Update README metadata for Neuro project

README.md

@@ -1,13 +1,14 @@
 ---
-title: Mechinterp
-emoji: 🐨
-colorFrom: purple
-colorTo: red
+title: Neuro
+emoji: 🏃
+colorFrom: gray
+colorTo: pink
 sdk: gradio
 sdk_version: 5.13.1
 app_file: app.py
 pinned: false
 license: mit
+short_description: visualize neurons in gpt-2
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,191 @@
+# app.py
+import os
+import gradio as gr
+from transformer_lens import HookedTransformer
+from transformer_lens.utils import to_numpy
+import torch
+
+model_name = "gpt2-small"
+# Determine device based on CUDA availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = HookedTransformer.from_pretrained(
+    model_name,
+    device=device  
+)
+
+# Only print GPU info if using CUDA
+if device == "cuda":
+    print(f"Using GPU: {torch.cuda.get_device_name(0)}")
+else:
+    print("Using CPU")
+
+def get_neuron_acts(text, layer, neuron_index):
+    cache = {}
+    def caching_hook(act, hook):
+        cache["activation"] = act[0, :, neuron_index]
+    
+    model.run_with_hooks(
+        text, fwd_hooks=[(f"blocks.{layer}.mlp.hook_post", caching_hook)]
+    )
+    return to_numpy(cache["activation"])
+
+def calculate_color(val, max_val, min_val):
+    normalized_val = (val - min_val) / max_val
+    return f"rgb(240, {240*(1-normalized_val)}, {240*(1-normalized_val)})"
+
+style_string = """<style>
+    span.token {
+        border: 1px solid rgb(123, 123, 123)
+    }
+</style>"""
+
+def basic_neuron_vis(text, layer, neuron_index, max_val=None, min_val=None):
+    if layer is None:
+        return "Please select a Layer"
+    if neuron_index is None:
+        return "Please select a Neuron"
+    acts = get_neuron_acts(text, layer, neuron_index)
+    act_max = acts.max()
+    act_min = acts.min()
+    if max_val is None:
+        max_val = act_max
+    if min_val is None:
+        min_val = act_min
+    
+    htmls = [style_string]
+    htmls.append(f"<h4>Layer: <b>{layer}</b>. Neuron Index: <b>{neuron_index}</b></h4>")
+    htmls.append(f"<h4>Max Range: <b>{max_val:.4f}</b>. Min Range: <b>{min_val:.4f}</b></h4>")
+    
+    if act_max != max_val or act_min != min_val:
+        htmls.append(
+            f"<h4>Custom Range Set. Max Act: <b>{act_max:.4f}</b>. Min Act: <b>{act_min:.4f}</b></h4>"
+        )
+    
+    str_tokens = model.to_str_tokens(text)
+    for tok, act in zip(str_tokens, acts):
+        htmls.append(
+            f"<span class='token' style='background-color:{calculate_color(act, max_val, min_val)}' >{tok}</span>"
+        )
+    
+    return "".join(htmls)
+
+default_text = """The sun rises red, sets golden.
+Digits flow: 101, 202, 303—cyclic repetition.
+"Echo," whispers the shadow, "repeat, revise, reverse."
+Blue squares align in a grid: 4x4, then shift to 5x5.
+α -> β -> γ: transformations loop endlessly.
+
+If X=12, and Y=34, then Z? Calculate: Z = X² + Y².
+Strings dance: "abc", "cab", "bca"—rotational symmetry.
+Prime steps skip by: 2, 3, 5, 7, 11…
+Noise: "X...Y...Z..." patterns emerge. Silence.
+
+Fractals form: 1, 1.5, 2.25, 3.375… exponential growth.
+Colors swirl: red fades to orange, orange to yellow.
+Binary murmurs: 1010, 1100, 1110, 1001—bit-flips.
+Triangles: 1, 3, 6, 10, 15… T(n) = n(n+1)/2.
+"Reverse," whispers the wind, "invert and repeat."
+
+Nested loops:
+1 -> (2, 4) -> (8, 16) -> (32, 64)
+2 -> (3, 9) -> (27, 81) -> (243, 729).
+
+The moon glows silver, wanes to shadow.
+Patterns persist: 11, 22, 33—harmonic echoes.
+"Reshape," calls the river, "reflect, refract, renew."
+Yellow hexagons tessellate, shifting into orange octagons.
+1/3 -> 1/9 -> 1/27: recursive reduction spirals infinitely.
+
+Chords hum: A minor, C major, G7 resolve softly.
+The Fibonacci sequence: 1, 1, 2, 3, 5, 8… emerges.
+Golden spirals curl inwards, outwards, endlessly.
+Hexagons tessellate: one becomes six, becomes many.
+
+In the forest, whispers:
+A -> B -> C -> (AB), (BC), (CA).
+Axiom: F. Rule: F -> F+F-F-F+F.
+
+The tide ebbs:
+12 -> 9 -> 6 -> 3 -> 12.
+Modulo cycles: 17 -> 3, 6, 12, 1…
+
+Strange attractors pull:
+(0.1, 0.2), (0.3, 0.6), (0.5, 1.0).
+Chaos stabilizes into order, and order dissolves.
+
+Infinite regress:
+"Who am I?" asked the mirror.
+"You are the question," it answered.
+
+Numbers sing:
+e ≈ 2.7182818...
+π ≈ 3.14159...
+i² = -1: imaginary worlds collide.
+
+Recursive paradox:
+The serpent bites its tail, and time folds.
+
+Symmetry hums:
+Palindromes—"radar", "level", "madam"—appear and fade.
+Blue fades to white, white dissolves to black.
+Sequences echo: 1, 10, 100, 1000…
+"Cycle," whispers the clock, "count forward, reverse."""  # Shortened for example
+default_layer = 1
+default_neuron_index = 1
+default_max_val = 4.0
+default_min_val = 0.0
+
+def get_random_active_neuron(text, threshold=2.5):
+    # Try random layers and neurons until we find one that exceeds threshold
+    import random
+    max_attempts = 100
+    
+    for _ in range(max_attempts):
+        layer = random.randint(0, model.cfg.n_layers - 1)
+        neuron = random.randint(0, model.cfg.d_mlp - 1)
+        acts = get_neuron_acts(text, layer, neuron)
+        if acts.max() > threshold:
+            return layer, neuron
+    
+    # If no neuron found, return default values
+    return 0, 0
+
+with gr.Blocks() as demo:
+    gr.HTML(value=f"Neuroscope for {model_name}")
+    with gr.Row():
+        with gr.Column():
+            text = gr.Textbox(label="Text", value=default_text)
+            layer = gr.Number(label="Layer", value=default_layer, precision=0)
+            neuron_index = gr.Number(
+                label="Neuron Index", value=default_neuron_index, precision=0
+            )
+            random_btn = gr.Button("Find Random Active Neuron")
+            max_val = gr.Number(label="Max Value", value=default_max_val)
+            min_val = gr.Number(label="Min Value", value=default_min_val)
+            inputs = [text, layer, neuron_index, max_val, min_val]
+        with gr.Column():
+            out = gr.HTML(
+                label="Neuron Acts",
+                value=basic_neuron_vis(
+                    default_text,
+                    default_layer,
+                    default_neuron_index,
+                    default_max_val,
+                    default_min_val,
+                ),
+            )
+    
+    def random_neuron_callback(text):
+        layer_num, neuron_num = get_random_active_neuron(text)
+        return layer_num, neuron_num
+    
+    random_btn.click(
+        random_neuron_callback,
+        inputs=[text],
+        outputs=[layer, neuron_index]
+    )
+    
+    for inp in inputs:
+        inp.change(basic_neuron_vis, inputs, out)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,20 @@
+accelerate>=0.23.0
+beartype>=0.14.1
+better-abc>=0.0.3
+datasets>=2.7.1
+einops>=0.6.0
+fancy-einsum>=0.0.3
+jaxtyping>=0.2.11
+numpy>=1.24  # Assuming Python 3.9+
+pandas>=1.1.5
+rich>=12.6.0
+sentencepiece
+torch>=1.10
+tqdm>=4.64.1
+transformers>=4.41,<4.42  # Assuming Python 3.9+
+typing-extensions
+wandb>=0.13.5
+typeguard>=4.2
+pytest>=7.2.0  # From dev dependencies, needed by transformer_lens
+gradio==4.19.2  # Keeping your existing Gradio requirement
+transformer_lens==1.13.0