disable models unrunnable on hf spaces

app.py

@@ -26,15 +26,15 @@ cancel_event = threading.Event()
 MODELS = {
     "Taiwan-ELM-1_1B-Instruct": {"repo_id": "liswei/Taiwan-ELM-1_1B-Instruct", "description": "Taiwan-ELM-1_1B-Instruct"},
     "Taiwan-ELM-270M-Instruct": {"repo_id": "liswei/Taiwan-ELM-270M-Instruct", "description": "Taiwan-ELM-270M-Instruct"},
-    "Granite-4.0-Tiny-Preview": {"repo_id": "ibm-granite/granite-4.0-tiny-preview", "description": "Granite-4.0-Tiny-Preview"},
+    # "Granite-4.0-Tiny-Preview": {"repo_id": "ibm-granite/granite-4.0-tiny-preview", "description": "Granite-4.0-Tiny-Preview"},
     "Qwen3-0.6B":    {"repo_id":"Qwen/Qwen3-0.6B","description":"Dense causal language model with 0.6 B total parameters (0.44 B non-embedding), 28 transformer layers, 16 query heads & 8 KV heads, native 32 768-token context window, dual-mode generation, full multilingual & agentic capabilities."},
     "Qwen3-1.7B":    {"repo_id":"Qwen/Qwen3-1.7B","description":"Dense causal language model with 1.7 B total parameters (1.4 B non-embedding), 28 layers, 16 query heads & 8 KV heads, 32 768-token context, stronger reasoning vs. 0.6 B variant, dual-mode inference, instruction following across 100+ languages."},
     "Qwen3-4B":      {"repo_id":"Qwen/Qwen3-4B","description":"Dense causal language model with 4.0 B total parameters (3.6 B non-embedding), 36 layers, 32 query heads & 8 KV heads, native 32 768-token context (extendable to 131 072 via YaRN), balanced mid-range capacity & long-context reasoning."},
     "Qwen3-8B":      {"repo_id":"Qwen/Qwen3-8B","description":"Dense causal language model with 8.2 B total parameters (6.95 B non-embedding), 36 layers, 32 query heads & 8 KV heads, 32 768-token context (131 072 via YaRN), excels at multilingual instruction following & zero-shot tasks."},
     "Qwen3-14B":     {"repo_id":"Qwen/Qwen3-14B","description":"Dense causal language model with 14.8 B total parameters (13.2 B non-embedding), 40 layers, 40 query heads & 8 KV heads, 32 768-token context (131 072 via YaRN), enhanced human preference alignment & advanced agent integration."},
-    "Qwen3-32B":     {"repo_id":"Qwen/Qwen3-32B","description":"Dense causal language model with 32.8 B total parameters (31.2 B non-embedding), 64 layers, 64 query heads & 8 KV heads, 32 768-token context (131 072 via YaRN), flagship variant delivering state-of-the-art reasoning & instruction following."},
-    "Qwen3-30B-A3B": {"repo_id":"Qwen/Qwen3-30B-A3B","description":"Mixture-of-Experts model with 30.5 B total parameters (29.9 B non-embedding, 3.3 B activated per token), 48 layers, 128 experts (8 activated per token), 32 query heads & 4 KV heads, 32 768-token context (131 072 via YaRN), MoE routing for scalable specialized reasoning."},
-    "Qwen3-235B-A22B":{"repo_id":"Qwen/Qwen3-235B-A22B","description":"Mixture-of-Experts model with 235 B total parameters (234 B non-embedding, 22 B activated per token), 94 layers, 128 experts (8 activated per token), 64 query heads & 4 KV heads, 32 768-token context (131 072 via YaRN), ultra-scale reasoning & agentic workflows."},
+    # "Qwen3-32B":     {"repo_id":"Qwen/Qwen3-32B","description":"Dense causal language model with 32.8 B total parameters (31.2 B non-embedding), 64 layers, 64 query heads & 8 KV heads, 32 768-token context (131 072 via YaRN), flagship variant delivering state-of-the-art reasoning & instruction following."},
+    # "Qwen3-30B-A3B": {"repo_id":"Qwen/Qwen3-30B-A3B","description":"Mixture-of-Experts model with 30.5 B total parameters (29.9 B non-embedding, 3.3 B activated per token), 48 layers, 128 experts (8 activated per token), 32 query heads & 4 KV heads, 32 768-token context (131 072 via YaRN), MoE routing for scalable specialized reasoning."},
+    # "Qwen3-235B-A22B":{"repo_id":"Qwen/Qwen3-235B-A22B","description":"Mixture-of-Experts model with 235 B total parameters (234 B non-embedding, 22 B activated per token), 94 layers, 128 experts (8 activated per token), 64 query heads & 4 KV heads, 32 768-token context (131 072 via YaRN), ultra-scale reasoning & agentic workflows."},
     "Gemma-3-4B-IT": {"repo_id": "unsloth/gemma-3-4b-it", "description": "Gemma-3-4B-IT"},
     "SmolLM2-135M-Instruct-TaiwanChat": {"repo_id": "Luigi/SmolLM2-135M-Instruct-TaiwanChat", "description": "SmolLM2‑135M Instruct fine-tuned on TaiwanChat"},
     "SmolLM2-135M-Instruct": {"repo_id": "HuggingFaceTB/SmolLM2-135M-Instruct", "description": "Original SmolLM2‑135M Instruct"},
@@ -44,7 +44,7 @@ MODELS = {
     "Qwen2.5-3B-Instruct": {"repo_id": "Qwen/Qwen2.5-3B-Instruct", "description": "Qwen2.5-3B-Instruct"},
     "Qwen2.5-7B-Instruct": {"repo_id": "Qwen/Qwen2.5-7B-Instruct", "description": "Qwen2.5-7B-Instruct"},
     "Phi-4-mini-Reasoning": {"repo_id": "microsoft/Phi-4-mini-reasoning", "description": "Phi-4-mini-Reasoning"},
-    "Phi-4-Reasoning":      {"repo_id": "microsoft/Phi-4-reasoning",      "description": "Phi-4-Reasoning"},
+    # "Phi-4-Reasoning":      {"repo_id": "microsoft/Phi-4-reasoning",      "description": "Phi-4-Reasoning"},
     "Phi-4-mini-Instruct": {"repo_id": "microsoft/Phi-4-mini-instruct", "description": "Phi-4-mini-Instruct"},
     "Meta-Llama-3.1-8B-Instruct": {"repo_id": "MaziyarPanahi/Meta-Llama-3.1-8B-Instruct", "description": "Meta-Llama-3.1-8B-Instruct"},
     "DeepSeek-R1-Distill-Llama-8B": {"repo_id": "unsloth/DeepSeek-R1-Distill-Llama-8B", "description": "DeepSeek-R1-Distill-Llama-8B"},