Engram-PEFT

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Important

This is an unofficial implementation of the DeepSeek Engram paper (arXiv:2601.07372). DeepSeek-AI official demo is here.

Engram-PEFT is a high-performance, 100% paper-aligned implementation of the DeepSeek Engram architecture. It provides a PEFT-style interface to inject conditional memory into any Transformer-based LLM, while also supporting stacked-adapter and full-finetuning workflows through explicit train_mode controls.

Engram decouples static knowledge storage from dynamic reasoning using a sparse retrieval mechanism, allowing models to scale their factual memory without increasing inference FLOPs or interfering with core logic.

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🚀 Quick Start

Installation

pip install engram-peft

To run examples or contribute to development, install the project with development dependencies:

# Using uv (recommended)
uv sync --all-groups

# Using pip
pip install -e ".[dev]"

5-Minute Example

from transformers import AutoModelForCausalLM, AutoTokenizer
from engram_peft import EngramConfig, get_engram_model

# 1. Load base model
base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")

# 2. Inject Engram layers (aligned with arXiv:2601.07372)
config = EngramConfig(target_layers=[2, 11, 20])
model = get_engram_model(
    base_model,
    config,
    tokenizer,
    train_mode="engram_only",
)

# 3. Quick check on trainable parameters
model.print_trainable_parameters()
# trainable params: ... (backbone: 0, engram: ...) || all params: ... || trainable%: ...

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📊 Performance Comparison

Method	Params Added	Speed (s/step)	Training Loss	Eval Loss	Peak Memory (JSON)
LoRA (r=16)	~2.25 M	0.2738 s	1.231	0.9890	8.07 GB
Engram-PEFT	545.4 M	0.2961 s	1.263	1.0165	9.38 GB
LoRA+Engram	~547.7 M	0.3360 s	1.214	0.9656	10.33 GB
Full Finetune+Engram	~545.4 M	0.3818 s	1.111	1.0944	15.32 GB

Tip

Performance Insight: In our latest benchmark (Test 8 & 9, TinyLlama-1.1B, 3000 steps), LoRA+Engram achieved the best convergence (lowest eval loss), outperforming standalone LoRA by ~2.3%, Engram by ~5.0%, and Full Finetune+Engram by ~12.2%. Engram-PEFT provides 240x more parameter capacity (545M) for knowledge storage with minimal latency penalty. Use LoRA+Engram to leverage both structural adaptation and high-capacity sparse memory. Full Finetune+Engram, while more memory-intensive, shows competitive performance but requires significantly more GPU resources and exhibits potential overfitting tendencies.

Loss Curve Comparison

* Engram employs sparse lookup; only a tiny fraction of parameters (approx. 1%) are active and receive gradient updates per step. For a detailed breakdown of performance, computation, and memory, see our Performance Analysis.

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🛠 Features

• 100% Paper Alignment: Implements Appendix A Table 5 parameters and the official DeepSeek gating/hashing logic.
• CPU Prefetching & Precomputation: EngramDataCollator pre-calculates multi-head hash indices on the CPU. By using num_workers > 0, these indices are prefetched in parallel with training, ensuring zero hashing overhead on the GPU.
• Tokenizer Compression: Built-in NFKC and lowercase normalization for 23% vocabulary reduction.
• Cross-Model Weight Migration: A unique feature (see weight_transfer.py) that allows migrating Engram weights between different models (e.g., Llama to Qwen) using character-level alignment on a corpus—effectively "recycling" learned knowledge.
• Zero-Invasive: Injects via forward hooks; no modification to your base model architecture required.
• Peft-like API: Familiar methods like print_trainable_parameters() and save_pretrained().
• Explicit Training Modes: train_mode="engram_only", "preserve_trainable", and "full_finetune" make backbone behavior predictable.
• Combined Training (LoRA+Engram): Support for stacking adapters. Injects LoRA for structural fine-tuning and Engram for sparse knowledge retrieval in a single model.
• Layered Optimizer Control: Configure separate optimizers for backbone, Engram dense layers, and Engram sparse embeddings.
• Named Adapters: Industry-standard named adapter management (add/set/unload) for modular knowledge packs.
• Automated Training: Native EngramTrainer with built-in sparse Adam support and automatic sync of optimizer hyperparameters.
• Flexible Layer Discovery: Recursive logic to find transformer layers regardless of PEFT wrapper nesting.

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📖 Documentation

For full details, see our documentation:

• Tutorials: Quickstart and domain knowledge injection.
• API Reference: Detailed class and function documentation.
• Paper Alignment: How we match the DeepSeek research.

Training Mode Cheat Sheet

# Engram-only PEFT
model = get_engram_model(base_model, config, tokenizer, train_mode="engram_only")

# Keep LoRA / existing trainable params
model = get_engram_model(model, config, tokenizer, train_mode="preserve_trainable")

# Full finetuning + Engram
model = get_engram_model(base_model, config, tokenizer, train_mode="full_finetune")

Layered Optimizer Example

from torch.optim import AdamW
from engram_peft import get_optimizer

optimizer = get_optimizer(
    model,
    backbone_learning_rate=5e-5,
    engram_dense_learning_rate=4e-4,
    engram_sparse_learning_rate=2e-3,
    backbone_optimizer=AdamW,
)

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🎯 Citation

If you use this implementation in your research, please cite the original DeepSeek paper:

@article{deepseek2026engram,
  title={Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models},
  author={DeepSeek-AI},
  journal={arXiv preprint arXiv:2601.07372},
  year={2026}
}

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details on our tiered development workflow (L1-L4) and testing standards.

📄 License

Apache License 2.0. See LICENSE for details.