Delete benchmarks.py

benchmarks.py

@@ -1,87 +0,0 @@
-import torch
-import random
-import zlib
-
-class BenchmarkSuite:
-    def __init__(self, model, tokenizer, device="cpu", model_id="unknown"):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.device = device
-        self.model_id = model_id
-
-    def _get_deterministic_score(self, benchmark_name, min_val, max_val):
-        """
-        Generates a consistent 'fake' score based on the model name.
-        This ensures Qwen-0.6B always gets the same score, even in simulation mode.
-        """
-        # Create a seed from the model ID + benchmark name
-        seed_str = f"{self.model_id}_{benchmark_name}"
-        # Use adler32 for a consistent integer hash
-        seed_val = zlib.adler32(seed_str.encode('utf-8'))
-        random.seed(seed_val)
-        return random.uniform(min_val, max_val)
-
-    def run_benchmark(self, benchmark_name, simulation_mode=True):
-        metrics = {
-            "ARC-C": self._run_arc_c,
-            "ARC-E": self._run_arc_e,
-            "GSM8K": self._run_gsm8k,
-            "MMLU": self._run_mmlu,
-            "HellaSwag": self._run_hellaswag,
-            "PIQA": self._run_piqa,
-            "Perplexity": self._run_perplexity
-        }
-        
-        if benchmark_name in metrics:
-            return metrics[benchmark_name](simulation_mode)
-        return {"score": 0.0, "rating": "Unknown"}
-
-    def _evaluate_result(self, score, threshold_good, threshold_bad, lower_is_better=False):
-        if lower_is_better:
-            if score < threshold_good: return "Excellent 🟢"
-            if score < threshold_bad: return "Average 🟡"
-            return "Poor 🔴"
-        else:
-            if score > threshold_good: return "Excellent 🟢"
-            if score > threshold_bad: return "Average 🟡"
-            return "Poor 🔴"
-
-    # --- Benchmarks ---
-
-    def _run_perplexity(self, sim):
-        if sim:
-            # Deterministic Simulation
-            val = self._get_deterministic_score("perplexity", 8.0, 45.0)
-            return {
-                "score": val, 
-                "rating": self._evaluate_result(val, 15.0, 30.0, lower_is_better=True),
-                "unit": "PPL"
-            }
-        else:
-            # REAL Logic (from Step 1)
-            # Warning: This is slow!
-            return {"score": 25.4, "rating": "Real (Mocked)", "unit": "PPL"}
-
-    def _run_mmlu(self, sim):
-        val = self._get_deterministic_score("mmlu", 25.0, 80.0)
-        return {"score": val, "rating": self._evaluate_result(val, 60.0, 40.0), "unit": "%"}
-
-    def _run_gsm8k(self, sim):
-        val = self._get_deterministic_score("gsm8k", 10.0, 70.0)
-        return {"score": val, "rating": self._evaluate_result(val, 50.0, 25.0), "unit": "%"}
-
-    def _run_arc_c(self, sim):
-        val = self._get_deterministic_score("arc_c", 30.0, 75.0)
-        return {"score": val, "rating": self._evaluate_result(val, 60.0, 40.0), "unit": "%"}
-
-    def _run_arc_e(self, sim):
-        val = self._get_deterministic_score("arc_e", 40.0, 85.0)
-        return {"score": val, "rating": self._evaluate_result(val, 70.0, 50.0), "unit": "%"}
-
-    def _run_hellaswag(self, sim):
-        val = self._get_deterministic_score("hellaswag", 40.0, 90.0)
-        return {"score": val, "rating": self._evaluate_result(val, 75.0, 50.0), "unit": "%"}
-
-    def _run_piqa(self, sim):
-        val = self._get_deterministic_score("piqa", 50.0, 85.0)
-        return {"score": val, "rating": self._evaluate_result(val, 75.0, 60.0), "unit": "%"}