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";

[metavacua]

After careful consideration, contemplation, and testing I have determined that the most important missing part of LARQL is a validation suite and linter.

Language models need explicit manifests.

Current round-trip extract/convert to compile is basically impossible on every architecture. The extract to vindex and compile vindex back to the same format functions as a sequent calculus like cut rule which allows us to establish more rigorously the consistency of the language model calculus.

Any differences in the round trip tells us important things for group, algebra, and category theoretical semantics.

LARQL could probably use mature language model ingestion and compilation tools from the rust ecosystem rather than rolling its own.