初始化项目，由ModelHub XC社区提供模型

Model: Josephgflowers/Tinyllama-1.5B-Cinder-New-Test-1-with-gguf
Source: Original Platform

calculate_least_used_layers.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import numpy as np
+
+def compute_angular_distance(a, b):
+    """Compute the angular distance between two tensors."""
+    a = a.detach().cpu().numpy().flatten()
+    b = b.detach().cpu().numpy().flatten()
+    cosine_similarity = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+    angular_distance = np.arccos(np.clip(cosine_similarity, -1.0, 1.0)) / np.pi
+    return angular_distance
+
+def get_model_hidden_states(model, tokenizer, input_text):
+    """Pass input through the model and get hidden states."""
+    inputs = tokenizer(input_text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs, output_hidden_states=True)
+    hidden_states = outputs.hidden_states
+    return hidden_states
+
+def calculate_layer_distances(hidden_states):
+    """Calculate and return angular distances between consecutive hidden states."""
+    distances = []
+    for i in range(len(hidden_states) - 1):
+        distance = compute_angular_distance(hidden_states[i], hidden_states[i+1])
+        distances.append(distance)
+    return distances
+
+def find_optimal_pruning_layer(distances):
+    """Find the optimal layer to prune based on minimum angular distance."""
+    min_distance = min(distances)
+    optimal_layer = distances.index(min_distance)
+    return optimal_layer, min_distance
+
+# Load the model and tokenizer
+model_name_or_path = "/home/joe/Downloads/checkpoint-2000-math/checkpoint-2004"  # Replace with your model path.
+print(model_name_or_path)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+
+# Define your sample input text
+input_text = "I have a reasoning math problem for you. What is 4+4*7=?"
+
+# Get model hidden states for the input text
+hidden_states = get_model_hidden_states(model, tokenizer, input_text)
+
+# Calculate angular distances for all consecutive layers
+distances = calculate_layer_distances(hidden_states)
+
+# Display angular distances
+for i, distance in enumerate(distances, start=1):
+    print(f"Layer {i} to {i+1}: Angular Distance = {distance:.4f}")
+
+# Find the optimal layer for pruning
+optimal_layer, min_distance = find_optimal_pruning_layer(distances)
+
+print(f"\nLayer Pair with Minimal Angular Distance Suggesting Redundancy: {optimal_layer + 1} to {optimal_layer + 2}")
+print(f"Minimum Angular Distance (Suggesting Redundancy): {min_distance:.4f}")
+
+
+