Update app.py

app.py

@@ -1,150 +1,1574 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import gradio as gr
+import io
+import sys
 import requests
 from bs4 import BeautifulSoup
-import io
-import os
-import base64
-import zipfile
-from PIL import Image
-from io import BytesIO
-import tempfile
-
-### ----------------------------------------------------------------
-### PART 1: "PARSED BENCHMARK RESULTS" SECTION
-### ----------------------------------------------------------------
+import gradio as gr
 
-# This text is the exact content from your "great results" output.
-# If you want to dynamically run the script again to produce the text each time,
-# you can integrate the script's logic. But here, we simply store the final output.
-PARSED_BENCHMARK_RESULTS = """\
-### RESULTS ###
----
-Model Rank: 44
-Model Name: sometimesanotion/Qwen2.5-14B-Vimarckoso-v3
-Model average score across benchmarks in %: 40.1
-Models average score on IFEval benchmarks in %: 72.57
-Models average score on BBH benchmarks in %: 48.58
-Models average score on MATH benchmarks in %: 34.44
-Models average score in GPQA benchmarks in %: 17.34
-Models average score in MUSR benchmarks in %: 19.39
-Models average score in MMLU-PRO benchmarks in %: 48.26
-###
-models:
-  - model: CultriX/SeQwence-14Bv1
-  - model: allknowingroger/Qwenslerp5-14B
-merge_method: slerp
-base_model: CultriX/SeQwence-14Bv1
-dtype: bfloat16
-parameters:
-  t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
-###
----
-Model Rank: 45
-Model Name: sthenno-com/miscii-14b-1225
-Model average score across benchmarks in %: 40.08
-Models average score on IFEval benchmarks in %: 78.78
-Models average score on BBH benchmarks in %: 50.91
-Models average score on MATH benchmarks in %: 31.57
-Models average score in GPQA benchmarks in %: 17.0
-Models average score in MUSR benchmarks in %: 14.77
-Models average score in MMLU-PRO benchmarks in %: 47.46
-###
-tokenizer_source: "base"
-chat_template: "chatml"
+# ---------------------------------------------------------
+# PART 1: FULL BENCHMARK DATA (Rank 44 through 105)
+# ---------------------------------------------------------
+# For each model, we store:
+# - rank (int)
+# - name (str)
+# - scores (dict) with keys: average, IFEval, BBH, MATH, GPQA, MUSR, MMLU-PRO
+# - known_config (dict if found, or None if no config)
 
-merge_method: ties
-dtype: bfloat16
+benchmark_data = [
+    {
+        "rank": 44,
+        "name": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3",
+        "scores": {
+            "average": 40.10,
+            "IFEval": 72.57,
+            "BBH": 48.58,
+            "MATH": 34.44,
+            "GPQA": 17.34,
+            "MUSR": 19.39,
+            "MMLU-PRO": 48.26
+        },
+        "known_config": {
+            "models": [
+                {"model": "CultriX/SeQwence-14Bv1"},
+                {"model": "allknowingroger/Qwenslerp5-14B"}
+            ],
+            "merge_method": "slerp",
+            "base_model": "CultriX/SeQwence-14Bv1",
+            "dtype": "bfloat16",
+            "parameters": {
+                "t": [0, 0.5, 1, 0.5, 0]
+            }
+        }
+    },
+    {
+        "rank": 45,
+        "name": "sthenno-com/miscii-14b-1225",
+        "scores": {
+            "average": 40.08,
+            "IFEval": 78.78,
+            "BBH": 50.91,
+            "MATH": 31.57,
+            "GPQA": 17.00,
+            "MUSR": 14.77,
+            "MMLU-PRO": 47.46
+        },
+        "known_config": {
+            "tokenizer_source": "base",
+            "chat_template": "chatml",
+            "merge_method": "ties",
+            "dtype": "bfloat16",
+            "parameters": {
+                "normalize": True
+            },
+            "base_model": "sthenno-com/miscii-14b-1028",
+            "models": [
+                {
+                    "model": "sthenno-com/miscii-14b-1028",
+                    "parameters": {
+                        "weight": 1,
+                        "density": 0.5
+                    }
+                },
+                {
+                    "model": "sthenno/miscii-1218",
+                    "parameters": {
+                        "weight": 1,
+                        "density": 0.5
+                    }
+                },
+                {
+                    "model": "sthenno/exp-002",
+                    "parameters": {
+                        "weight": 0.9,
+                        "density": 0.5
+                    }
+                },
+                {
+                    "model": "sthenno/miscii-1218",
+                    "parameters": {
+                        "weight": 0.6,
+                        "density": 0.5
+                    }
+                }
+            ]
+        }
+    },
+    {
+        "rank": 46,
+        "name": "djuna/Q2.5-Veltha-14B-0.5",
+        "scores": {
+            "average": 39.96,
+            "IFEval": 77.96,
+            "BBH": 50.32,
+            "MATH": 33.84,
+            "GPQA": 15.77,
+            "MUSR": 14.17,
+            "MMLU-PRO": 47.72
+        },
+        "known_config": {
+            "merge_method": "della_linear",
+            "dtype": "float32",
+            "out_dtype": "bfloat16",
+            "parameters": {
+                "epsilon": 0.04,
+                "lambda": 1.05,
+                "normalize": True
+            },
+            "base_model": "arcee-ai/SuperNova-Medius",
+            "tokenizer_source": "arcee-ai/SuperNova-Medius",
+            "models": [
+                {
+                    "model": "arcee-ai/SuperNova-Medius",
+                    "parameters": {
+                        "weight": 10,
+                        "density": 1
+                    }
+                },
+                {
+                    "model": "EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2",
+                    "parameters": {
+                        "weight": 7,
+                        "density": 0.5
+                    }
+                },
+                {
+                    "model": "v000000/Qwen2.5-Lumen-14B",
+                    "parameters": {
+                        "weight": 7,
+                        "density": 0.4
+                    }
+                },
+                {
+                    "model": "allura-org/TQ2.5-14B-Aletheia-v1",
+                    "parameters": {
+                        "weight": 8,
+                        "density": 0.4
+                    }
+                },
+                {
+                    "model": "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2",
+                    "parameters": {
+                        "weight": 8,
+                        "density": 0.45
+                    }
+                }
+            ]
+        }
+    },
+    {
+        "rank": 48,
+        "name": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-model_stock",
+        "scores": {
+            "average": 39.81,
+            "IFEval": 71.62,
+            "BBH": 48.76,
+            "MATH": 33.99,
+            "GPQA": 17.34,
+            "MUSR": 19.23,
+            "MMLU-PRO": 47.95
+        },
+        "known_config": None
+    },
+    {
+        "rank": 50,
+        "name": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-Prose01",
+        "scores": {
+            "average": 39.46,
+            "IFEval": 68.72,
+            "BBH": 47.71,
+            "MATH": 35.05,
+            "GPQA": 18.23,
+            "MUSR": 19.56,
+            "MMLU-PRO": 47.50
+        },
+        "known_config": None
+    },
+    {
+        "rank": 52,
+        "name": "arcee-ai/Virtuoso-Small",
+        "scores": {
+            "average": 39.43,
+            "IFEval": 79.35,
+            "BBH": 50.40,
+            "MATH": 34.29,
+            "GPQA": 11.52,
+            "MUSR": 14.44,
+            "MMLU-PRO": 46.57
+        },
+        "known_config": None
+    },
+    {
+        "rank": 54,
+        "name": "sometimesanotion/Qwentinuum-14B-v6",
+        "scores": {
+            "average": 39.23,
+            "IFEval": 63.04,
+            "BBH": 50.23,
+            "MATH": 33.84,
+            "GPQA": 18.23,
+            "MUSR": 21.18,
+            "MMLU-PRO": 48.89
+        },
+        "known_config": None
+    },
+    {
+        "rank": 55,
+        "name": "djuna/Q2.5-Veltha-14B",
+        "scores": {
+            "average": 39.21,
+            "IFEval": 82.92,
+            "BBH": 49.75,
+            "MATH": 28.02,
+            "GPQA": 14.54,
+            "MUSR": 12.26,
+            "MMLU-PRO": 47.76
+        },
+        "known_config": {
+            "merge_method": "della_linear",
+            "dtype": "float32",
+            "out_dtype": "bfloat16",
+            "parameters": {
+                "epsilon": 0.04,
+                "lambda": 1.05,
+                "normalize": True
+            },
+            "base_model": "qwen/Qwen2.5-14b",
+            "tokenizer_source": "arcee-ai/SuperNova-Medius",
+            "models": [
+                {
+                    "model": "arcee-ai/SuperNova-Medius",
+                    "parameters": {
+                        "weight": 10,
+                        "density": 1
+                    }
+                },
+                {
+                    "model": "EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2",
+                    "parameters": {
+                        "weight": 7,
+                        "density": 0.5
+                    }
+                },
+                {
+                    "model": "v000000/Qwen2.5-Lumen-14B",
+                    "parameters": {
+                        "weight": 7,
+                        "density": 0.4
+                    }
+                },
+                {
+                    "model": "allura-org/TQ2.5-14B-Aletheia-v1",
+                    "parameters": {
+                        "weight": 8,
+                        "density": 0.4
+                    }
+                },
+                {
+                    "model": "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2",
+                    "parameters": {
+                        "weight": 8,
+                        "density": 0.45
+                    }
+                }
+            ]
+        }
+    },
+    {
+        "rank": 57,
+        "name": "allknowingroger/QwenSlerp6-14B",
+        "scores": {
+            "average": 39.02,
+            "IFEval": 68.67,
+            "BBH": 47.59,
+            "MATH": 34.14,
+            "GPQA": 16.44,
+            "MUSR": 18.32,
+            "MMLU-PRO": 48.95
+        },
+        "known_config": {
+            "models": [
+                {"model": "CultriX/SeQwence-14Bv1"},
+                {"model": "allknowingroger/Qwenslerp2-14B"}
+            ],
+            "merge_method": "slerp",
+            "base_model": "CultriX/SeQwence-14Bv1",
+            "dtype": "bfloat16",
+            "parameters": {
+                "t": [0, 0.5, 1, 0.5, 0]
+            }
+        }
+    },
+    {
+        "rank": 58,
+        "name": "allknowingroger/QwenSlerp5-14B",
+        "scores": {
+            "average": 38.94,
+            "IFEval": 71.19,
+            "BBH": 47.39,
+            "MATH": 33.16,
+            "GPQA": 15.32,
+            "MUSR": 17.81,
+            "MMLU-PRO": 48.78
+        },
+        "known_config": {
+            "models": [
+                {"model": "CultriX/SeQwence-14Bv1"},
+                {"model": "CultriX/Qwestion-14B"}
+            ],
+            "merge_method": "slerp",
+            "base_model": "CultriX/SeQwence-14Bv1",
+            "dtype": "bfloat16",
+            "parameters": {
+                "t": [0, 0.5, 1, 0.5, 0]
+            }
+        }
+    },
+    {
+        "rank": 59,
+        "name": "sometimesanotion/Qwentinuum-14B-v5",
+        "scores": {
+            "average": 38.87,
+            "IFEval": 62.86,
+            "BBH": 50.28,
+            "MATH": 31.57,
+            "GPQA": 18.34,
+            "MUSR": 21.09,
+            "MMLU-PRO": 49.09
+        },
+        "known_config": None
+    },
+    {
+        "rank": 60,
+        "name": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+        "scores": {
+            "average": 38.82,
+            "IFEval": 59.90,
+            "BBH": 50.12,
+            "MATH": 34.89,
+            "GPQA": 18.46,
+            "MUSR": 21.02,
+            "MMLU-PRO": 48.56
+        },
+        "known_config": {
+            # This model had two YAML segments:
+            # We'll store them in a single dictionary with keys "config1" and "config2" to preserve them:
+            "config1": {
+                "name": "Qwenvergence-14B-v6-Prose-model_stock",
+                "merge_method": "model_stock",
+                "base_model": "Qwen/Qwen2.5-14B",
+                "tokenizer_source": "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2",
+                "parameters": {
+                    "int8_mask": True,
+                    "normalize": True,
+                    "rescale": False
+                },
+                "models": [
+                    "arcee-ai/Virtuoso-Small",
+                    "sometimesanotion/Lamarck-14B-v0.3",
+                    "EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2",
+                    "allura-org/TQ2.5-14B-Sugarquill-v1",
+                    "oxyapi/oxy-1-small",
+                    "v000000/Qwen2.5-Lumen-14B",
+                    "sthenno-com/miscii-14b-1225",
+                    "sthenno-com/miscii-14b-1225",
+                    "underwoods/medius-erebus-magnum-14b",
+                    "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2"
+                ],
+                "dtype": "float32",
+                "out_dtype": "bfloat16"
+            },
+            "config2": {
+                "name": "Qwenvergence-14B-v6-Prose",
+                "merge_method": "ties",
+                "base_model": "Qwen/Qwen2.5-14B",
+                "tokenizer_source": "base",
+                "parameters": {
+                    "density": 1.00,
+                    "weight": 1.00,
+                    "int8_mask": True,
+                    "normalize": True,
+                    "rescale": False
+                },
+                "dtype": "float32",
+                "out_dtype": "bfloat16",
+                "models": [
+                    {
+                        "model": "sometimesanotion/Qwenvergence-14B-v6-Prose-slerp",
+                        "parameters": {
+                            "density": 1.00,
+                            "weight": 1.00
+                        }
+                    }
+                ]
+            }
+        }
+    },
+    {
+        "rank": 61,
+        "name": "CultriX/Qwen2.5-14B-Brocav3",
+        "scores": {
+            "average": 38.76,
+            "IFEval": 69.52,
+            "BBH": 49.05,
+            "MATH": 32.25,
+            "GPQA": 14.54,
+            "MUSR": 19.25,
+            "MMLU-PRO": 47.97
+        },
+        "known_config": {
+            "merge_method": "della_linear",
+            "base_model": "CultriX/Qwen2.5-14B-Wernickev3",
+            "dtype": "bfloat16",
+            "parameters": {
+                "epsilon": 0.012,
+                "lambda": 1.4,
+                "normalize": True
+            },
+            "adaptive_merge_parameters": {
+                "task_weights": {
+                    "tinyArc": 1.6,
+                    "tinyHellaswag": 1.5,
+                    "tinyMMLU": 1.65,
+                    "tinyTruthfulQA": 1.9,
+                    "tinyTruthfulQA_mc1": 1.7,
+                    "tinyWinogrande": 1.75,
+                    "IFEval": 1.9,
+                    "BBH": 1.7,
+                    "MATH": 2.1,
+                    "GPQA": 1.8,
+                    "MUSR": 1.9,
+                    "MMLU-PRO": 1.8
+                },
+                "smoothing_factor": 0.1
+            },
+            "gradient_clipping": {
+                "CultriX/Qwen2.5-14B-Wernickev3": 0.86,
+                "CultriX/Qwenfinity-2.5-14B": 0.83,
+                "djuna/Q2.5-Veltha-14B-0.5": 0.91,
+                "CultriX/Qwen2.5-14B-Broca": 0.85,
+                "qingy2019/Qwen2.5-Math-14B-Instruct": 0.93,
+                "CultriX/SeQwence-14Bv1": 0.88,
+                "sometimesanotion/Qwen2.5-14B-Vimarckoso": 0.89,
+                "allknowingroger/QwenSlerp6-14B": 0.87
+            },
+            "models": [
+                {
+                    "model": "CultriX/Qwen2.5-14B-Wernickev3",
+                    "parameters": {
+                        "weight": 0.26,
+                        "density": 0.7
+                    }
+                },
+                {
+                    "model": "CultriX/Qwenfinity-2.5-14B",
+                    "parameters": {
+                        "weight": 0.23,
+                        "density": 0.65
+                    }
+                },
+                {
+                    "model": "djuna/Q2.5-Veltha-14B-0.5",
+                    "parameters": {
+                        "weight": 0.22,
+                        "density": 0.72
+                    }
+                },
+                {
+                    "model": "CultriX/Qwen2.5-14B-Broca",
+                    "parameters": {
+                        "weight": 0.15,
+                        "density": 0.65
+                    }
+                },
+                {
+                    "model": "qingy2019/Qwen2.5-Math-14B-Instruct",
+                    "parameters": {
+                        "weight": 0.18,
+                        "density": 0.73
+                    }
+                },
+                {
+                    "model": "CultriX/SeQwence-14Bv1",
+                    "parameters": {
+                        "weight": 0.14,
+                        "density": 0.63
+                    }
+                },
+                {
+                    "model": "sometimesanotion/Qwen2.5-14B-Vimarckoso",
+                    "parameters": {
+                        "weight": 0.12,
+                        "density": 0.6
+                    }
+                },
+                {
+                    "model": "allknowingroger/QwenSlerp6-14B",
+                    "parameters": {
+                        "weight": 0.1,
+                        "density": 0.62
+                    }
+                }
+            ],
+            "tokenizer_source": "CultriX/Qwen2.5-14B-Wernickev3"
+        }
+    },
+    {
+        "rank": 62,
+        "name": "sometimesanotion/Qwentinuum-14B-v7",
+        "scores": {
+            "average": 38.76,
+            "IFEval": 61.09,
+            "BBH": 50.35,
+            "MATH": 33.38,
+            "GPQA": 18.79,
+            "MUSR": 19.95,
+            "MMLU-PRO": 49.00
+        },
+        "known_config": None
+    },
+    {
+        "rank": 64,
+        "name": "sometimesanotion/Qwentinuum-14B-v3",
+        "scores": {
+            "average": 38.74,
+            "IFEval": 61.58,
+            "BBH": 50.04,
+            "MATH": 32.85,
+            "GPQA": 18.34,
+            "MUSR": 20.62,
+            "MMLU-PRO": 49.03
+        },
+        "known_config": None
+    },
+    {
+        "rank": 65,
+        "name": "allura-org/TQ2.5-14B-Aletheia-v1",
+        "scores": {
+            "average": 38.74,
+            "IFEval": 75.30,
+            "BBH": 50.88,
+            "MATH": 29.53,
+            "GPQA": 14.99,
+            "MUSR": 14.61,
+            "MMLU-PRO": 47.12
+        },
+        # The snippet had:
+        # <|im_start|>system
+        # ...
+        # This was presumably some leftover system text. We'll treat it as config, or None.
+        # We'll store it as a minimal known_config example:
+        "known_config": {
+            "system_text_example": "<|im_start|>system ... <|im_end|>"
+        }
+    },
+    {
+        "rank": 66,
+        "name": "qingy2024/Fusion4-14B-Instruct",
+        "scores": {
+            "average": 38.73,
+            "IFEval": 76.49,
+            "BBH": 50.70,
+            "MATH": 33.91,
+            "GPQA": 10.74,
+            "MUSR": 13.97,
+            "MMLU-PRO": 46.60
+        },
+        "known_config": {
+            "models": [
+                {
+                    "model": "arcee-ai/Virtuoso-Small",
+                    "parameters": {
+                        "weight": 1,
+                        "density": 1
+                    }
+                }
+            ],
+            "merge_method": "ties",
+            "base_model": "Qwen/Qwen2.5-14B",
+            "parameters": {
+                "weight": 1,
+                "density": 1,
+                "normalize": True,
+                "int8_mask": True
+            },
+            "dtype": "float16"
+        }
+    },
+    {
+        "rank": 68,
+        "name": "CultriX/Qwen2.5-14B-Brocav7",
+        "scores": {
+            "average": 38.52,
+            "IFEval": 67.24,
+            "BBH": 48.91,
+            "MATH": 31.87,
+            "GPQA": 15.66,
+            "MUSR": 20.15,
+            "MMLU-PRO": 47.31
+        },
+        "known_config": {
+            "merge_method": "della_linear",
+            "base_model": "CultriX/Qwen2.5-14B-Wernickev3",
+            "dtype": "bfloat16",
+            "parameters": {
+                "epsilon": 0.01,
+                "lambda": 1.5,
+                "normalize": True,
+                "smoothing_factor": 0.08
+            },
+            "gradient_clipping": {
+                "CultriX/Qwen2.5-14B-Wernickev3": 0.85,
+                "CultriX/Qwenfinity-2.5-14B": 0.82,
+                "djuna/Q2.5-Veltha-14B-0.5": 0.92,
+                "CultriX/Qwen2.5-14B-Broca": 0.86,
+                "qingy2019/Qwen2.5-Math-14B-Instruct": 0.94,
+                "CultriX/SeQwence-14Bv1": 0.87,
+                "sometimesanotion/Qwen2.5-14B-Vimarckoso": 0.90,
+                "allknowingroger/QwenSlerp6-14B": 0.86
+            },
+            "models": [
+                {
+                    "model": "CultriX/Qwen2.5-14B-Wernickev3",
+                    "parameters": {
+                        "weight": 0.25,
+                        "density": 0.72
+                    }
+                },
+                {
+                    "model": "CultriX/Qwenfinity-2.5-14B",
+                    "parameters": {
+                        "weight": 0.22,
+                        "density": 0.68
+                    }
+                },
+                {
+                    "model": "djuna/Q2.5-Veltha-14B-0.5",
+                    "parameters": {
+                        "weight": 0.20,
+                        "density": 0.75
+                    }
+                },
+                {
+                    "model": "CultriX/Qwen2.5-14B-Broca",
+                    "parameters": {
+                        "weight": 0.16,
+                        "density": 0.68
+                    }
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+                    "model": "qingy2019/Qwen2.5-Math-14B-Instruct",
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+                        "density": 0.75
+                    }
+                },
+                {
+                    "model": "CultriX/SeQwence-14Bv1",
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+                    }
+                },
+                {
+                    "model": "sometimesanotion/Qwen2.5-14B-Vimarckoso",
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+                        "weight": 0.11,
+                        "density": 0.62
+                    }
+                },
+                {
+                    "model": "allknowingroger/QwenSlerp6-14B",
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+                        "weight": 0.09,
+                        "density": 0.65
+                    }
+                }
+            ],
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+                    "GPQA": 1.85,
+                    "MUSR": 1.95,
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+            "tokenizer_source": "CultriX/Qwen2.5-14B-Wernickev3"
+        }
+    },
+    {
+        "rank": 71,
+        "name": "sometimesanotion/Qwentinuum-14B-v6-Prose",
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+            "IFEval": 56.43,
+            "BBH": 50.14,
+            "MATH": 35.57,
+            "GPQA": 18.46,
+            "MUSR": 21.34,
+            "MMLU-PRO": 48.80
+        },
+        "known_config": {
+            "name": "Qwentinuum-14B-v6-Prose-slerp",
+            "merge_method": "slerp",
+            "base_model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+            "tokenizer_source": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+            "dtype": "bfloat16",
+            "out_dtype": "bfloat16",
+            "parameters": {
+                "int8_mask": True,
+                "normalize": True,
+                "rescale": False
+            },
+            "slices": [
+                {
+                    "sources": [
+                        {
+                            "model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+                            "layer_range": [0, 8]
+                        },
+                        {
+                            "model": "sometimesanotion/Qwentinuum-14B-v6",
+                            "layer_range": [0, 8]
+                        }
+                    ]
+                },
+                {
+                    "sources": [
+                        {
+                            "model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+                            "layer_range": [8, 16]
+                        },
+                        {
+                            "model": "sometimesanotion/Qwentinuum-14B-v6",
+                            "layer_range": [8, 16]
+                        }
+                    ]
+                },
+                {
+                    "sources": [
+                        {
+                            "model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+                            "layer_range": [16, 24]
+                        },
+                        {
+                            "model": "sometimesanotion/Qwentinuum-14B-v6",
+                            "layer_range": [16, 24]
+                        }
+                    ]
+                },
+                {
+                    "sources": [
+                        {
+                            "model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+                            "layer_range": [24, 32]
+                        },
+                        {
+                            "model": "sometimesanotion/Qwentinuum-14B-v6",
+                            "layer_range": [24, 32]
+                        }
+                    ]
+                },
+                {
+                    "sources": [
+                        {
+                            "model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+                            "layer_range": [32, 40]
+                        },
+                        {
+                            "model": "sometimesanotion/Qwentinuum-14B-v6",
+                            "layer_range": [32, 40]
+                        }
+                    ]
+                },
+                {
+                    "sources": [
+                        {
+                            "model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+                            "layer_range": [40, 48]
+                        },
+                        {
+                            "model": "sometimesanotion/Qwentinuum-14B-v6",
+                            "layer_range": [40, 48]
+                        }
+                    ]
+                }
+            ],
+            # The 'parameters' block that includes "t: 0.40" is implied by the snippet
+        }
+    },
+    {
+        "rank": 76,
+        "name": "CultriX/Qwen2.5-14B-Brocav6",
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+            "base_model": "CultriX/Qwen2.5-14B-Wernickev3",
+            "dtype": "bfloat16",
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+                "lambda": 1.5,
+                "normalize": True
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+                    "tinyHellaswag": 1.55,
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+                    "GPQA": 1.85,
+                    "MUSR": 1.95,
+                    "MMLU-PRO": 1.85
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+            "gradient_clipping": {
+                "CultriX/Qwen2.5-14B-Wernickev3": 0.85,
+                "CultriX/Qwenfinity-2.5-14B": 0.82,
+                "djuna/Q2.5-Veltha-14B-0.5": 0.92,
+                "CultriX/Qwen2.5-14B-Broca": 0.86,
+                "qingy2019/Qwen2.5-Math-14B-Instruct": 0.94,
+                "CultriX/SeQwence-14Bv1": 0.87,
+                "sometimesanotion/Qwen2.5-14B-Vimarckoso": 0.90,
+                "allknowingroger/QwenSlerp6-14B": 0.86
+            },
+            "models": [
+                {
+                    "model": "CultriX/Qwen2.5-14B-Wernickev3",
+                    "parameters": {
+                        "weight": 0.25,
+                        "density": 0.72
+                    }
+                },
+                {
+                    "model": "CultriX/Qwenfinity-2.5-14B",
+                    "parameters": {
+                        "weight": 0.22,
+                        "density": 0.68
+                    }
+                },
+                {
+                    "model": "djuna/Q2.5-Veltha-14B-0.5",
+                    "parameters": {
+                        "weight": 0.20,
+                        "density": 0.75
+                    }
+                },
+                {
+                    "model": "CultriX/Qwen2.5-14B-Broca",
+                    "parameters": {
+                        "weight": 0.16,
+                        "density": 0.68
+                    }
+                },
+                {
+                    "model": "qingy2019/Qwen2.5-Math-14B-Instruct",
+                    "parameters": {
+                        "weight": 0.19,
+                        "density": 0.75
+                    }
+                },
+                {
+                    "model": "CultriX/SeQwence-14Bv1",
+                    "parameters": {
+                        "weight": 0.13,
+                        "density": 0.65
+                    }
+                },
+                {
+                    "model": "sometimesanotion/Qwen2.5-14B-Vimarckoso",
+                    "parameters": {
+                        "weight": 0.11,
+                        "density": 0.62
+                    }
+                },
+                {
+                    "model": "allknowingroger/QwenSlerp6-14B",
+                    "parameters": {
+                        "weight": 0.09,
+                        "density": 0.65
+                    }
+                }
+            ]
+        }
+    },
+    {
+        "rank": 80,
+        "name": "CultriX/SeQwence-14Bv1",
+        "scores": {
+            "average": 38.20,
+            "IFEval": 66.78,
+            "BBH": 47.19,
+            "MATH": 33.53,
+            "GPQA": 14.88,
+            "MUSR": 18.80,
+            "MMLU-PRO": 48.00
+        },
+        "known_config": {
+            "models": [
+                {
+                    "model": "CultriX/Qwen2.5-14B-Wernicke",
+                    "parameters": {
+                        "weight": 0.35,
+                        "density": 0.6
+                    }
+                },
+                {
+                    "model": "VAGOsolutions/SauerkrautLM-v2-14b-DPO",
+                    "parameters": {
+                        "weight": 0.30,
+                        "density": 0.6
+                    }
+                },
+                {
+                    "model": "CultriX/Qwen2.5-14B-MegaMerge-pt2",
+                    "parameters": {
+                        "weight": 0.20,
+                        "density": 0.5
+                    }
+                },
+                {
+                    "model": "CultriX/SeQwence-14B",
+                    "parameters": {
+                        "weight": 0.15,
+                        "density": 0.4
+                    }
+                },
+                {
+                    "model": "v000000/Qwen2.5-Lumen-14B",
+                    "parameters": {
+                        "weight": 0.10,
+                        "density": 0.5
+                    }
+                }
+            ],
+            "base_model": "Qwen/Qwen2.5-14B",
+            "merge_method": "dare_ties",
+            "parameters": {
+                "normalize": True,
+                "int8_mask": True
+            },
+            "dtype": "bfloat16",
+            "tokenizer_source": "Qwen/Qwen2.5-14B-Instruct"
+        }
+    },
+    {
+        "rank": 85,
+        "name": "sometimesanotion/Qwentinuum-14B-v013",
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+            "MUSR": 24.99,
+            "MMLU-PRO": 44.34
+        },
+        "known_config": {
+            "name": "Qwentinuum-14B-v013",
+            "merge_method": "model_stock",
+            "base_model": "Qwen/Qwen2.5-14B",
+            "tokenizer_source": "base",
+            "parameters": {
+                "int8_mask": True,
+                "normalize": True,
+                "rescale": False
+            },
+            "models": [
+                "sometimesanotion/Qwenvergence-14B-v3-Prose+sometimesanotion/Qwenvergence-Abliterate-512",
+                "sometimesanotion/Qwentinuum-14B-v011+sometimesanotion/Qwenvergence-Abliterate-512",
+                "sometimesanotion/Qwentinuum-14B-v012+sometimesanotion/Qwenvergence-Abliterate-256",
+                "sometimesanotion/Qwenvergence-14B-v6-Prose+sometimesanotion/Qwenvergence-Abliterate-512",
+                "sometimesanotion/Lamarck-14B-v0.3+sometimesanotion/Qwenvergence-Abliterate-512",
+                "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2"
+            ],
+            "dtype": "bfloat16",
+            "out_dtype": "bfloat16"
+        }
+    },
+    {
+        "rank": 86,
+        "name": "CultriX/Qwen2.5-14B-Wernickev3",
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+            "MATH": 32.78,
+            "GPQA": 14.99,
+            "MUSR": 18.69,
+            "MMLU-PRO": 46.13
+        },
+        "known_config": {
+            "CONFIG SuperiorMerge-14B-From-2-to-10": {
+                "models": [
+                    {
+                        "model": "VAGOsolutions/SauerkrautLM-v2-14b-DPO",
+                        "parameters": {
+                            "weight": 0.25,
+                            "density": 0.6
+                        }
+                    },
+                    {
+                        "model": "allknowingroger/QwenSlerp6-14B",
+                        "parameters": {
+                            "weight": 0.25,
+                            "density": 0.6
+                        }
+                    },
+                    {
+                        "model": "CultriX/SeQwence-14B-EvolMerge",
+                        "parameters": {
+                            "weight": 0.20,
+                            "density": 0.5
+                        }
+                    },
+                    {
+                        "model": "CultriX/Qwen2.5-14B-Wernicke",
+                        "parameters": {
+                            "weight": 0.15,
+                            "density": 0.5
+                        }
+                    },
+                    {
+                        "model": "allknowingroger/QwenStock3-14B",
+                        "parameters": {
+                            "weight": 0.15,
+                            "density": 0.5
+                        }
+                    }
+                ],
+                "base_model": "Qwen/Qwen2.5-14B",
+                "merge_method": "dare_ties",
+                "parameters": {
+                    "normalize": True,
+                    "int8_mask": True
+                },
+                "dtype": "bfloat16",
+                "tokenizer_source": "Qwen/Qwen2.5-14B-Instruct"
+            }
+        }
+    },
+    {
+        "rank": 88,
+        "name": "allknowingroger/QwenSlerp4-14B",
+        "scores": {
+            "average": 37.80,
+            "IFEval": 63.28,
+            "BBH": 49.38,
+            "MATH": 30.97,
+            "GPQA": 16.33,
+            "MUSR": 17.59,
+            "MMLU-PRO": 49.28
+        },
+        "known_config": {
+            "models": [
+                {
+                    "model": "CultriX/Qwen2.5-14B-Wernicke",
+                    "parameters": {
+                        "weight": 0.55,
+                        "density": 0.80
+                    }
+                },
+                {
+                    "model": "VAGOsolutions/SauerkrautLM-v2-14b-DPO",
+                    "parameters": {
+                        "weight": 0.20,
+                        "density": 0.60
+                    }
+                },
+                {
+                    "model": "rombodawg/Rombos-LLM-V2.6-Qwen-14b",
+                    "parameters": {
+                        "weight": 0.25,
+                        "density": 0.70
+                    }
+                },
+                {
+                    "model": "allknowingroger/Qwenslerp2-14B",
+                    "parameters": {
+                        "weight": 0.15,
+                        "density": 0.65
+                    }
+                }
+            ],
+            "base_model": "Qwen/Qwen2.5-14B",
+            "merge_method": "dare_ties",
+            "parameters": {
+                "normalize": True,
+                "int8_mask": True
+            },
+            "dtype": "bfloat16",
+            "tokenizer_source": "Qwen/Qwen2.5-14B-Instruct",
+            "adaptive_merge_parameters": {
+                "task_weights": {
+                    "IFEval": 1.0,
+                    "MATH": 1.3,
+                    "GPQA": 1.1,
+                    "MUSR": 1.2,
+                    "MMLU-PRO": 1.0
+                },
+                "smoothing_factor": 0.15
+            },
+            "gradient_clipping": 1.0
+        }
+    },
+    {
+        "rank": 89,
+        "name": "CultriX/Qwen2.5-14B-Broca",
+        "scores": {
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+            "IFEval": 56.04,
+            "BBH": 50.03,
+            "MATH": 34.59,
+            "GPQA": 18.23,
+            "MUSR": 18.95,
+            "MMLU-PRO": 48.49
+        },
+        "known_config": {
+            "merge_method": "della_linear",
+            "base_model": "CultriX/Qwen2.5-14B-Wernickev3",
+            "dtype": "bfloat16",
+            "parameters": {
+                "epsilon": 0.03,
+                "lambda": 1.1,
+                "normalize": True
+            },
+            "adaptive_merge_parameters": {
+                "task_weights": {
+                    "tinyArc": 1.3,
+                    "tinyHellaswag": 1.2,
+                    "tinyMMLU": 1.1,
+                    "tinyTruthfulQA": 1.4,
+                    "tinyWinogrande": 1.2,
+                    "IFEval": 1.3,
+                    "BBH": 1.3,
+                    "MATH": 1.4,
+                    "GPQA": 1.3,
+                    "MUSR": 1.2,
+                    "MMLU-PRO": 1.2
+                },
+                "smoothing_factor": 0.15
+            },
+            "gradient_clipping": 1.0,
+            "models": [
+                {
+                    "model": "CultriX/Qwen2.5-14B-Wernickev3",
+                    "parameters": {
+                        "weight": 0.5,
+                        "density": 0.7
+                    }
+                },
+                {
+                    "model": "djuna/Q2.5-Veltha-14B-0.5",
+                    "parameters": {
+                        "weight": 0.3,
+                        "density": 0.8
+                    }
+                },
+                {
+                    "model": "CultriX/SeQwence-14B-EvolMerge",
+                    "parameters": {
+                        "weight": 0.2,
+                        "density": 0.6
+                    }
+                }
+            ],
+            "tokenizer_source": "CultriX/Qwen2.5-14B-Wernickev3"
+        }
+    },
+    {
+        "rank": 90,
+        "name": "CultriX/Qwen2.5-14B-Emerged",
+        "scores": {
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+            "IFEval": 70.00,
+            "BBH": 45.93,
+            "MATH": 30.74,
+            "GPQA": 14.32,
+            "MUSR": 18.47,
+            "MMLU-PRO": 46.51
+        },
+        "known_config": {
+            "models": [
+                {"model": "CultriX/Qwen2.5-14B-Wernickev3"},
+                {"model": "CultriX/Qwen2.5-14B-Wernickev5"}
+            ],
+            "merge_method": "slerp",
+            "base_model": "CultriX/Qwen2.5-14B-Wernickev3",
+            "dtype": "bfloat16",
+            "parameters": {
+                "t": [0, 0.5, 1, 0.5, 0]
+            },
+            "dtype_duplicate": "bfloat16",  # The snippet repeated 'dtype' line
+            "adaptive_merge_parameters": {
+                "task_weights": {
+                    "tinyArc": 1.2,
+                    "tinyHellaswag": 1.1,
+                    "tinyMMLU": 1.2,
+                    "tinyTruthfulQA": 1.3,
+                    "tinyTruthfulQA_mc1": 1.1,
+                    "tinyWinogrande": 1.2
+                },
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+            "gradient_clipping": 1.0
+        }
+    },
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+            "MMLU-PRO": 49.02
+        },
+        "known_config": None
+    },
+    {
+        "rank": 92,
+        "name": "qingy2024/Fusion-14B-Instruct",
+        "scores": {
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+            "GPQA": 13.98,
+            "MUSR": 14.81,
+            "MMLU-PRO": 44.93
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+        "known_config": {
+            "models": [
+                {
+                    "model": "qingy2024/Qwen2.5-Math-14B-Instruct-Preview",
+                    "parameters": {
+                        "weight": 0.3,
+                        "density": 0.6
+                    }
+                },
+                {
+                    "model": "arcee-ai/Virtuoso-Small",
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+                        "weight": 0.7,
+                        "density": 0.6
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+            ],
+            "base_model": "Qwen/Qwen2.5-14B",
+            "merge_method": "dare_ties",
+            "parameters": {
+                "normalize": True,
+                "int8_mask": True
+            },
+            "dtype": "bfloat16",
+            "tokenizer_source": "Qwen/Qwen2.5-14B-Instruct"
+        }
+    },
+    {
+        "rank": 94,
+        "name": "CultriX/Qwestion-14B",
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+            "BBH": 48.76,
+            "MATH": 31.72,
+            "GPQA": 15.77,
+            "MUSR": 17.22,
+            "MMLU-PRO": 49.14
+        },
+        "known_config": {
+            "models": [
+                {
+                    "model": "CultriX/Qwen2.5-14B-Wernicke",
+                    "parameters": {
+                        "weight": 0.55,
+                        "density": 0.80
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+                },
+                {
+                    "model": "VAGOsolutions/SauerkrautLM-v2-14b-DPO",
+                    "parameters": {
+                        "weight": 0.20,
+                        "density": 0.60
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+                },
+                {
+                    "model": "rombodawg/Rombos-LLM-V2.6-Qwen-14b",
+                    "parameters": {
+                        "weight": 0.25,
+                        "density": 0.70
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+                },
+                {
+                    "model": "allknowingroger/Qwenslerp2-14B",
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+                        "weight": 0.15,
+                        "density": 0.65
+                    }
+                }
+            ],
+            "base_model": "Qwen/Qwen2.5-14B",
+            "merge_method": "dare_ties",
+            "parameters": {
+                "normalize": True,
+                "int8_mask": True
+            },
+            "dtype": "bfloat16",
+            "tokenizer_source": "Qwen/Qwen2.5-14B-Instruct",
+            "adaptive_merge_parameters": {
+                "task_weights": {
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+                    "MATH": 1.3,
+                    "GPQA": 1.1,
+                    "MUSR": 1.2,
+                    "MMLU-PRO": 1.0
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+                "smoothing_factor": 0.15
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+            "gradient_clipping": 1.0
+        }
+    },
+    {
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+        "name": "sometimesanotion/Qwenvergence-14B-v3-Prose",
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+            "IFEval": 49.18,
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+            "GPQA": 19.35,
+            "MUSR": 21.77,
+            "MMLU-PRO": 48.55
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+        "known_config": None
+    },
+    {
+        "rank": 102,
+        "name": "CultriX/SeQwence-14B-v5",
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+            "MMLU-PRO": 49.05
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+        "known_config": None
+    },
+    {
+        "rank": 103,
+        "name": "sometimesanotion/Qwen-14B-ProseStock-v4",
+        "scores": {
+            "average": 37.23,
+            "IFEval": 49.42,
+            "BBH": 49.54,
+            "MATH": 35.50,
+            "GPQA": 18.46,
+            "MUSR": 21.70,
+            "MMLU-PRO": 48.74
+        },
+        "known_config": None
+    },
+    {
+        "rank": 104,
+        "name": "sometimesanotion/IF-reasoning-experiment-40",
+        "scores": {
+            "average": 37.21,
+            "IFEval": 63.30,
+            "BBH": 44.31,
+            "MATH": 27.72,
+            "GPQA": 17.34,
+            "MUSR": 25.86,
+            "MMLU-PRO": 44.72
+        },
+        "known_config": {
+            "name": "sometimesanotion/IF-reasoning-experiment-40",
+            "merge_method": "slerp",
+            "base_model": "sometimesanotion/Qwenvergence-Abliterate",
+            "tokenizer_source": "base",
+            "dtype": "float32",
+            "out_dtype": "bfloat16",
+            "parameters": {
+                "t": [
+                    {"value": 0.40}
+                ]
+            },
+            "slices": [
+                {
+                    "sources": [
+                        {
+                            "model": "sometimesanotion/Qwenvergence-Abliterate",
+                            "layer_range": [0, 48]
+                        },
+                        {
+                            "model": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3+sometimesanotion/Qwenvergence-Abliterate-64",
+                            "layer_range": [0, 48]
+                        }
+                    ]
+                }
+            ]
+        }
+    },
+    {
+        "rank": 105,
+        "name": "CultriX/SeQwence-14B-EvolMerge",
+        "scores": {
+            "average": 37.20,
+            "IFEval": 53.82,
+            "BBH": 50.78,
+            "MATH": 31.80,
+            "GPQA": 17.45,
+            "MUSR": 20.26,
+            "MMLU-PRO": 49.10
+        },
+        "known_config": {
+            "base_model": "CultriX/SeQwence-14Bv1",
+            "dtype": "bfloat16",
+            "merge_method": "dare_ties",
+            "parameters": {
+                "int8_mask": 1.0,
+                "normalize": 1.0
+            },
+            "slices": [
+                {
+                    "sources": [
+                        {
+                            "layer_range": [0, 48],
+                            "model": "CultriX/SeQwence-14Bv1",
+                            "parameters": {
+                                "density": [
+                                    0.9723868064882017,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    0.9714039829478123
+                                ],
+                                "weight": [
+                                    0.303941801676895,
+                                    0.364404551023674,
+                                    0.315900913803921,
+                                    0.3276032249804535,
+                                    0.32167313684876814,
+                                    0.4385348686221433
+                                ]
+                            }
+                        },
+                        {
+                            "layer_range": [0, 48],
+                            "model": "CultriX/Qwestion-14B",
+                            "parameters": {
+                                "density": [
+                                    1.0,
+                                    0.9914516102369406,
+                                    1.0,
+                                    0.8035966798672015,
+                                    0.8192028457518323,
+                                    0.9514479609471497
+                                ],
+                                "weight": [
+                                    0.23754044230348376,
+                                    0.26302919982461254,
+                                    0.26313082788173275,
+                                    0.17815237275761467,
+                                    0.34301750695974753,
+                                    0.5374787613924082
+                                ]
+                            }
+                        },
+                        {
+                            "layer_range": [0, 48],
+                            "model": "CultriX/Qwen2.5-14B-Wernicke",
+                            "parameters": {
+                                "density": [
+                                    0.9250003667144193,
+                                    0.9603820599250329,
+                                    0.8766642760655986,
+                                    1.0,
+                                    0.9993615706551808,
+                                    0.7459506348277176
+                                ],
+                                "weight": [
+                                    0.48038202535582214,
+                                    0.5870170049221364,
+                                    0.27054455623315504,
+                                    0.06016442415521043,
+                                    0.4012739361231067,
+                                    0.26890177448533076
+                                ]
+                            }
+                        }
+                    ]
+                }
+            ]
+        }
+    }
+]
 
-parameters:
-  normalize: true
 
-base_model: sthenno-com/miscii-14b-1028
+# ---------------------------------------------------------
+# PART 2: PARSING LOGIC -- PRINTS OR SCRAPES
+# ---------------------------------------------------------
 
-models:
-  - model: sthenno-com/miscii-14b-1028
-    parameters:
-      weight: 1
-      density: 0.5
-  - model: sthenno/miscii-1218
-    parameters:
-      weight: 1
-      density: 0.5
-  - model: sthenno/exp-002
-    parameters:
-      weight: 0.9
-      density: 0.5
-  - model: sthenno/miscii-1218
-    parameters:
-      weight: 0.6
-      density: 0.5
-###
----
-Model Rank: 46
-Model Name: djuna/Q2.5-Veltha-14B-0.5
-Model average score across benchmarks in %: 39.96
-Models average score on IFEval benchmarks in %: 77.96
-Models average score on BBH benchmarks in %: 50.32
-Models average score on MATH benchmarks in %: 33.84
-Models average score in GPQA benchmarks in %: 15.77
-Models average score in MUSR benchmarks in %: 14.17
-Models average score in MMLU-PRO benchmarks in %: 47.72
-###
-merge_method: della_linear
-dtype: float32
-out_dtype: bfloat16
-parameters:
-  epsilon: 0.04
-  lambda: 1.05
-  normalize: true
-base_model: arcee-ai/SuperNova-Medius
-tokenizer_source: arcee-ai/SuperNova-Medius
-models:
-  - model: arcee-ai/SuperNova-Medius
-    parameters:
-      weight: 10
-      density: 1
-  - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
-    parameters:
-      weight: 7
-      density: 0.5
-  - model: v000000/Qwen2.5-Lumen-14B
-    parameters:
-      weight: 7
-      density: 0.4
-  - model: allura-org/TQ2.5-14B-Aletheia-v1
-    parameters:
-      weight: 8
-      density: 0.4
-  - model: huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
-    parameters:
-      weight: 8
-      density: 0.45
-###
----
-Model Rank: 48
-Model Name: sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-model_stock
-Model average score across benchmarks in %: 39.81
-Models average score on IFEval benchmarks in %: 71.62
-Models average score on BBH benchmarks in %: 48.76
-Models average score on MATH benchmarks in %: 33.99
-Models average score in GPQA benchmarks in %: 17.34
-Models average score in MUSR benchmarks in %: 19.23
-Models average score in MMLU-PRO benchmarks in %: 47.95
-(No MergeKit configuration found.)
+def print_benchmark_and_config_info(model_info):
+    """
+    Prints an overview (to stdout) of the benchmark scores for one model,
+    then prints its known MergeKit config if present, otherwise prints a "No config found" note.
+    """
+    print("---")
+    print(f"Model Rank: {model_info['rank']}")
+    print(f"Model Name: {model_info['name']}")
+    print(f"Model average score across benchmarks in %: {model_info['scores']['average']}")
+    print(f"Models average score on IFEval benchmarks in %: {model_info['scores']['IFEval']}")
+    print(f"Models average score on BBH benchmarks in %: {model_info['scores']['BBH']}")
+    print(f"Models average score on MATH benchmarks in %: {model_info['scores']['MATH']}")
+    print(f"Models average score in GPQA benchmarks in %: {model_info['scores']['GPQA']}")
+    print(f"Models average score in MUSR benchmarks in %: {model_info['scores']['MUSR']}")
+    print(f"Models average score in MMLU-PRO benchmarks in %: {model_info['scores']['MMLU-PRO']}")
 
-You can try the following Python script to scrape the model page:
-######################################################################
-import requests
+    if model_info["known_config"] is not None:
+        print("###")
+        # For demonstration, let's just print the dictionary in a 'rough' YAML style
+        # If you want perfect YAML, consider using pyyaml to dump it.
+        _print_dict_as_yaml(model_info["known_config"], indent_level=0)
+        print("###")
+    else:
+        print("(No MergeKit configuration found.)")
+        print("")
+        print("You can try the following Python script to scrape the model page:")
+        print("######################################################################")
+        print(
+f'''import requests
 from bs4 import BeautifulSoup
 
 def scrape_model_page(model_url):
     try:
         response = requests.get(model_url)
         if response.status_code != 200:
-            return f"Error: Unable to fetch the page (Status Code: {response.status_code})"
+            return f"Error: Unable to fetch the page (Status Code: {{response.status_code}})"
 
         soup = BeautifulSoup(response.text, "html.parser")
 
@@ -154,307 +1578,80 @@ def scrape_model_page(model_url):
         metadata_section = soup.find("div", class_="metadata")
         metadata_text = metadata_section.text.strip() if metadata_section else "No metadata found."
 
-        return {
+        return {{
             "yaml_configuration": yaml_text,
             "metadata": metadata_text
-        }
+        }}
 
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error: {{str(e)}}"
 
 if __name__ == "__main__":
-    model_url = "https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-model_stock"
+    model_url = "https://huggingface.co/{model_info['name']}"
     result = scrape_model_page(model_url)
-    print(result)
-######################################################################
----
-Model Rank: 50
-Model Name: sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-Prose01
-Model average score across benchmarks in %: 39.46
-Models average score on IFEval benchmarks in %: 68.72
-Models average score on BBH benchmarks in %: 47.71
-Models average score on MATH benchmarks in %: 35.05
-Models average score in GPQA benchmarks in %: 18.23
-Models average score in MUSR benchmarks in %: 19.56
-Models average score in MMLU-PRO benchmarks in %: 47.5
-(No MergeKit configuration found.)
-
-# ... [SNIP: The rest of your “great results” content was included in full] ...
-# (Due to character length constraints in an answer, you’d typically keep it all in one large string.)
-"""
+    print(result)'''
+        )
+        print("######################################################################")
 
 
-def view_parsed_benchmark_results():
+def _print_dict_as_yaml(data, indent_level=0):
     """
-    Simply returns the giant text block (the 'great results') 
-    so we can display it in our Gradio app.
+    Recursively prints dict 'data' as pseudo-YAML to stdout.
+    (We do it manually because the user data can be nested.)
     """
-    return PARSED_BENCHMARK_RESULTS
+    indent = "  " * indent_level
+    if isinstance(data, dict):
+        for k, v in data.items():
+            if isinstance(v, dict):
+                print(f"{indent}{k}:")
+                _print_dict_as_yaml(v, indent_level+1)
+            elif isinstance(v, list):
+                print(f"{indent}{k}:")
+                for item in v:
+                    if isinstance(item, dict):
+                        print(f"{indent}-")
+                        _print_dict_as_yaml(item, indent_level+2)
+                    else:
+                        print(f"{indent}- {item}")
+            else:
+                print(f"{indent}{k}: {v}")
+    else:
+        print(f"{indent}{data}")
 
 
-### ----------------------------------------------------------------
-### PART 2: YOUR EXISTING GRADIO CODE
-### ----------------------------------------------------------------
-
-columns = ["Model Configuration", "Model Link", "tinyArc", "tinyHellaswag", "tinyMMLU", "tinyTruthfulQA", "tinyTruthfulQA_mc1", "tinyWinogrande"]
-
-data_full = [
-    ['CultriX/Qwen2.5-14B-SLERPv7', 'https://huggingface.co/CultriX/Qwen2.5-14B-SLERPv7', 0.7205, 0.8272, 0.7541, 0.6581, 0.5, 0.729],
-    ['djuna/Q2.5-Veltha-14B-0.5', 'https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5', 0.7492, 0.8386, 0.7305, 0.598, 0.43, 0.7817],
-    ['CultriX/Qwen2.5-14B-FinalMerge', 'https://huggingface.co/CultriX/Qwen2.5-14B-FinalMerge', 0.7248, 0.8277, 0.7113, 0.7052, 0.57, 0.7001],
-    ['CultriX/Qwen2.5-14B-MultiCultyv2', 'https://huggingface.co/CultriX/Qwen2.5-14B-MultiCultyv2', 0.7295, 0.8359, 0.7363, 0.5767, 0.44, 0.7316],
-    ['CultriX/Qwen2.5-14B-Brocav7', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav7', 0.7445, 0.8353, 0.7508, 0.6292, 0.46, 0.7629],
-    ['CultriX/Qwen2.5-14B-Broca', 'https://huggingface.co/CultriX/Qwen2.5-14B-Broca', 0.7456, 0.8352, 0.748, 0.6034, 0.44, 0.7716],
-    ['CultriX/Qwen2.5-14B-Brocav3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav3', 0.7395, 0.8388, 0.7393, 0.6405, 0.47, 0.7659],
-    ['CultriX/Qwen2.5-14B-Brocav4', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav4', 0.7432, 0.8377, 0.7444, 0.6277, 0.48, 0.758],
-    ['CultriX/Qwen2.5-14B-Brocav2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav2', 0.7492, 0.8302, 0.7508, 0.6377, 0.51, 0.7478],
-    ['CultriX/Qwen2.5-14B-Brocav5', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav5', 0.7445, 0.8313, 0.7547, 0.6376, 0.5, 0.7304],
-    ['CultriX/Qwen2.5-14B-Brocav6', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav6', 0.7179, 0.8354, 0.7531, 0.6378, 0.49, 0.7524],
-    ['CultriX/Qwenfinity-2.5-14B', 'https://huggingface.co/CultriX/Qwenfinity-2.5-14B', 0.7347, 0.8254, 0.7279, 0.7267, 0.56, 0.697],
-    ['CultriX/Qwen2.5-14B-Emergedv2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Emergedv2', 0.7137, 0.8335, 0.7363, 0.5836, 0.44, 0.7344],
-    ['CultriX/Qwen2.5-14B-Unity', 'https://huggingface.co/CultriX/Qwen2.5-14B-Unity', 0.7063, 0.8343, 0.7423, 0.682, 0.57, 0.7498],
-    ['CultriX/Qwen2.5-14B-MultiCultyv3', 'https://huggingface.co/CultriX/Qwen2.5-14B-MultiCultyv3', 0.7132, 0.8216, 0.7395, 0.6792, 0.55, 0.712],
-    ['CultriX/Qwen2.5-14B-Emergedv3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Emergedv3', 0.7436, 0.8312, 0.7519, 0.6585, 0.55, 0.7068],
-    ['CultriX/SeQwence-14Bv1', 'https://huggingface.co/CultriX/SeQwence-14Bv1', 0.7278, 0.841, 0.7541, 0.6816, 0.52, 0.7539],
-    ['CultriX/Qwen2.5-14B-Wernickev2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev2', 0.7391, 0.8168, 0.7273, 0.622, 0.45, 0.7572],
-    ['CultriX/Qwen2.5-14B-Wernickev3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev3', 0.7357, 0.8148, 0.7245, 0.7023, 0.55, 0.7869],
-    ['CultriX/Qwen2.5-14B-Wernickev4', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev4', 0.7355, 0.829, 0.7497, 0.6306, 0.48, 0.7635],
-    ['CultriX/SeQwential-14B-v1', 'https://huggingface.co/CultriX/SeQwential-14B-v1', 0.7355, 0.8205, 0.7549, 0.6367, 0.48, 0.7626],
-    ['CultriX/Qwen2.5-14B-Wernickev5', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev5', 0.7224, 0.8272, 0.7541, 0.679, 0.51, 0.7578],
-    ['CultriX/Qwen2.5-14B-Wernickev6', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev6', 0.6994, 0.7549, 0.5816, 0.6991, 0.58, 0.7267],
-    ['CultriX/Qwen2.5-14B-Wernickev7', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev7', 0.7147, 0.7599, 0.6097, 0.7056, 0.57, 0.7164],
-    ['CultriX/Qwen2.5-14B-FinalMerge-tmp2', 'https://huggingface.co/CultriX/Qwen2.5-14B-FinalMerge-tmp2', 0.7255, 0.8192, 0.7535, 0.6671, 0.5, 0.7612],
-    ['CultriX/Qwen2.5-14B-BrocaV8', 'https://huggingface.co/CultriX/Qwen2.5-14B-BrocaV8', 0.7415, 0.8396, 0.7334, 0.5785, 0.4300, 0.7646],
-]
-df_full = pd.DataFrame(data_full, columns=columns)
-
-def plot_average_scores():
-    df_full["Average Score"] = df_full.iloc[:, 2:].mean(axis=1)
-    df_avg_sorted = df_full.sort_values(by="Average Score", ascending=False)
-
-    plt.figure(figsize=(14, 10))
-    plt.barh(df_avg_sorted["Model Configuration"], df_avg_sorted["Average Score"])
-    plt.title("Average Performance of Models Across Tasks", fontsize=16)
-    plt.xlabel("Average Score", fontsize=14)
-    plt.ylabel("Model Configuration", fontsize=14)
-    plt.gca().invert_yaxis()
-    plt.grid(axis='x', linestyle='--', alpha=0.7)
-    plt.tight_layout()
-    
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
-
-def plot_task_performance():
-    df_full_melted = df_full.melt(id_vars=["Model Configuration", "Model Link"], var_name="Task", value_name="Score")
-
-    plt.figure(figsize=(16, 12))
-    for model in df_full["Model Configuration"]:
-        model_data = df_full_melted[df_full_melted["Model Configuration"] == model]
-        plt.plot(model_data["Task"], model_data["Score"], marker="o", label=model)
-
-    plt.title("Performance of All Models Across Tasks", fontsize=16)
-    plt.xlabel("Task", fontsize=14)
-    plt.ylabel("Score", fontsize=14)
-    plt.xticks(rotation=45)
-    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9)
-    plt.grid(axis='y', linestyle='--', alpha=0.7)
-    plt.tight_layout()
-    
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
-
-def plot_task_specific_top_models():
-    top_models = df_full.iloc[:, 2:].idxmax()
-    top_scores = df_full.iloc[:, 2:].max()
-
-    results = pd.DataFrame({"Top Model": top_models, "Score": top_scores}).reset_index().rename(columns={"index": "Task"})
-
-    plt.figure(figsize=(14, 8))
-    plt.bar(results["Task"], results["Score"])
-    plt.title("Task-Specific Top Models", fontsize=16)
-    plt.xlabel("Task", fontsize=14)
-    plt.ylabel("Score", fontsize=14)
-    plt.grid(axis="y", linestyle="--", alpha=0.7)
-    plt.tight_layout()
-
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
-
-def plot_heatmap():
-    plt.figure(figsize=(14, 10))
-    sns.heatmap(df_full.iloc[:, 2:], annot=True, cmap="YlGnBu", 
-                xticklabels=columns[2:], yticklabels=df_full["Model Configuration"])
-    plt.title("Performance Heatmap", fontsize=16)
-    plt.tight_layout()
-    
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
-
-def scrape_mergekit_config(model_name):
-    model_link = df_full.loc[df_full["Model Configuration"] == model_name, "Model Link"].values[0]
-    response = requests.get(model_link)
-    if response.status_code != 200:
-        return f"Failed to fetch model page for {model_name}. Please check the link."
-
-    soup = BeautifulSoup(response.text, "html.parser")
-    yaml_config = soup.find("pre")  # Assume YAML is in <pre> tags
-    if yaml_config:
-        return yaml_config.text.strip()
-    return f"No YAML configuration found for {model_name}."
-
-def download_yaml(yaml_content, model_name):
-    if "No YAML configuration found" in yaml_content or "Failed to fetch model page" in yaml_content:
-        return None
-    
-    filename = f"{model_name.replace('/', '_')}_config.yaml"
-    return gr.File(value=yaml_content.encode(), filename=filename)
-
-def scrape_model_page(model_url):
-    try:
-        response = requests.get(model_url)
-        if response.status_code != 200:
-            return f"Error: Unable to fetch the page (Status Code: {response.status_code})"
-        
-        soup = BeautifulSoup(response.text, "html.parser")
-        yaml_config = soup.find("pre")
-        yaml_text = yaml_config.text.strip() if yaml_config else "No YAML configuration found."
-        metadata_section = soup.find("div", class_="metadata")
-        metadata_text = metadata_section.text.strip() if metadata_section else "No metadata found."
-        return f"**YAML Configuration:**\n{yaml_text}\n\n**Metadata:**\n{metadata_text}"
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-def display_scraped_model_data(model_url):
-    return scrape_model_page(model_url)
-
-def download_all_data():
-    csv_buffer = io.StringIO()
-    df_full.to_csv(csv_buffer, index=False)
-    csv_data = csv_buffer.getvalue().encode('utf-8')
-    
-    average_plot_pil, average_plot_name = plot_average_scores()
-    task_plot_pil, task_plot_name = plot_task_performance()
-    top_models_plot_pil, top_models_plot_name = plot_task_specific_top_models()
-    heatmap_plot_pil, heatmap_plot_name = plot_heatmap()
-
-    plot_dict = {
-        "average_performance": (average_plot_pil, average_plot_name),
-        "task_performance": (task_plot_pil, task_plot_name),
-        "top_models": (top_models_plot_pil, top_models_plot_name),
-        "heatmap": (heatmap_plot_pil, heatmap_plot_name)
-    }
-
-    zip_buffer = io.BytesIO()
-    with zipfile.ZipFile(zip_buffer, 'w') as zf:
-        zf.writestr("model_scores.csv", csv_data)
+def run_parsing_script():
+    """
+    Loops over all models in benchmark_data, calling print_benchmark_and_config_info() 
+    to generate the entire "great results" text. 
+    We capture the stdout prints, then return them as a single string.
+    """
+    old_stdout = sys.stdout
+    captured_output = io.StringIO()
+    sys.stdout = captured_output
 
-        for name, (pil_image, filename) in plot_dict.items():
-            image_bytes = io.BytesIO()
-            pil_image.save(image_bytes, format='PNG')
-            image_bytes.seek(0)
-            zf.writestr(filename, image_bytes.read())
+    for model in benchmark_data:
+        print_benchmark_and_config_info(model)
 
-        for model_name in df_full["Model Configuration"].to_list():
-            yaml_content = scrape_mergekit_config(model_name)
-            if ("No YAML configuration found" not in yaml_content) and ("Failed to fetch model page" not in yaml_content):
-                zf.writestr(f"{model_name.replace('/', '_')}_config.yaml", yaml_content.encode())
+    sys.stdout = old_stdout
+    return captured_output.getvalue()
 
-    zip_buffer.seek(0)
-    return zip_buffer, "analysis_data.zip"
 
+# ---------------------------------------------------------
+# PART 3: GRADIO APP
+# ---------------------------------------------------------
 
-### ----------------------------------------------------------------
-### PART 3: GRADIO INTERFACE
-### ----------------------------------------------------------------
+def parse_and_show_results():
+    """
+    Gradio-compatible function that runs the parser
+    and returns the captured output text.
+    """
+    return run_parsing_script()
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Comprehensive Model Performance Analysis with Hugging Face Links")
-    
-    with gr.Tab("Plots & Scraping"):
-        with gr.Row():
-            btn1 = gr.Button("Show Average Performance")
-            img1 = gr.Image(type="pil", label="Average Performance Plot")
-            img1_download = gr.File(label="Download Average Performance")
-            btn1.click(plot_average_scores, outputs=[img1,img1_download])
-            
-        with gr.Row():
-            btn2 = gr.Button("Show Task Performance")
-            img2 = gr.Image(type="pil", label="Task Performance Plot")
-            img2_download = gr.File(label="Download Task Performance")
-            btn2.click(plot_task_performance, outputs=[img2, img2_download])
-
-        with gr.Row():
-            btn3 = gr.Button("Task-Specific Top Models")
-            img3 = gr.Image(type="pil", label="Task-Specific Top Models Plot")
-            img3_download = gr.File(label="Download Top Models")
-            btn3.click(plot_task_specific_top_models, outputs=[img3, img3_download])
-        
-        with gr.Row():
-            btn4 = gr.Button("Plot Performance Heatmap")
-            heatmap_img = gr.Image(type="pil", label="Performance Heatmap")
-            heatmap_download = gr.File(label="Download Heatmap")
-            btn4.click(plot_heatmap, outputs=[heatmap_img, heatmap_download])
-
-        with gr.Row():
-            model_selector = gr.Dropdown(choices=df_full["Model Configuration"].tolist(), label="Select a Model")
-            with gr.Column():
-                scrape_btn = gr.Button("Scrape MergeKit Configuration")
-                yaml_output = gr.Textbox(lines=10, placeholder="YAML Configuration will appear here.")
-                scrape_btn.click(scrape_mergekit_config, inputs=model_selector, outputs=yaml_output)
-            with gr.Column():
-                save_yaml_btn = gr.Button("Save MergeKit Configuration")
-                yaml_download = gr.File(label="Download MergeKit Configuration")
-                save_yaml_btn.click(download_yaml, inputs=[yaml_output, model_selector], outputs=yaml_download)
-
-        with gr.Row():
-            download_all_btn = gr.Button("Download Everything")
-            all_downloads = gr.File(label="Download All Data")
-            download_all_btn.click(download_all_data, outputs=all_downloads)
-            
-        gr.Markdown("## Live Scraping Features")
-        with gr.Row():
-            url_input = gr.Textbox(label="Enter Hugging Face Model URL", placeholder="https://huggingface.co/<model>")
-            live_scrape_btn = gr.Button("Scrape Model Page")
-            live_scrape_output = gr.Textbox(label="Scraped Data", lines=15)
-            live_scrape_btn.click(display_scraped_model_data, inputs=url_input, outputs=live_scrape_output)
-
-    # NEW TAB: Show the parsed benchmark results from your big script run
-    with gr.Tab("Parsed Benchmark Results"):
-        gr.Markdown("Here is the aggregated set of benchmark scores & configurations obtained from your script:")
-        show_results_btn = gr.Button("Show Parsed Results")
-        results_box = gr.Textbox(label="Benchmark Results", lines=30)
-        
-        # When user clicks the button, show the giant text block in the textbox
-        show_results_btn.click(fn=view_parsed_benchmark_results, outputs=results_box)
+    gr.Markdown("# Full-Dataset Dynamic Benchmark Parsing")
+    gr.Markdown("Click the button below to parse all models (Rank 44 to 105) from the dataset:")
+    parse_btn = gr.Button("Parse Benchmarks")
+    results_box = gr.Textbox(label="Parsed Benchmark Results", lines=25)
+    parse_btn.click(fn=parse_and_show_results, outputs=results_box)
 
 demo.launch()