LLM-DNA

Extract LLM DNA vectors — low-dimensional, training-free representations that capture functional behavior and evolutionary relationships between language models.

📄 Paper: LLM DNA: Tracing Model Evolution via Functional Representations (ICLR 2026 Oral)

Overview

The explosive growth of large language models has created a vast but opaque landscape: millions of models exist, yet their evolutionary relationships through fine-tuning, distillation, or adaptation are often undocumented. LLM-DNA provides a general, scalable, training-free pipeline for extracting LLM DNA — mathematically-grounded representations that satisfy inheritance and genetic determinism properties.

Key Features:

• 🧬 Extract DNA vectors from any HuggingFace or local model
• 🚀 Training-free, works across architectures and tokenizers
• 📊 Tested on 305+ LLMs with superior or competitive performance
• 🔍 Uncover undocumented relationships between models
• 🌳 Build evolutionary trees using phylogenetic algorithms

Installation

pip install llm-dna

Use llm-dna for install/package naming, and llm_dna for Python imports.

Optional extras are available for model families that need additional runtime dependencies:

# Apple Silicon / MLX-backed models
pip install "llm-dna[apple]"

# Quantized HuggingFace models (bitsandbytes, GPTQ, compressed-tensors, optimum)
pip install "llm-dna[quantization]"

# Architecture-specific model families such as Mamba or TIMM-backed models
pip install "llm-dna[model_families]"

# Everything above
pip install "llm-dna[full]"

Extra guidance:

• apple: required for MLX and mlx-community/* style model families on Apple Silicon.
• quantization: required for many GPTQ, bitsandbytes, and compressed-tensors model families.
• model_families: required for specific architectures whose modeling code depends on packages like mamba-ssm or timm.

Extraction method and defaults

The public single-model API, batch API, and CLI now use TextDNAExtractor, restoring the original experiment's text-response pipeline:

1. Use the same ordered probes for every model. The default is 100 probes each from squad,cqa,hs,wg,arc,mmlu, for 600 probes total.
2. Encode generated answers with Qwen/Qwen3-Embedding-8B and keep the first 64 coordinates of each answer. Empty answers keep their original slots as zeros.
3. Optionally L2 normalize each 64-dimensional answer embedding.
4. Concatenate all 600 slots into 38,400 dimensions and project the entire vector into 128 dimensions using a shared float32 Gaussian matrix, seed 42, with unit-length columns, exactly as in the private implementation.

There is no per-model fitted scaler, per-probe final projection, or truncation of the concatenated vector. The resulting DNA is not normalized. PCA/SVD fitting per model is rejected because it does not give models a shared coordinate system. TextDNAExtractor is the sole supported extractor. Both API and CLI default to extractor_type="text" / --extractor-type text. The old hidden-state EmbeddingDNAExtractor and its unused base classes have been removed, including their package exports. Replace explicit extractor_type="embedding" settings with "text", or omit the option to use the default.

normalize_embeddings controls the extra L2 normalization after the 64-coordinate slice, not any normalization inside the encoder or the projection matrix's column normalization. The private experiment enabled this step, while the paper's algorithm does not explicitly describe it. Both settings are available; see the normalization experiment for the measured comparison and default selection, and the source audit for implementation and paper provenance.

The provisional default is normalize_embeddings=False. Across six projection seeds, its mean F1 is 0.92434 versus 0.92313 with L2 on the original paper cohort; this small difference does not establish superiority. A separate deduplicated, pair-group-disjoint validation failed to demonstrate useful F1 for either arm. For exact reconstruction of the original saved experiment, explicitly use normalize_embeddings=True / --normalize-embeddings: all 313 saved signatures match within 2.1e-6 and the published Table 1 Qwen row is reproduced.

Generation defaults are max_length=1024, temperature=0.7, top_p=0.9, with available chat templates enabled. These settings follow the original experiment; backend, model revision, quantization, and generation randomness can still change answers. random_seed seeds probe sampling and the shared projection; it does not control provider/Hugging Face sampling. Replaying the original caches is the reproducible comparison. The 8B sentence encoder needs substantially more memory than the old MPNet default. An explicitly selected alternative encoder produces a different DNA space.

Vectors from the former public pipeline are incompatible with the corrected vectors and must be recomputed. Compare only vectors with matching encoder, preprocessing, ordered probes, aggregation, and projection. Saved metadata records these settings, the probe-order hash, and the projection hash. Response caches with a different model, probe count/order, or recorded generation settings are rejected. Legacy caches without generation settings are explicitly marked as having unknown generation provenance.

Quick Start

from llm_dna import DNAExtractionConfig, calc_dna

config = DNAExtractionConfig(
    model_name="distilgpt2",
    gpu_id=0,
    max_samples=100,
)

result = calc_dna(config)
print(f"DNA shape: {result.vector.shape}")  # (128,)

Python API

from llm_dna import DNAExtractionConfig, calc_dna

config = DNAExtractionConfig(
    model_name="Qwen/Qwen2.5-0.5B-Instruct",
    gpu_id=0,
    max_samples=100,
    dna_dim=128,
    reduction_method="random_projection",
    trust_remote_code=True,
)

result = calc_dna(config)

# DNA vector (numpy.ndarray)
vector = result.vector

# Saved paths (when save=True)
print(result.output_path)
print(result.summary_path)

CLI

# Single model
calc-dna --model-name distilgpt2 --gpus 0

# Multiple models with round-robin GPU assignment
calc-dna --llm-list ./configs/llm_list.txt --gpus 0,1

# With hyperparameters
calc-dna \
  --model-name mistralai/Mistral-7B-v0.1 \
  --dna-dim 256 \
  --max-samples 200 \
  --reduction-method random_projection \
  --load-in-8bit

Notes

• Metadata auto-fetched: Model metadata is automatically retrieved from HuggingFace Hub and cached.
• Auth token: Pass via token=... or set HF_TOKEN environment variable.
• Chat templates: Enabled when available. Disable with --no-use-chat-template or use_chat_template=False.

Offline replay and normalization ablation

With an existing checkout of the private experiment repository and its caches:

OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 \
  .venv/bin/python scripts/ablate_normalization.py \
  --private-root /home/zhaomin/project/dna \
  --output-dir experiments/normalization_20260929

The script reads original inputs without modifying them and records source hashes, cohort selection, paired metrics, and vector reconstruction errors. It performs no model generation or provider API calls. See its --help for controls and the experiment report for the exact command used.

To extract from your own raw encoder embedding cache without loading a model:

import numpy as np
from llm_dna import TextDNAExtractor

raw = np.load("ordered_response_embeddings.npy", allow_pickle=False)  # [probes, encoder_dim]
extractor = TextDNAExtractor(normalize_embeddings=True)  # original implementation
signature = extractor.extract_dna_from_embeddings(raw, model_name="my-model")

Use normalize_embeddings=False or --no-normalize-embeddings for the ablation without extra row normalization. The same cached raw embeddings and projection must be used for both settings.

Tests

# Offline tests (slow model/API integration tests are skipped)
pytest tests/ -v

# Include real model/API integration tests explicitly
pytest tests/ -v --run-slow

# Fast tests only (skip real model loading)
pytest tests/ -m "not slow"

Citation

If you use LLM-DNA in your research, please cite:

@inproceedings{wu2026llmdna,
  title={LLM DNA: Tracing Model Evolution via Functional Representations},
  author={Wu, Zhaomin and Zhao, Haodong and Wang, Ziyang and Guo, Jizhou and Wang, Qian and He, Bingsheng},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/pdf?id=UIxHaAqFqQ}
}

License

Apache 2.0