                            generated_events.extend(transition_events)
-                        case StopFLASH():
-                            # Reuse delete_instance logic to stop FLASH simulation
-                            placement = delete_instance(
-                                DeleteInstance(instance_id=command.instance_id),
-                                self.state.instances,
-                            )
-                            transition_events = get_transition_events(
-                                self.state.instances, placement
-                            )
-                            generated_events.extend(transition_events)
                         case TaskFinished():
                             generated_events.append(
                                 TaskDeleted(

src/exo/master/placement.py

@@ -17,24 +17,20 @@ from exo.shared.topology import Topology
 from exo.shared.types.commands import (
     CreateInstance,
     DeleteInstance,
-    LaunchFLASH,
     PlaceInstance,
 )
-from exo.shared.types.common import Host, NodeId
 from exo.shared.types.events import Event, InstanceCreated, InstanceDeleted
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
+from exo.shared.types.models import ModelId
 from exo.shared.types.topology import NodeInfo
 from exo.shared.types.worker.instances import (
-    FLASHInstance,
     Instance,
     InstanceId,
     InstanceMeta,
     MlxJacclInstance,
     MlxRingInstance,
 )
-from exo.shared.types.worker.runners import RunnerId, ShardAssignments
-from exo.shared.types.worker.shards import PipelineShardMetadata, Sharding
+from exo.shared.types.worker.shards import Sharding
 
 
 def random_ephemeral_port() -> int:
@@ -169,9 +165,6 @@ def place_instance(
                 hosts_by_node=hosts_by_node,
                 ephemeral_port=ephemeral_port,
             )
-        case InstanceMeta.FLASH:
-            # FLASH instances are handled by place_flash_instance()
-            raise ValueError("FLASH instances should use place_flash_instance()")
 
     return target_instances
 
@@ -187,148 +180,6 @@ def delete_instance(
     raise ValueError(f"Instance {command.instance_id} not found")
 
 
-def place_flash_instance(
-    command: LaunchFLASH,
-    topology: Topology,
-    current_instances: Mapping[InstanceId, Instance],
-) -> dict[InstanceId, Instance]:
-    """Place a FLASH simulation instance across available nodes.
-
-    Unlike MLX instances which use ring/JACCL topology for tensor parallelism,
-    FLASH instances use MPI for communication. We just need to provide the
-    node IPs so the runner can generate an MPI hostfile.
-    """
-    instance_id = InstanceId()
-    target_instances = dict(deepcopy(current_instances))
-
-    all_nodes = list(topology.list_nodes())
-
-    if len(all_nodes) < command.min_nodes:
-        raise ValueError(
-            f"Not enough nodes: need {command.min_nodes}, have {len(all_nodes)}"
-        )
-
-    # Select nodes (take the first min_nodes)
-    selected_nodes = all_nodes[: command.min_nodes]
-
-    logger.info(
-        f"Placing FLASH instance '{command.simulation_name}' on {len(selected_nodes)} nodes"
-    )
-
-    # Build shard assignments (one runner per node for FLASH)
-    runner_to_shard: dict[RunnerId, PipelineShardMetadata] = {}
-    node_to_runner: dict[NodeId, RunnerId] = {}
-
-    # Create a dummy ModelMetadata for FLASH (required by ShardMetadata interface)
-    flash_model_meta = ModelMetadata(
-        model_id=ModelId(command.simulation_name),
-        pretty_name=f"FLASH: {command.simulation_name}",
-        storage_size=Memory(in_bytes=0),
-        n_layers=1,
-        hidden_size=1,
-        supports_tensor=False,
-    )
-
-    for i, node_info in enumerate(selected_nodes):
-        runner_id = RunnerId()
-        node_to_runner[node_info.node_id] = runner_id
-        runner_to_shard[runner_id] = PipelineShardMetadata(
-            device_rank=i,
-            world_size=len(selected_nodes),
-            model_meta=flash_model_meta,
-            start_layer=0,
-            end_layer=1,
-            n_layers=1,
-        )
-
-    shard_assignments = ShardAssignments(
-        model_id=ModelId(command.simulation_name),
-        runner_to_shard=runner_to_shard,
-        node_to_runner=node_to_runner,
-    )
-
-    # Build hosts_by_node - get hostnames/IPs for MPI hostfile generation
-    hosts_by_node: dict[NodeId, list[Host]] = {}
-
-    # If explicit hosts are provided, use them directly
-    if command.hosts:
-        explicit_hosts = [h.strip() for h in command.hosts.split(",") if h.strip()]
-        logger.info(f"FLASH placement: explicit hosts provided: {explicit_hosts}")
-        for i, node_info in enumerate(selected_nodes):
-            if i < len(explicit_hosts):
-                hosts_by_node[node_info.node_id] = [Host(ip=explicit_hosts[i], port=0)]
-                logger.info(
-                    f"FLASH placement: node {node_info.node_id} (rank {i}) -> IP {explicit_hosts[i]}"
-                )
-            else:
-                logger.warning(
-                    f"Not enough hosts provided for node {i}, using localhost"
-                )
-                hosts_by_node[node_info.node_id] = [Host(ip="127.0.0.1", port=0)]
-        logger.info(
-            f"FLASH placement: coordinator will be rank 0 at IP {explicit_hosts[0]}"
-        )
-    else:
-        # Try to get IPs from topology edges
-        for node_info in selected_nodes:
-            node_hosts: list[Host] = []
-
-            # Get IP from outgoing edges (connections to other nodes via mDNS discovery)
-            for _, edge_data in topology.out_edges(node_info.node_id):
-                if hasattr(edge_data, "send_back_multiaddr"):
-                    # Extract IP from multiaddr like /ip4/192.168.1.100/tcp/52415
-                    multiaddr = str(edge_data.send_back_multiaddr)
-                    if "/ip4/" in multiaddr:
-                        parts = multiaddr.split("/")
-                        try:
-                            ip_idx = parts.index("ip4") + 1
-                            ip = parts[ip_idx]
-                            # Skip link-local and localhost addresses
-                            if not ip.startswith("169.254.") and not ip.startswith(
-                                "127."
-                            ):
-                                node_hosts.append(Host(ip=ip, port=0))
-                                break
-                        except (ValueError, IndexError):
-                            pass
-
-            # Last resort: use localhost (will only work for single-node)
-            if not node_hosts:
-                logger.warning(
-                    f"Could not determine IP for node {node_info.node_id}, using localhost"
-                )
-                node_hosts.append(Host(ip="127.0.0.1", port=0))
-
-            hosts_by_node[node_info.node_id] = node_hosts
-
-    total_ranks = len(selected_nodes) * command.ranks_per_node
-
-    # Determine coordinator IP - first node's first host IP
-    first_node_id: NodeId = next(iter(hosts_by_node.keys()))
-    coordinator_ip: str = (
-        hosts_by_node[first_node_id][0].ip
-        if hosts_by_node[first_node_id]
-        else "127.0.0.1"
-    )
-
-    target_instances[instance_id] = FLASHInstance(
-        instance_id=instance_id,
-        shard_assignments=shard_assignments,
-        hosts_by_node=hosts_by_node,
-        flash_executable_path=command.flash_executable_path,
-        parameter_file_path=command.parameter_file_path,
-        working_directory=command.working_directory,
-        ranks_per_node=command.ranks_per_node,
-        total_ranks=total_ranks,
-        simulation_name=command.simulation_name,
-        coordinator_ip=coordinator_ip,
-    )
-
-    logger.info(f"Created FLASH instance {instance_id} with {total_ranks} total ranks")
-
-    return target_instances
-
-
 def get_transition_events(
     current_instances: Mapping[InstanceId, Instance],
     target_instances: Mapping[InstanceId, Instance],

src/exo/rsh/__init__.py

@@ -1,13 +0,0 @@
-"""Exo RSH - Remote Shell for MPI without SSH.
-
-This module provides a remote execution mechanism that allows mpirun to spawn
-processes on remote nodes without requiring SSH setup. It works by:
-
-1. Each Exo node runs an API server on port 52415 with an /execute endpoint
-2. The exo-rsh script acts as a drop-in replacement for ssh
-3. When mpirun calls "exo-rsh hostname command", it HTTP POSTs to the target's /execute
-4. The target executes the command and returns output
-
-Usage:
-    mpirun --mca plm_rsh_agent exo-rsh -np 4 --hostfile hosts.txt ./program
-"""

src/exo/rsh/client.py

@@ -1,101 +0,0 @@
-#!/usr/bin/env python3
-"""exo-rsh - Remote shell client for MPI.
-
-This script is called by mpirun as a replacement for ssh.
-Usage: exo-rsh [ssh-options...] hostname command [args...]
-
-It connects to the target node's Exo API (port 52415) and executes the command.
-"""
-
-import json
-import socket
-import sys
-from typing import Any, cast
-from urllib.error import URLError
-from urllib.request import Request, urlopen
-
-# Use the same port as Exo's API server
-EXO_API_PORT = 52415
-
-
-def resolve_hostname(hostname: str) -> str:
-    """Resolve hostname to IP address."""
-    try:
-        return socket.gethostbyname(hostname)
-    except socket.gaierror:
-        # If resolution fails, try using the hostname directly
-        return hostname
-
-
-def main():
-    # Parse arguments - mpirun calls us like: exo-rsh [options] hostname command [args...]
-    # SSH options we might see: -x (disable X11), -o options, etc.
-    args = sys.argv[1:]
-
-    # Skip SSH-style options
-    hostname = None
-    command_start = 0
-
-    i = 0
-    while i < len(args):
-        arg = args[i]
-        if arg.startswith("-"):
-            # Skip option and its value if needed
-            if arg in ("-o", "-i", "-l", "-p", "-F"):
-                i += 2  # Skip option and its argument
-                continue
-            i += 1
-            continue
-        else:
-            # First non-option is the hostname
-            hostname = arg
-            command_start = i + 1
-            break
-        i += 1
-
-    if hostname is None or command_start >= len(args):
-        print("Usage: exo-rsh [options] hostname command [args...]", file=sys.stderr)
-        sys.exit(1)
-
-    command = args[command_start:]
-
-    # Resolve hostname to IP
-    ip = resolve_hostname(hostname)
-
-    # Make request to Exo API
-    url = f"http://{ip}:{EXO_API_PORT}/execute"
-    data = json.dumps({"command": command}).encode("utf-8")
-
-    try:
-        req = Request(url, data=data, headers={"Content-Type": "application/json"})
-        with urlopen(req, timeout=300) as response:  # pyright: ignore[reportAny]
-            response_body: bytes = cast(bytes, response.read())  # pyright: ignore[reportAny]
-            result: dict[str, Any] = json.loads(response_body.decode("utf-8"))  # pyright: ignore[reportAny]
-
-        # Output stdout/stderr
-        stdout: str = cast(str, result.get("stdout", ""))
-        stderr: str = cast(str, result.get("stderr", ""))
-        exit_code: int = cast(int, result.get("exit_code", 0))
-
-        if stdout:
-            sys.stdout.write(stdout)
-            sys.stdout.flush()
-        if stderr:
-            sys.stderr.write(stderr)
-            sys.stderr.flush()
-
-        sys.exit(exit_code)
-
-    except URLError as e:
-        print(
-            f"exo-rsh: Failed to connect to {hostname}:{EXO_API_PORT}: {e}",
-            file=sys.stderr,
-        )
-        sys.exit(255)
-    except Exception as e:
-        print(f"exo-rsh: Error: {e}", file=sys.stderr)
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

src/exo/shared/models/model_cards.py

@@ -1,3 +1,6 @@
+from anyio import Path, open_file
+import tomlkit 
+
 from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
 from exo.utils.pydantic_ext import CamelCaseModel
@@ -11,35 +14,24 @@ class ModelCard(CamelCaseModel):
     tags: list[str]
     metadata: ModelMetadata
 
+    @staticmethod
+    async def load(path: Path) -> "ModelCard":
+        async with await open_file(path) as f:
+            data = await f.read()
+            py = tomlkit.loads(data)
+            return ModelCard.model_validate(py)
+
+    async def save(self, path: Path):
+        async with await open_file(path, "w") as f:
+            py = self.model_dump()
+            data = tomlkit.dumps(py) # pyright: ignore[reportUnknownMemberType]
+            await f.write(data)
+
+
+
 
 MODEL_CARDS: dict[str, ModelCard] = {
     # deepseek v3
-    # "deepseek-v3-0324:4bit": ModelCard(
-    #     short_id="deepseek-v3-0324:4bit",
-    #     model_id="mlx-community/DeepSeek-V3-0324-4bit",
-    #     name="DeepSeek V3 0324 (4-bit)",
-    #     description="""DeepSeek V3 is a large language model trained on the DeepSeek V3 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-V3-0324-4bit"),
-    #         pretty_name="DeepSeek V3 0324 (4-bit)",
-    #         storage_size=Memory.from_kb(409706307),
-    #         n_layers=61,
-    #     ),
-    # ),
-    # "deepseek-v3-0324": ModelCard(
-    #     short_id="deepseek-v3-0324",
-    #     model_id="mlx-community/DeepSeek-v3-0324-8bit",
-    #     name="DeepSeek V3 0324 (8-bit)",
-    #     description="""DeepSeek V3 is a large language model trained on the DeepSeek V3 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-v3-0324-8bit"),
-    #         pretty_name="DeepSeek V3 0324 (8-bit)",
-    #         storage_size=Memory.from_kb(754706307),
-    #         n_layers=61,
-    #     ),
-    # ),
     "deepseek-v3.1-4bit": ModelCard(
         short_id="deepseek-v3.1-4bit",
         model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
@@ -70,65 +62,6 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
-    # "deepseek-v3.2": ModelCard(
-    #     short_id="deepseek-v3.2",
-    #     model_id=ModelId("mlx-community/DeepSeek-V3.2-8bit"),
-    #     name="DeepSeek V3.2 (8-bit)",
-    #     description="""DeepSeek V3.2 is a large language model trained on the DeepSeek V3.2 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-V3.2-8bit"),
-    #         pretty_name="DeepSeek V3.2 (8-bit)",
-    #         storage_size=Memory.from_kb(754706307),
-    #         n_layers=61,
-    #         hidden_size=7168,
-    #         supports_tensor=True,
-    #     ),
-    # ),
-    # "deepseek-v3.2-4bit": ModelCard(
-    #     short_id="deepseek-v3.2-4bit",
-    #     model_id=ModelId("mlx-community/DeepSeek-V3.2-4bit"),
-    #     name="DeepSeek V3.2 (4-bit)",
-    #     description="""DeepSeek V3.2 is a large language model trained on the DeepSeek V3.2 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-V3.2-4bit"),
-    #         pretty_name="DeepSeek V3.2 (4-bit)",
-    #         storage_size=Memory.from_kb(754706307 // 2),  # TODO !!!!!
-    #         n_layers=61,
-    #         hidden_size=7168,
-    #         supports_tensor=True,
-    #     ),
-    # ),
-    # deepseek r1
-    # "deepseek-r1-0528-4bit": ModelCard(
-    #     short_id="deepseek-r1-0528-4bit",
-    #     model_id="mlx-community/DeepSeek-R1-0528-4bit",
-    #     name="DeepSeek-R1-0528 (4-bit)",
-    #     description="""DeepSeek R1 is a large language model trained on the DeepSeek R1 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-R1-0528-4bit"),
-    #         pretty_name="DeepSeek R1 671B (4-bit)",
-    #         storage_size=Memory.from_kb(409706307),
-    #         n_layers=61,
-    #         hidden_size=7168,
-    #     ),
-    # ),
-    # "deepseek-r1-0528": ModelCard(
-    #     short_id="deepseek-r1-0528",
-    #     model_id="mlx-community/DeepSeek-R1-0528-8bit",
-    #     name="DeepSeek-R1-0528 (8-bit)",
-    #     description="""DeepSeek R1 is a large language model trained on the DeepSeek R1 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-R1-0528-8bit"),
-    #         pretty_name="DeepSeek R1 671B (8-bit)",
-    #         storage_size=Memory.from_bytes(754998771712),
-    #         n_layers=61,
-    # .       hidden_size=7168,
-    #     ),
-    # ),
     # kimi k2
     "kimi-k2-instruct-4bit": ModelCard(
         short_id="kimi-k2-instruct-4bit",
@@ -525,8 +458,9 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
-    # Needs to be quantized g32 or g16.
+    # glm 4.5
     "glm-4.5-air-8bit": ModelCard(
+        # Needs to be quantized g32 or g16 to work with tensor parallel
         short_id="glm-4.5-air-8bit",
         model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
         name="GLM 4.5 Air 8bit",
@@ -556,6 +490,7 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
+    # glm 4.7
     "glm-4.7-4bit": ModelCard(
         short_id="glm-4.7-4bit",
         model_id=ModelId("mlx-community/GLM-4.7-4bit"),
@@ -601,6 +536,7 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
+    # minimax-m2
     "minimax-m2.1-8bit": ModelCard(
         short_id="minimax-m2.1-8bit",
         model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
@@ -631,19 +567,4 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
-    # "devstral-2-123b-instruct-2512-8bit": ModelCard(
-    #     short_id="devstral-2-123b-instruct-2512-8bit",
-    #     model_id=ModelId("mlx-community/Devstral-2-123B-Instruct-2512-8bit"),
-    #     name="Devstral 2 123B Instruct 2512 (8-bit, MLX)",
-    #     description="""Mistral AI's Devstral 2 123B Instruct (2512) is an agentic coding model.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/Devstral-2-123B-Instruct-2512-8bit"),
-    #         pretty_name="Devstral 2 123B Instruct 2512 (8-bit, MLX)",
-    #         storage_size=Memory.from_kb(133_000_000),
-    #         n_layers=88,
-    #         hidden_size=12288,
-    #         supports_tensor=True,
-    #     ),
-    # ),
 }

src/exo/shared/types/commands.py

@@ -35,26 +35,6 @@ class DeleteInstance(BaseCommand):
     instance_id: InstanceId
 
 
-class LaunchFLASH(BaseCommand):
-    """Command to launch a FLASH MPI simulation."""
-
-    simulation_name: str
-    flash_executable_path: str
-    parameter_file_path: str
-    working_directory: str
-    ranks_per_node: int = 1
-    min_nodes: int = 1
-    # Optional: explicit hostnames for MPI (e.g., "s14,james21-1")
-    # Used when topology edges don't contain IP addresses
-    hosts: str = ""
-
-
-class StopFLASH(BaseCommand):
-    """Command to stop a running FLASH simulation."""
-
-    instance_id: InstanceId
-
-
 class TaskFinished(BaseCommand):
     finished_command_id: CommandId
 
@@ -70,8 +50,6 @@ Command = (
     | PlaceInstance
     | CreateInstance
     | DeleteInstance
-    | LaunchFLASH
-    | StopFLASH
     | TaskFinished
 )
 

src/exo/shared/types/worker/instances.py

@@ -14,7 +14,6 @@ class InstanceId(Id):
 class InstanceMeta(str, Enum):
     MlxRing = "MlxRing"
     MlxJaccl = "MlxJaccl"
-    FLASH = "FLASH"
 
 
 class BaseInstance(TaggedModel):
@@ -35,27 +34,8 @@ class MlxJacclInstance(BaseInstance):
     jaccl_coordinators: dict[NodeId, str]
 
 
-class FLASHInstance(BaseInstance):
-    """Instance for FLASH MPI simulation.
-
-    Unlike MLX instances which do tensor parallelism, FLASH instances
-    coordinate MPI processes across nodes. Each node runs one or more
-    MPI ranks of the FLASH simulation.
-    """
-
-    hosts_by_node: dict[NodeId, list[Host]]
-    flash_executable_path: str
-    parameter_file_path: str
-    working_directory: str
-    ranks_per_node: int = 1
-    total_ranks: int
-    simulation_name: str
-    coordinator_ip: str
-    network_interface: str = "en0"  # Network interface for MPI (e.g., en0, eth0)
-
-
 # TODO: Single node instance
-Instance = MlxRingInstance | MlxJacclInstance | FLASHInstance
+Instance = MlxRingInstance | MlxJacclInstance
 
 
 class BoundInstance(CamelCaseModel):

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

@@ -164,11 +164,6 @@ def mlx_distributed_init(
                 os.environ["MLX_JACCL_COORDINATOR"] = jaccl_coordinator
                 group = mx.distributed.init(backend="jaccl", strict=True)
 
-            case _:
-                raise ValueError(
-                    f"Unsupported instance type for MLX distributed: {type(bound_instance.instance)}"
-                )
-
         logger.info(f"Rank {rank} mlx distributed initialization complete")
 
         return group

src/exo/worker/plan.py

@@ -21,12 +21,7 @@ from exo.shared.types.worker.downloads import (
     DownloadOngoing,
     DownloadProgress,
 )
-from exo.shared.types.worker.instances import (
-    BoundInstance,
-    FLASHInstance,
-    Instance,
-    InstanceId,
-)
+from exo.shared.types.worker.instances import BoundInstance, Instance, InstanceId
 from exo.shared.types.worker.runners import (
     RunnerConnected,
     RunnerConnecting,
@@ -55,11 +50,6 @@ def plan(
     all_runners: Mapping[RunnerId, RunnerStatus],  # all global
     tasks: Mapping[TaskId, Task],
 ) -> Task | None:
-    # Check for FLASH instance tasks first
-    flash_task = _plan_flash(runners, instances)
-    if flash_task is not None:
-        return flash_task
-
     # Python short circuiting OR logic should evaluate these sequentially.
     return (
         _kill_runner(runners, all_runners, instances)
@@ -72,34 +62,6 @@ def plan(
     )
 
 
-def _plan_flash(
-    runners: Mapping[RunnerId, RunnerSupervisor],
-    instances: Mapping[InstanceId, Instance],
-) -> Task | None:
-    """Plan tasks specifically for FLASH instances.
-
-    FLASH instances have a simpler lifecycle:
-    - CreateRunner (handled by _create_runner)
-    - LoadModel (starts the simulation immediately)
-    - Shutdown (handled by _kill_runner)
-
-    This function handles the LoadModel step for FLASH instances,
-    skipping the MLX-specific download/init/warmup steps.
-    """
-    for runner in runners.values():
-        instance = runner.bound_instance.instance
-
-        # Only handle FLASH instances
-        if not isinstance(instance, FLASHInstance):
-            continue
-
-        # If runner is idle, emit LoadModel to start the simulation
-        if isinstance(runner.status, RunnerIdle):
-            return LoadModel(instance_id=instance.instance_id)
-
-    return None
-
-
 def _kill_runner(
     runners: Mapping[RunnerId, RunnerSupervisor],
     all_runners: Mapping[RunnerId, RunnerStatus],
@@ -152,10 +114,6 @@ def _model_needs_download(
     download_status: Mapping[ModelId, DownloadProgress],
 ) -> DownloadModel | None:
     for runner in runners.values():
-        # FLASH instances don't need model downloads
-        if isinstance(runner.bound_instance.instance, FLASHInstance):
-            continue
-
         model_id = runner.bound_instance.bound_shard.model_meta.model_id
         if isinstance(runner.status, RunnerIdle) and (
             model_id not in download_status

src/exo/worker/runner/bootstrap.py

@@ -4,11 +4,7 @@ import loguru
 
 from exo.shared.types.events import Event, RunnerStatusUpdated
 from exo.shared.types.tasks import Task
-from exo.shared.types.worker.instances import (
-    BoundInstance,
-    FLASHInstance,
-    MlxJacclInstance,
-)
+from exo.shared.types.worker.instances import BoundInstance, MlxJacclInstance
 from exo.shared.types.worker.runners import RunnerFailed
 from exo.utils.channels import ClosedResourceError, MpReceiver, MpSender
 
@@ -21,27 +17,20 @@ def entrypoint(
     task_receiver: MpReceiver[Task],
     _logger: "loguru.Logger",
 ) -> None:
+    if (
+        isinstance(bound_instance.instance, MlxJacclInstance)
+        and len(bound_instance.instance.ibv_devices) >= 2
+    ):
+        os.environ["MLX_METAL_FAST_SYNCH"] = "1"
+
     global logger
     logger = _logger
 
-    # Route based on instance type
+    # Import main after setting global logger - this lets us just import logger from this module
     try:
-        if isinstance(bound_instance.instance, FLASHInstance):
-            # FLASH MPI simulation runner
-            from exo.worker.runner.flash_runner import main
+        from exo.worker.runner.runner import main
 
-            main(bound_instance, event_sender, task_receiver)
-        else:
-            # MLX runner (default)
-            if (
-                isinstance(bound_instance.instance, MlxJacclInstance)
-                and len(bound_instance.instance.ibv_devices) >= 2
-            ):
-                os.environ["MLX_METAL_FAST_SYNCH"] = "1"
-
-            from exo.worker.runner.runner import main
-
-            main(bound_instance, event_sender, task_receiver)
+        main(bound_instance, event_sender, task_receiver)
     except ClosedResourceError:
         logger.warning("Runner communication closed unexpectedly")
     except Exception as e:

src/exo/worker/runner/flash_runner.py

@@ -1,301 +0,0 @@
-"""FLASH MPI Runner - spawns and monitors FLASH simulations.
-
-Exo-native distributed MPI:
-- Exo handles node discovery and coordination
-- Coordinator generates hostfile from Exo topology
-- mpirun uses exo-rsh (no SSH required) to spawn on remote nodes
-- exo-rsh connects to each node's Exo API (/execute endpoint) for remote execution
-- Workers just report ready and wait
-"""
-
-import os
-import shutil
-import socket
-import subprocess
-import threading
-
-from exo.shared.types.events import (
-    Event,
-    RunnerStatusUpdated,
-    TaskAcknowledged,
-    TaskStatusUpdated,
-)
-from exo.shared.types.tasks import (
-    LoadModel,
-    Shutdown,
-    Task,
-    TaskStatus,
-)
-from exo.shared.types.worker.instances import BoundInstance, FLASHInstance
-from exo.shared.types.worker.runners import (
-    RunnerFailed,
-    RunnerIdle,
-    RunnerLoading,
-    RunnerReady,
-    RunnerRunning,
-    RunnerShutdown,
-    RunnerShuttingDown,
-    RunnerStatus,
-)
-from exo.utils.channels import MpReceiver, MpSender
-from exo.worker.runner.bootstrap import logger
-
-# Find mpirun in PATH, fallback to common locations
-MPIRUN_PATH = shutil.which("mpirun") or "/opt/homebrew/bin/mpirun"
-
-# exo-rsh is installed as console script by exo package
-_exo_rsh_path = shutil.which("exo-rsh")
-if not _exo_rsh_path:
-    raise RuntimeError("exo-rsh not found in PATH - this should be installed with exo")
-EXO_RSH_PATH: str = _exo_rsh_path
-
-
-def get_my_rank(instance: FLASHInstance, my_node_id: str) -> int:
-    """Determine this node's rank based on position in hosts_by_node."""
-    for i, node_id in enumerate(instance.hosts_by_node.keys()):
-        if str(node_id) == str(my_node_id):
-            return i
-    return -1
-
-
-def get_coordinator_host(instance: FLASHInstance) -> str:
-    """Get the IP of the coordinator node."""
-    return instance.coordinator_ip
-
-
-def resolve_host(host: str) -> str:
-    """Resolve host string to a usable hostname for MPI hostfile.
-
-    Accepts either an IP address or hostname. For IPs, attempts to resolve
-    to a hostname via DNS/mDNS. Hostnames are returned as-is after validation.
-    """
-    # Check if input is already a hostname (not an IP)
-    try:
-        socket.inet_aton(host)
-        is_ip = True
-    except socket.error:
-        is_ip = False
-
-    if not is_ip:
-        # Already a hostname, verify it resolves and return as-is
-        try:
-            socket.gethostbyname(host)
-            return host
-        except socket.gaierror:
-            logger.warning(f"Hostname {host} does not resolve, using anyway")
-            return host
-
-    # It's an IP address, try to resolve to hostname
-    try:
-        hostname, _, _ = socket.gethostbyaddr(host)
-        hostname = hostname.split(".")[0]
-        logger.info(f"Resolved {host} to {hostname}")
-        return hostname
-    except socket.herror:
-        pass
-
-    # Fall back to IP
-    logger.warning(f"Could not resolve {host} to hostname, using IP directly")
-    return host
-
-
-def generate_hostfile(instance: FLASHInstance, working_dir: str) -> str:
-    """Generate MPI hostfile from instance topology."""
-    hostfile_path = os.path.join(working_dir, "flash_hosts.txt")
-    with open(hostfile_path, "w") as f:
-        for _node_id, hosts in instance.hosts_by_node.items():
-            if hosts:
-                host = resolve_host(hosts[0].ip)
-                f.write(f"{host} slots={instance.ranks_per_node}\n")
-    logger.info(f"Generated hostfile at {hostfile_path}")
-    with open(hostfile_path, "r") as f:
-        logger.info(f"Hostfile contents:\n{f.read()}")
-    return hostfile_path
-
-
-def main(
-    bound_instance: BoundInstance,
-    event_sender: MpSender[Event],
-    task_receiver: MpReceiver[Task],
-):
-    """Main FLASH runner loop.
-
-    Coordinator: generates hostfile and runs mpirun (uses exo-rsh instead of SSH)
-    Workers: just report ready and wait for mpirun to spawn processes on them
-    """
-    assert isinstance(bound_instance.instance, FLASHInstance)
-    instance = bound_instance.instance
-    runner_id = bound_instance.bound_runner_id
-    my_node_id = str(bound_instance.bound_node_id)
-
-    logger.info(f"FLASH runner starting for simulation: {instance.simulation_name}")
-
-    my_rank = get_my_rank(instance, my_node_id)
-    world_size = len(instance.hosts_by_node)
-    is_coordinator = my_rank == 0
-    coordinator_ip = get_coordinator_host(instance)
-
-    logger.info(
-        f"FLASH node: rank={my_rank}, world_size={world_size}, coordinator={is_coordinator}"
-    )
-    logger.info(f"FLASH coordinator IP: {coordinator_ip}")
-
-    process: subprocess.Popen[bytes] | None = None
-    current_status: RunnerStatus = RunnerIdle()
-    shutdown_requested = False
-
-    event_sender.send(
-        RunnerStatusUpdated(runner_id=runner_id, runner_status=current_status)
-    )
-
-    def monitor_output(proc: subprocess.Popen[bytes]) -> None:
-        """Monitor FLASH stdout for progress updates."""
-        if proc.stdout is None:
-            return
-        for line in iter(proc.stdout.readline, b""):
-            if shutdown_requested:
-                break
-            try:
-                decoded: str = line.decode("utf-8", errors="replace").strip()
-                if decoded:
-                    logger.info(f"[FLASH] {decoded}")
-            except Exception as e:
-                logger.warning(f"Error parsing FLASH output: {e}")
-
-    with task_receiver as tasks:
-        for task in tasks:
-            event_sender.send(
-                TaskStatusUpdated(task_id=task.task_id, task_status=TaskStatus.Running)
-            )
-            event_sender.send(TaskAcknowledged(task_id=task.task_id))
-
-            match task:
-                case LoadModel() if isinstance(current_status, RunnerIdle):
-                    current_status = RunnerLoading()
-                    logger.info("Starting FLASH simulation")
-                    event_sender.send(
-                        RunnerStatusUpdated(
-                            runner_id=runner_id, runner_status=current_status
-                        )
-                    )
-
-                    try:
-                        if is_coordinator:
-                            # Coordinator: generate hostfile and run mpirun
-                            hostfile = generate_hostfile(
-                                instance, instance.working_directory
-                            )
-
-                            iface = instance.network_interface
-                            cmd = [
-                                MPIRUN_PATH,
-                                "-np",
-                                str(instance.total_ranks),
-                                "--hostfile",
-                                hostfile,
-                                "--wdir",
-                                instance.working_directory,
-                                "--oversubscribe",
-                                "--mca",
-                                "btl",
-                                "tcp,self",
-                                "--mca",
-                                "btl_tcp_if_include",
-                                iface,
-                                "--mca",
-                                "oob_tcp_if_include",
-                                iface,
-                                "--mca",
-                                "plm_rsh_no_tree_spawn",
-                                "1",
-                            ]
-
-                            # Use exo-rsh for remote execution (no SSH needed)
-                            cmd.extend(["--mca", "plm_rsh_agent", EXO_RSH_PATH])
-
-                            cmd.append(instance.flash_executable_path)
-
-                            logger.info(f"FLASH distributed launch: {' '.join(cmd)}")
-
-                            process = subprocess.Popen(
-                                cmd,
-                                cwd=instance.working_directory,
-                                stdout=subprocess.PIPE,
-                                stderr=subprocess.STDOUT,
-                            )
-
-                            monitor_thread = threading.Thread(
-                                target=monitor_output, args=(process,), daemon=True
-                            )
-                            monitor_thread.start()
-
-                            current_status = RunnerRunning()
-                            logger.info(
-                                f"FLASH running on {world_size} nodes with {instance.total_ranks} ranks"
-                            )
-
-                        else:
-                            # Worker: mpirun on coordinator will use exo-rsh to spawn processes here
-                            logger.info(
-                                f"Worker {my_rank}: Ready for mpirun to spawn processes via exo-rsh"
-                            )
-                            current_status = RunnerRunning()
-
-                    except Exception as e:
-                        logger.error(f"Failed to start FLASH: {e}")
-                        import traceback
-
-                        logger.error(traceback.format_exc())
-                        current_status = RunnerFailed(error_message=str(e))
-
-                case Shutdown():
-                    shutdown_requested = True
-                    current_status = RunnerShuttingDown()
-                    logger.info("FLASH runner shutting down")
-                    event_sender.send(
-                        RunnerStatusUpdated(
-                            runner_id=runner_id, runner_status=current_status
-                        )
-                    )
-
-                    if process and process.poll() is None:
-                        logger.info("Terminating FLASH simulation")
-                        process.terminate()
-                        try:
-                            process.wait(timeout=10)
-                        except subprocess.TimeoutExpired:
-                            logger.warning("FLASH didn't terminate, killing")
-                            process.kill()
-                            process.wait()
-
-                    current_status = RunnerShutdown()
-
-                case _:
-                    if process and process.poll() is not None:
-                        exit_code = process.returncode
-                        if exit_code == 0:
-                            logger.info("FLASH simulation completed successfully")
-                            current_status = RunnerReady()
-                        else:
-                            logger.error(
-                                f"FLASH simulation failed with code {exit_code}"
-                            )
-                            current_status = RunnerFailed(
-                                error_message=f"Exit code {exit_code}"
-                            )
-
-            event_sender.send(
-                TaskStatusUpdated(task_id=task.task_id, task_status=TaskStatus.Complete)
-            )
-            event_sender.send(
-                RunnerStatusUpdated(runner_id=runner_id, runner_status=current_status)
-            )
-
-            if isinstance(current_status, RunnerShutdown):
-                break
-
-    if process and process.poll() is None:
-        process.terminate()
-        process.wait(timeout=5)
-
-    logger.info("FLASH runner exiting")