Feature Requests

[chrishayuk]

Anything that anyone would desperately like to see in LARQL.
I maintain a ROADMAP.md for the main project and each crate but curious if there is something you'd desperately like to see

[metavacua]

Model Compression & Interpretability: Knowledge Distillation vs. LARQL's Graph-Database Paradigm

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📌 Executive Summary

Two dominant paradigms for handling large language models (LLMs):

Knowledge Distillation (KD)	LARQL's Graph-Database Paradigm
Compresses large models into smaller, efficient ones.	Treats transformers as queryable graph databases.
Focuses on efficiency (smaller, faster models).	Focuses on interpretability (explicit relationship structures).
Transfers knowledge from a teacher to a student model via probability distributions.	Decomposes model weights into nodes (residual states) and edges (information flow).
Example: DistilBERT (40% smaller, 60% faster than BERT).	Example: LARQL vindex for Gemma 3 4B (~3GB for browse-only queries).

Key Insight: They are complementary, not competing. KD optimizes for deployment efficiency, while LARQL optimizes for transparency and queryability.

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🔍 Foundational Concepts in Knowledge Distillation

1. Teacher-Student Framework

• Teacher Model: Large, high-capacity model (e.g., BERT, PaLM).
• Student Model: Smaller model trained to mimic the teacher.
• Goal: Achieve compression while retaining ~97% of performance (e.g., DistilBERT).

2. Soft Targets & Temperature Scaling

• Hard Labels: One-hot encoded (e.g., [1, 0, 0] for "cat").
• Soft Targets: Teacher’s probability distribution (e.g., [0.7, 0.2, 0.1]).
  • Preserves confidence levels and class relationships.
• Temperature Scaling ($T$):
  • Controls softness of probability distributions.
  • High $T$ → Softer distributions (more uniform probabilities).
  • Low $T$ → Harder distributions (sharper probabilities).

Distillation Loss:
$\mathcal{L}_{distill} = KL(p^t(T) | p^s(T))$

• $p^t(T)$: Teacher’s soft probabilities (scaled by $T$).
• $p^s(T)$: Student’s soft probabilities (scaled by $T$).
• Combined with standard cross-entropy loss for ground-truth labels.

3. Advanced Distillation Techniques

Technique	Description	Use Case
Attention-Based Distillation	Student learns teacher’s attention patterns.	Vision Transformers, Sequence Models
Distilling Step-by-Step	Student learns both answers and reasoning steps.	Complex reasoning tasks (e.g., math, logic)
Graph-Based Distillation	Preserves relational structures between data points.	Ranking, Metric Learning, Knowledge Graphs
Generative Model Distillation	Compresses multi-step generation (e.g., diffusion → single-step).	Text-to-speech, Image Generation
MiniLLM	Focuses student on high-probability outcomes from teacher.	Reduces mismatch between teacher and student

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⚠️ Limitations of Conventional Distillation

Challenge	Description
API Restrictions	Closed models (e.g., GPT-4, Llama) only return text, not soft probabilities.
Data Hunger	Requires large training datasets (even distilling step-by-step needs ~80% of fine-tuning data).
Black-Box Nature	No transparency into which knowledge transferred or why.
Costly Synthetic Data	Generating training data via API calls is expensive.

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🌐 LARQL: Transformers as Graph Databases

Core Idea

• Treat transformer weights as a graph database where:
  • Nodes = Residual states (intermediate representations).
  • Edges = Information flow (relationships between nodes).
• Query using LQL (Language Query Language) to inspect model internals.

Architecture

Component	Description	Example (Gemma 3 4B)
Nodes	Residual states (intermediate layer outputs).	348,160 features
Edges	Relationships between nodes (e.g., "France → capital → Paris").	512 relationship types
vindex	Memory-mappable file structure for efficient querying.	~3GB (browse-only)
Gate Vectors	Enable nearest-neighbor search on weights.	Stored in gate_vectors.bin

Advantages

✅ Interpretability: Directly query what the model learned (e.g., "Show me all relationships involving France").
✅ Queryability: Extract specific knowledge without running the full model.
✅ Efficiency: Smaller footprint for browse-only operations (e.g., 3GB vs. original model size).
✅ No Training Needed: Works on pre-trained models without distillation.

Example Query

-- Find all relationships where "France" is the subject
SELECT relationship, object
FROM model_graph
WHERE subject = "France";