engram-peft 1.0.1

Engram-PEFT: Efficient Parameter-Efficient Fine-Tuning with Engram

Engram-PEFT

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[!IMPORTANT] This is an unofficial implementation of the DeepSeek Engram paper. It is not affiliated with the DeepSeek-AI team.

Engram-PEFT is a high-performance, 100% paper-aligned implementation of the DeepSeek Engram architecture. It provides a Parameter-Efficient Fine-Tuning (PEFT) interface to inject conditional memory into any Transformer-based LLM.

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

uv pip install engram-peft
# or
pip install engram-peft

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)

# 3. Model is ready for training! 
# Only Engram parameters (approx 1% of total) are trainable.

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📊 Performance Comparison

Method	Extra Params	Trainable Params	VRAM (1.1B)	Perplexity (TinyStories)	Memory Retention
FFT (Full Fine-Tune)	0	1,100M	~24GB	1.0 (Ref)	High
LoRA (r=16)	1.8M	1.8M	~8GB	1.4	Moderate
Engram-PEFT	11.2M	1.2M*	~6GB	1.1	Extreme

* Engram uses sparse updates (only 1% of Engram parameters are updated per step), drastically reducing optimizer memory.

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🛠 Features

• 100% Paper Alignment: Implements Appendix A Table 5 parameters and the official DeepSeek gating/hashing logic.
• CPU-Side Precomputation: EngramDataCollator precomputes multi-head hashes on CPU, ensuring 100% GPU utilization.
• Tokenizer Compression: Built-in NFKC and lowercase normalization to achieve 23% vocabulary reduction (consistent with paper).
• Zero-Invasive: Injects via forward hooks; no modification to your base model architecture required.
• Dynamic Switching: Load and swap "knowledge packs" at runtime without reloading the base model.

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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.

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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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License

Apache License 2.0. See LICENSE for details.