add relevant, AND cheaper models

backend/config/models_config.py

@@ -6,18 +6,25 @@ This file centralizes all configurations related to models and providers used in
 
 # Definition of preferred providers, used in get_available_model_provider.py
 # PREFERRED_PROVIDERS = ["sambanova", "novita"]
-PREFERRED_PROVIDERS = ["fireworks-ai", "sambanova", "novita"]
+# increased number of providers
+PREFERRED_PROVIDERS = ["fireworks-ai", "cerebras", "sambanova", "together", "nebius", "novita", "hyperbolic", "cohere", "hf-inference"]
 
 # Default models to evaluate for evaluation
 DEFAULT_EVALUATION_MODELS = [
-    "Qwen/QwQ-32B",
-    "Qwen/Qwen2.5-72B-Instruct",
-    "Qwen/Qwen2.5-32B-Instruct",
-    "meta-llama/Llama-3.1-8B-Instruct",
-    "meta-llama/Llama-3.3-70B-Instruct",
-    "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
-    "mistralai/Mistral-Small-24B-Instruct-2501",
+    "Qwen/Qwen3-30B-A3B",
+    "Qwen/Qwen3-235B-A22B",
+    "meta-llama/Llama-4-Maverick-17B-128E-Instruct",
+    "meta-llama/Llama-4-Scout-17B-16E-Instruct"
 ]
+# DEFAULT_EVALUATION_MODELS = [
+#     "Qwen/QwQ-32B",
+#     "Qwen/Qwen2.5-72B-Instruct",
+#     "Qwen/Qwen2.5-32B-Instruct",
+#     "meta-llama/Llama-3.1-8B-Instruct",
+#     "meta-llama/Llama-3.3-70B-Instruct",
+#     "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+#     "mistralai/Mistral-Small-24B-Instruct-2501",
+# ]
 
 # Modèles alternatifs à utiliser si le modèle par défaut n'est pas disponible
 ALTERNATIVE_BENCHMARK_MODELS = [
@@ -33,7 +40,8 @@ ALTERNATIVE_BENCHMARK_MODELS = [
 ]
 
 # Required model for create_bench_config_file.py (only one default model)
-DEFAULT_BENCHMARK_MODEL = "Qwen/Qwen2.5-32B-Instruct"
+DEFAULT_BENCHMARK_MODEL = "Qwen/Qwen3-30B-A3B"
+# DEFAULT_BENCHMARK_MODEL = "Qwen/Qwen2.5-32B-Instruct"
 
 # Models by roles for benchmark configuration
 # All roles use the default model except chunking