11 GGUF Neural Network Visualization¶
Source: notebooks/11-gguf-neural-network-graphistry-vis-executed-e1.ipynb
Notebook focus¶
This page is a cell-by-cell walkthrough of the notebook, explaining the intent of each step and showing the exact code executed.
Cell-by-cell walkthrough¶
Cell 1 (Markdown)¶
11 GGUF Neural Network Visualization¶
Use GGUF metadata and GraphistryBuilders.embedding_knn() for quick
visual exploration of model structure.
What you will learn:
- Extract metadata from a GGUF file with gguf_report()
- Build a k-nearest-neighbor graph from embedding vectors
- Visualize token embedding neighborhoods
Requirements: llamatelemetry installed. A GGUF model dataset for the report.
Cell 2 (Markdown)¶
1) Install¶
Cell 3 (Code)¶
Summary: Installs required dependencies and runtime tools.
Cell 4 (Markdown)¶
2) GGUF metadata report¶
Cell 5 (Code)¶
Summary: Imports core libraries: json, llamatelemetry. Works with GGUF models, quantization, or metadata.
import json
from llamatelemetry.api.gguf import gguf_report
model_path = "/kaggle/input/your-model/model.gguf"
report = gguf_report(model_path)
print(f"Report keys: {list(report.keys())}")
print(json.dumps(report, indent=2, default=str))
Cell 6 (Markdown)¶
3) Build an embedding kNN graph¶
embedding_knn() computes pairwise cosine distances and connects each
node to its k nearest neighbors.
Cell 7 (Code)¶
Summary: Imports core libraries: llamatelemetry, numpy. Sets up Graphistry for graph visualization or analytics.
import numpy as np
from llamatelemetry.graphistry import GraphistryBuilders
# Simulated embeddings (replace with real token embeddings)
np.random.seed(42)
embeddings = np.random.randn(20, 16).tolist()
labels = [f"token-{i}" for i in range(20)]
nodes_df, edges_df = GraphistryBuilders.embedding_knn(
embeddings,
labels=labels,
k=3,
metric="cosine",
)
print(f"Nodes: {len(nodes_df)}")
print(nodes_df.head())
print(f"\nEdges: {len(edges_df)}")
print(edges_df.head())
Cell 8 (Markdown)¶
4) Inspect neighborhood structure¶
Cell 9 (Code)¶
Summary: Executes notebook-specific logic or data processing for this step.
# Show neighbors for a specific token
token_id = "token-0"
neighbors = edges_df[edges_df["source"] == token_id]
print(f"Neighbors of {token_id}:")
print(neighbors.to_string(index=False))
Cell 10 (Markdown)¶
5) Visualize (optional)¶
Cell 11 (Code)¶
Summary: Sets up Graphistry for graph visualization or analytics.