feat: centralize model configurations and benchmark settings into a new models.py module and update Dockerfile and scripts to use it.

benchmarks/run_vllm_bench.py

@@ -2,17 +2,32 @@
 import subprocess, time, json, sys, os, requests, argparse
 from pathlib import Path
 
+
 # =========================
 # ⚙️ GLOBAL SETTINGS
 # =========================
 
-# HARDWARE: 1x Strix Halo (128GB, RDNA 3.5)
-GPU_UTIL = "0.90" 
-# 1. THROUGHPUT CONFIG
-OFF_NUM_PROMPTS      = 200 
-OFF_FORCED_OUTPUT    = "512"
-# Default fallback if not specified in MODEL_TABLE
-DEFAULT_BATCH_TOKENS = "8192"
+try:
+    import models
+except ImportError:
+    # If running locally and models.py is in ../scripts?
+    # Or if running in /opt where models.py is alongside.
+    # We will try adding current dir to path just in case
+    sys.path.append(os.getcwd())
+    try:
+        import models
+    except ImportError:
+        # Fallback for local structure: assuming this is in benchmarks/ and models is in scripts/
+        sys.path.append(str(Path(__file__).parent.parent / "scripts"))
+        import models
+
+# Import from shared config
+MODEL_TABLE = models.MODEL_TABLE
+MODELS_TO_RUN = models.MODELS_TO_RUN
+GPU_UTIL = models.GPU_UTIL
+OFF_NUM_PROMPTS = models.OFF_NUM_PROMPTS
+OFF_FORCED_OUTPUT = models.OFF_FORCED_OUTPUT
+DEFAULT_BATCH_TOKENS = models.DEFAULT_BATCH_TOKENS
 
 # Fallbacks
 FALLBACK_INPUT_LEN  = 1024
@@ -21,84 +36,6 @@ FALLBACK_OUTPUT_LEN = 512
 RESULTS_DIR = Path("benchmark_results")
 RESULTS_DIR.mkdir(exist_ok=True)
 
-# =========================
-# 🛠️ MODEL CONFIGURATION 🛠️
-# =========================
-
-MODEL_TABLE = {
-    # 1. Llama 3.1 8B Instruct
-    # MAD uses 131k tokens. We scale to 32k for 32GB VRAM safety.
-    "meta-llama/Meta-Llama-3.1-8B-Instruct": {
-        "trust_remote": False,
-        "valid_tp": [1, 2],
-        "max_num_seqs": "64",
-        "max_tokens": "32768" 
-    },
-    
-    "google/gemma-3-12b-it": {
-        "trust_remote": False,
-        "valid_tp": [1, 2],
-        "max_num_seqs": "64",
-        "max_tokens": "32768" 
-    },
-    # 2. GPT-OSS 20B (MXFP4)
-    # MAD Row 0 uses 8192. We match this exactly.
-    "openai/gpt-oss-20b": {
-        "trust_remote": True,
-        "valid_tp": [1, 2],
-        "max_num_seqs": "64",
-        "max_tokens": "8192"
-    },
-    
-    "openai/gpt-oss-120b": {
-        "trust_remote": True,
-        "valid_tp": [1],
-        "max_num_seqs": "64",
-        "max_tokens": "8192"
-    },
-
-
-    "Qwen/Qwen3-14B-AWQ": {
-        "trust_remote": True,
-        "valid_tp": [1], # Too big for single GPU
-        "max_num_seqs": "32", # Lower concurrency for safety
-        "max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive
-        "enforce_eager": False, 
-        "env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error
-    },
-
-    # 4. Qwen 30B 4-bit
-    "cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit": {
-        "trust_remote": True,
-        "enforce_eager": False, 
-        "valid_tp": [1, 2],
-        "max_num_seqs": "64",
-        "max_tokens": "32768"
-    },
-
-    # 5. Qwen 80B AWQ
-    # Size: ~48GB. Fits on 2x32GB (64GB). Leftover for Cache: ~16GB.
-    # Config: 20k ctx fits in that cache. Eager mode required for stability.
-     "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16": {
-        "trust_remote": True,
-        "valid_tp": [1], # Too big for single GPU
-        "max_num_seqs": "32", # Lower concurrency for safety
-        "max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive
-        "enforce_eager": True, 
-        "env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error
-    },
-
-}
-
-MODELS_TO_RUN = [
-    "meta-llama/Meta-Llama-3.1-8B-Instruct",
-    "google/gemma-3-12b-it",
-    "Qwen/Qwen3-14B-AWQ",
-    "openai/gpt-oss-20b",
-    "openai/gpt-oss-120b",
-    "cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit",
-    "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
-]
 
 # =========================
 # UTILS

benchmarks/vllm_cluster_bench.py

@@ -25,12 +25,21 @@ RESULTS_DIR.mkdir(exist_ok=True)
 # Since this is a new file in root/benchmarks? No, likely scripts/ or same dir.
 # Let's assume it's in the same dir as run_vllm_bench.py.
 
+
 try:
-    from run_vllm_bench import MODEL_TABLE, MODELS_TO_RUN
+    import models
 except ImportError:
-    # Fallback if run directly and path issues
-    sys.path.append(os.path.dirname(__file__))
-    from run_vllm_bench import MODEL_TABLE, MODELS_TO_RUN
+    # If in /opt, this should work if path includes ., otherwise:
+    sys.path.append(os.getcwd())
+    try:
+        import models
+        # Also try parent/scripts for local dev if above failed?
+    except ImportError:
+        sys.path.append(str(Path(__file__).parent.parent / "scripts"))
+        import models
+        
+MODEL_TABLE = models.MODEL_TABLE
+MODELS_TO_RUN = models.MODELS_TO_RUN
 
 # =========================
 # UTILS (Adapted for Cluster)