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Running
on
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Running
on
Zero
Update
Browse files- app.py +4 -1
- requirements.txt +1 -1
app.py
CHANGED
@@ -5,6 +5,7 @@ from sae_auto_interp.features.features import upsample_mask
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import torch
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from transformers import AutoTokenizer
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from PIL import Image
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CITATION_BUTTON_TEXT = """
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@misc{zhang2024largemultimodalmodelsinterpret,
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@@ -46,7 +47,7 @@ greedy_file_path = "assets/greedy.jpg"
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railway_file_path = "assets/railway.jpg"
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happy_file_path = "assets/happy.jpg"
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-
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def generate_activations(image):
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prompt = "<image>"
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inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
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@@ -116,6 +117,7 @@ def visualize_activations(image, feature_num):
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return activation_images
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def clamp_features_max(
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sae: Sae, feature: int, hooked_module: torch.nn.Module, k: float = 10
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):
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@@ -142,6 +144,7 @@ def clamp_features_max(
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return handles
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def generate_with_clamp(feature_idx, feature_strength, text, image, chat_history):
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if not isinstance(feature_idx, int):
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feature_idx = int(feature_idx)
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import torch
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from transformers import AutoTokenizer
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from PIL import Image
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import spaces
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CITATION_BUTTON_TEXT = """
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@misc{zhang2024largemultimodalmodelsinterpret,
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railway_file_path = "assets/railway.jpg"
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happy_file_path = "assets/happy.jpg"
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@spaces.GPU
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def generate_activations(image):
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prompt = "<image>"
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inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
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return activation_images
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@spaces.GPU
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def clamp_features_max(
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sae: Sae, feature: int, hooked_module: torch.nn.Module, k: float = 10
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):
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return handles
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@spaces.GPU
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def generate_with_clamp(feature_idx, feature_strength, text, image, chat_history):
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if not isinstance(feature_idx, int):
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feature_idx = int(feature_idx)
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requirements.txt
CHANGED
@@ -3,4 +3,4 @@ gradio
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sae_auto_interp @ git+https://github.com/EvolvingLMMs-Lab/multimodal-sae
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fastapi==0.112.2
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gradio==4.44.1
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-
httpx==0.
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sae_auto_interp @ git+https://github.com/EvolvingLMMs-Lab/multimodal-sae
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fastapi==0.112.2
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gradio==4.44.1
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httpx==0.24.1
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