Tutorials

Step-by-step guides to mastering Engram-PEFT for efficient LLM knowledge injection.

---

Tutorial 1: 5-Minute Quickstart

Learn how to inject Engram conditional memory into a small model like TinyLlama and train it on a toy dataset.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from engram_peft import EngramConfig, get_engram_model, EngramDataCollator, get_optimizer
from engram_peft.utils import get_optimal_precision_config

# 1. Setup
model_id = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

# 2. Configure Engram
config = EngramConfig(
    target_layers=[2, 11, 20],
    embedding_dim=1024,
    tokenizer_name_or_path=model_id
)

# 3. Inject & Freeze
base_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
model = get_engram_model(
    base_model,
    config,
    tokenizer,
    train_mode="engram_only",
)

# Quick check on overhead
model.print_trainable_parameters()

# 4. Train
collator = EngramDataCollator(tokenizer=tokenizer, config=config)

# EngramTrainer handles the MixedOptimizer and Step Decay scheduler automatically
from engram_peft import EngramTrainer
trainer = EngramTrainer(
    model=model,
    args=TrainingArguments(
        output_dir="engram_out",
        per_device_train_batch_size=4,
        learning_rate=4e-4,  # Automatically passed to MixedOptimizer
        **get_optimal_precision_config()  # Automatically handle bf16/fp16
    ),
    data_collator=collator,
    train_dataset=my_dataset
)
trainer.train()

# 5. Save ONLY the knowledge pack
model.save_pretrained("medical_knowledge_pack")

---

Tutorial 1.5: Simplified Training via CLI

For most standard use cases, you don't need to write a custom Python script. Engram-PEFT provides a high-level CLI powered by typer.

1. Generating a Configuration Template

We provide a minimal example in examples/config.yaml, but we highly recommend generating a full, documented template using the CLI to see all available options:

engram-peft config-template --output my_config.yaml

The generated file is divided into four main sections with detailed comments: - model_name_or_path: The base model identifier. - engram_config: Core hyperparameters for Engram layers. - lora_config: (Optional) PEFT LoRA settings for hybrid adaptation. - training_args: All standard transformers.TrainingArguments. - data_args: Dataset name and tokenization settings.

2. Launching Training

Trigger the training pipeline using your configuration file:

engram-peft train --config my_config.yaml

3. Automated Inference Script

Once training is complete, the CLI automatically generates a ready-to-run inference.py script inside your output_dir. You can immediately test your trained model:

# Assuming output_dir is ./outputs/tinyllama-engram
uv run python outputs/tinyllama-engram/inference.py

4. Quick Overrides

You can override any nested configuration value using dot notation without editing the YAML file:

engram-peft train --config my_config.yaml \
    --overrides "training_args.learning_rate=1e-5" \
    --overrides "engram_config.target_layers=[2,15,31]"

---

Tutorial 2: Injecting Medical Knowledge into Llama-3

Engram is specifically designed to store vast amounts of static knowledge without interfering with the model's original reasoning capabilities.

Scenario: You want to fine-tune Llama-3-8B on a large corpus of medical textbooks (PubMed).

1. Initialize with full capacity: Increase engram_vocab_size_per_ngram to handle millions of specialized medical terms.

   config = EngramConfig(
       engram_vocab_size_per_ngram=[2262400, 2262400], # Large capacity
       target_layers=[2, 8, 16, 24], # More layers for deep knowledge
       tokenizer_name_or_path="meta-llama/Meta-Llama-3-8B"
   )
   

2. Train on Domain Data: Use the EngramDataCollator to ensure high-throughput training. Engram's sparse updates allow you to train on a much larger corpus than traditional LoRA without catastrophic forgetting of the base model's logic.

---

Tutorial 3: Mixing Engram with LoRA

Engram and LoRA can be used together! LoRA is excellent for task adaptation (e.g., following instructions), while Engram is superior for knowledge storage.

The "Double Adapter" Strategy

1. Apply LoRA to the base model's Attention or MLP layers.
2. Apply Engram to the Transformer Blocks using train_mode="preserve_trainable" to keep LoRA weights trainable.
3. Result: A model that has the reasoning style of LoRA and the factual memory of Engram.

from peft import LoraConfig, get_peft_model
from engram_peft import EngramConfig, get_engram_model

# 1. Apply LoRA first
lora_config = LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"])
model = get_peft_model(base_model, lora_config)

# 2. Inject Engram on top
engram_config = EngramConfig(target_layers=[1, 14])
# IMPORTANT: preserve_trainable keeps existing LoRA parameters trainable.
model = get_engram_model(
    model,
    engram_config,
    train_mode="preserve_trainable",
)

# Now both LoRA and Engram parameters are trainable!
model.print_trainable_parameters()

[!IMPORTANT] When stacking adapters, use train_mode="preserve_trainable" so Engram keeps the requires_grad=True status of existing parameters (like LoRA weights). wrap_peft=True is still supported as a backward-compatible alias, but train_mode is the recommended API.

---

Tutorial 4: Transparent Injection & Custom Models

Engram-PEFT uses a multi-tiered strategy to find transformer layers. You can monitor this process via logs or override it for custom models.

1. Enabling Detailed Logs

By default, the library is quiet. To see exactly where and how Engram layers are being injected, enable INFO logging:

import logging
# Only show INFO for engram_peft to avoid noise from other libraries
logging.basicConfig(level=logging.WARNING)
logging.getLogger("engram_peft").setLevel(logging.INFO)

Expected Log Output:

[Engram-PEFT] Starting best-effort architecture discovery...
[Engram-PEFT] Determined layer_container_path='model.layers' (source: Architecture Registry (llama))
[Engram-PEFT] Attaching Engram layers to 32 blocks...
  - [Injected] Layer 2 -> LlamaDecoderLayer (device: cuda:0)
  - [Injected] Layer 15 -> LlamaDecoderLayer (device: cuda:0)

2. Targeting Custom Architectures

Engram-PEFT includes a built-in registry for common architectures including: - Llama-2/3, Mistral, Mixtral, Qwen2 - DeepSeek V2/V3 - Gemma/Gemma 2, Phi/Phi-3 - BERT, RoBERTa, Longformer - GPT-2, GPT-NeoX - GLM/ChatGLM

If you are using a non-standard model that isn't in our built-in registry, Engram will fall back to a heuristic (finding the largest nn.ModuleList). If this fails, you can specify the path manually:

config = EngramConfig(
    layer_container_path="my_model.transformer.h", # Explicit path
    target_layers=[0, 5, 10]
)
model = get_engram_model(base_model, config)

---

Tutorial 5: Full Finetuning with Engram

If you want to train the backbone together with Engram, use train_mode="full_finetune" and configure separate optimizer groups for backbone, Engram dense layers, and Engram sparse embeddings.

from torch.optim import AdamW
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from engram_peft import EngramConfig, EngramTrainer, get_engram_model

model_id = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForCausalLM.from_pretrained(model_id)

config = EngramConfig(
    target_layers=[2, 11],
    tokenizer_name_or_path=model_id,
)

model = get_engram_model(
    base_model,
    config,
    tokenizer,
    train_mode="full_finetune",
)

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

# Or let EngramTrainer build the layered optimizer for you
trainer = EngramTrainer(
    model=model,
    args=TrainingArguments(
        output_dir="engram_full_ft_out",
        per_device_train_batch_size=2,
        learning_rate=4e-4,
    ),
    train_dataset=my_dataset,
    optimizer_kwargs={
        "backbone_learning_rate": 5e-5,
        "engram_dense_learning_rate": 4e-4,
        "engram_sparse_learning_rate": 2e-3,
        "backbone_optimizer": AdamW,
    },
)

# Save both parts after training
model.save_pretrained("engram_adapter_only")
model.base_model.save_pretrained("engram_full_model")

[!IMPORTANT] In train_mode="full_finetune", model.save_pretrained(...) still saves only Engram weights and config. Save model.base_model to a separate directory as well if you want a restorable full-finetuned checkpoint.

Tutorial 5.5: Checkpoint Save & Resume

EngramTrainer supports mid-training checkpointing and seamless resume. Enable save_strategy in your TrainingArguments to save checkpoints automatically.

Saving checkpoints during training

from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="./output",
    save_strategy="steps",
    save_steps=500,        # Save every 500 steps
    save_total_limit=3,    # Keep at most 3 recent checkpoints
    max_steps=2000,
    ...
)

trainer = EngramTrainer(model=model, args=training_args, ...)
trainer.train()

Each checkpoint directory contains:

File	Content
adapter_model.safetensors	LoRA adapter weights (if using LoRA+Engram)
engram_adapters.safetensors	Engram hash-table weights
config.json	Engram configuration
optimizer.pt	Optimizer state (Adam momentum/variance)
scheduler.pt	Learning rate scheduler state
trainer_state.json	Step count, epoch, log history

Resuming from a checkpoint

To resume after interruption (same script restarted, or a new script):

# Recreate model from the checkpoint
from engram_peft import EngramModel

model = EngramModel.from_pretrained(
    base_model, "output/checkpoint-1000", tokenizer=tokenizer
)

trainer = EngramTrainer(model=model, args=training_args, ...)
# Auto-detect latest checkpoint, or specify a path directly
trainer.train(resume_from_checkpoint=True)

The trainer restores: - Model weights (LoRA adapter + Engram) - Optimizer state (momentum, variance — no learning rate spike) - LR scheduler position - Step / epoch counters

Training continues seamlessly from where it left off.

[!NOTE] EngramModel.from_pretrained(...) loads weights for inference without touching optimizer/scheduler state. Use resume_from_checkpoint on the trainer only when you need to continue training.

---

Tutorial 6: Managing Multiple Knowledge Packs

Engram-PEFT supports a "Named Adapter" system similar to peft. You can load multiple specialized knowledge packs into the same base model and switch between them at runtime.

# Assuming you have an engram model with 'default' knowledge
engram_model.print_trainable_parameters()

# 1. Add a second adapter for a different domain
legal_config = EngramConfig(target_layers=[2, 11, 20], embedding_dim=1024)
engram_model.add_adapter("legal", legal_config)

# 2. Switch to the new adapter for training
engram_model.set_adapter("legal")
# ... run training for legal knowledge ...

# 3. Switch back to medical knowledge
engram_model.set_adapter("default")

---

Tutorial 7: Flexible Weight Migration

Engram-PEFT allows you to reuse pre-trained knowledge even if your target model has different layers, bucket capacities, or even a different tokenizer seed.

Case A: Structural Alignment (Different Layers/Buckets)

If you have weights trained on layers [0, 1] but your new model uses layers [5, 6]:

# Map layer 0 to 5, and layer 1 to 6
model.load_weights_flexible(
    "path/to/engram_weights.pt",
    layer_mapping={0: 5, 1: 6},
    reuse_structural=False # Recommended: Re-train Gating/Conv for the new layer position
)

Case B: Logic Alignment (Different Seeds/Tokenizer)

If the hashing logic differs (e.g., a different seed was used in EngramConfig), use a reference corpus to "re-discover" the correct indices via best-effort remapping:

# corpus should be a representative sample of your training data (tokens or strings)
model.remap_from_corpus(corpus, "path/to/engram_weights.pt")

---

Tutorial 8: Performance Benchmarking & Comparison

To truly understand the benefits of Engram vs. traditional PEFT methods (like LoRA), you can use our built-in benchmarking suite.

1. Running All Methods

We provide a shortcut to run a standard suite of experiments (LoRA, Engram, LoRA+Engram, Full Finetune, etc.) in one go.

uv run python examples/compare_engram_lora.py --all --max_steps 500

2. Custom Sweeps

You can also run specific methods or perform hyperparameter sweeps (e.g., comparing different layer counts) using the --methods flag with overrides:

# Compare different layer configurations for Engram
uv run python examples/compare_engram_lora.py --methods engram:target_layers=[2,11] engram:target_layers=[11,21] 

---

Technical Highlights of our Implementations

Our templates are designed to handle the unique complexities of these next-generation architectures:

• Recursive Layer Discovery: Automatically detects transformer layers even in complex multimodal wrappers (e.g., Qwen3.5's hybrid linear/standard attention blocks).
• Multimodal Configuration Sync: Seamlessly handles nested text_config structures used in Mistral-3 and Gemma-4, ensuring correct dimensionality (e.g., 3072 vs 4096) for memory injection.
• PLE-Aware Hooks: Designed to work alongside Gemma 4's Per-Layer Embeddings (PLE) by injecting hooks at the layer boundary, capturing the optimal representation for sparse lookup.
• Thinking Mode Handling: Templates include optimized generation parameters (e.g., stop_strings) to manage reasoning outputs in Qwen and Gemma, providing clean responses after the <think> or <|think|> phase.

Running a Template

# Example: Training Qwen 3.5-4B using 4-bit quantization
uv run python examples/qwen3_engram_lora.py --load_in_4bit --max_steps 300

Refer to the Examples README for more details on each template.

Quantization Support

Engram-PEFT natively supports fine-tuning on top of models quantized via bitsandbytes (4-bit/8-bit) or GPTQ.

Core Mechanism

Since quantized models use low-precision weights (e.g., uint8 or nf4) but perform computation in a compute_dtype (typically float16 or bfloat16), Engram layers must adapt to this precision.

Engram-PEFT handles this via: 1. Smart Detection: get_engram_model automatically detects the compute_dtype of targeted layers and aligns the injected Engram layers accordingly. 2. Explicit Control: You can force a specific precision (e.g., float32 for training stability) using the engram_dtype parameter in EngramConfig.

Usage Example: 4-bit Training

This is the most common configuration for low-VRAM fine-tuning:

import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import prepare_model_for_kbit_training
from engram_peft import get_engram_model, EngramConfig

model_id = "mistralai/Mistral-7B-v0.1"

# 1. Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)

# 2. Load backbone model
base_model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto"
)

# 3. [CRITICAL] Prepare quantized model for PEFT training
base_model = prepare_model_for_kbit_training(base_model)

# 4. Inject Engram
config = EngramConfig(target_layers=[10, 20])
model = get_engram_model(base_model, config)

Combining with LoRA

You can use both LoRA and Engram to maximize parameter efficiency:

from peft import LoraConfig, get_peft_model

# 1. Apply LoRA first
lora_config = LoraConfig(
    r=16,
    target_modules=["q_proj", "v_proj"],
    task_type="CAUSAL_LM"
)
peft_model = get_peft_model(base_model, lora_config)

# 2. Apply Engram (use train_mode="preserve_trainable" to preserve LoRA's trainable state)
engram_model = get_engram_model(peft_model, config, train_mode="preserve_trainable")

Important Notes

• VRAM Usage: While the backbone is quantized, Engram embedding tables can still be large. If you hit OOM, consider reducing embedding_dim or the number of target_layers.
• Training Stability: If loss diverges in extreme quantization scenarios, try setting engram_dtype="float32" in your EngramConfig to maintain higher precision for the memory module.
• Saving/Loading: Engram adapters are saved in full precision (or BF16) and will automatically re-align to the current backbone's precision when reloaded.

---

NPU Support: Training on Huawei Ascend

Engram-PEFT fully supports training and inference on Huawei Ascend NPU hardware via the unified device backend in engram_peft.utils.device.

Prerequisites

1. Ascend CANN installed and configured (see Huawei Ascend documentation).
2. torch_npu installed:

   pip install torch-npu --index-url https://repo.huaweicloud.com/repository/pypi/simple
   

Usage

No code changes are needed — Engram-PEFT automatically detects NPU and switches all AMP settings accordingly:

from engram_peft import EngramConfig, get_engram_model

config = EngramConfig(
    target_layers=[2, 11, 20],
    engram_dtype="float16",  # NPU typically does not support bfloat16
)
model = get_engram_model(base_model, config, tokenizer)

For training with the EngramTrainer or TRL integration:

from engram_peft import EngramTrainer
from engram_peft.utils import get_optimal_precision_config

trainer = EngramTrainer(
    model=model,
    args=TrainingArguments(
        output_dir="npu_engram_out",
        per_device_train_batch_size=4,
        **get_optimal_precision_config(),  # auto-detects NPU → fp16
    ),
    data_collator=collator,
    train_dataset=dataset,
)
trainer.train()

Using with accelerate (Single Device)

Create an accelerate config that targets NPU:

accelerate config
# Choose: "This machine" → "None" (don't use DeepSpeed/FSDP) → "NO" (no distributed training)
# Then set `--mixed_precision fp16` explicitly.

Or use a config file:

compute_environment: LOCAL_MACHINE
distributed_type: 'NO'
mixed_precision: fp16
machine_rank: 0
main_process_ip: null
main_process_port: null
num_machines: 1
num_processes: 1
use_cpu: false

Distributed Training on NPU (DDP)

NPU uses HCCL (Huawei Collective Communication Library) instead of NCCL. The unified backend provides automatic detection:

import torch
import torch.distributed as dist
from engram_peft.utils.device import get_distributed_backend

backend = get_distributed_backend()  # "hccl" on NPU, "nccl" on CUDA, "" on CPU
if backend:
    dist.init_process_group(backend=backend)

When using transformers.Trainer or EngramTrainer with accelerate, you must configure the backend via environment variables or accelerate config:

# Option 1: Environment variables
export ACCELERATE_TORCH_DEVICE=npu
export NCCL_BACKEND=hccl   # If PyTorch-NPU build supports this

# Option 2: Use accelerate config for multi-NPU
accelerate config
# Choose: "This machine" → "Multi-NPU" → set backend to "hccl"

accelerate config for multi-NPU:

compute_environment: LOCAL_MACHINE
distributed_type: MULTI_NPU
machine_rank: 0
main_process_ip: null
main_process_port: null
num_machines: 1
num_processes: 8  # Number of NPU devices
mixed_precision: fp16

Distributed Training on NPU (DeepSpeed)

DeepSpeed on Ascend is supported in deepspeed>=0.12.0 (experimental). To use it:

1. Install DeepSpeed with NPU support:

   uv sync --group deepspeed  # installs deepspeed>=0.15.0
   

2. Set torch.distributed backend before trainer initialization:

   import torch.distributed as dist
   from engram_peft.utils.device import get_distributed_backend
   
   backend = get_distributed_backend()
   if backend:
       dist.init_process_group(backend=backend, init_method="env://")
   

3. Pass a standard DeepSpeed config to TrainingArguments:

   training_args = TrainingArguments(
       output_dir="npu_ds_out",
       deepspeed="ds_config.json",
       **get_optimal_precision_config(),
   )
   

Note: DeepSpeed's NPU integration is less mature than CUDA. If you encounter issues, fall back to DDP with accelerate.

Known Limitations

• bfloat16 is generally not supported on NPU — use engram_dtype="float16" explicitly.
• bitsandbytes quantization does not support NPU; 4-bit/8-bit training is not available on NPU.
• pin_memory=True in DataLoader may behave differently on NPU; set pin_memory=False if you encounter issues.
• DeepSpeed's Ascend support is experimental — validate with small-scale tests before production.

---

Tutorial 9: Seamless SFT with TRL

Engram-PEFT provides a deep integration with Hugging Face trl, including full support for sparse embeddings in instruction tuning.

Using EngramCompatibleSFTTrainer

While the standard SFTTrainer doesn't natively support sparse gradient clipping, our EngramCompatibleSFTTrainer solves this by providing custom clipping and optimization logic.

import torch
from datasets import Dataset
from trl import SFTConfig
from engram_peft import EngramConfig, get_engram_model, create_engram_sft_trainer
from engram_peft.utils import get_optimal_precision_config

# 1. Setup Model and Engram
model = ...
tokenizer = ...
config = EngramConfig(target_layers=[2, 11])
model = get_engram_model(model, config, tokenizer=tokenizer)

# 2. Configure SFT
sft_config = SFTConfig(
    output_dir="outputs/sft_results",
    learning_rate=2e-4,
    max_steps=500,
    **get_optimal_precision_config() # Optimal hardware support
)

# 3. Create & Train (factory handles everything)
trainer = create_engram_sft_trainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=my_dataset,
    args=sft_config,
)

trainer.train()

Key Advantages: * Sparse Support: use_sparse_embeddings=True (default) now works out-of-the-box in TRL. * Mixed Optimizer: Automatically uses SparseAdam for embeddings and AdamW for dense weights. * Robust Clipping: Bypasses PyTorch NotImplementedError for sparse gradients on all hardware.

---

Tutorial 10: Knowledge Memorization Benchmark (PopQA)

This tutorial walks through the examples/engram_knowledge_memory.py script — a complete end-to-end benchmark that evaluates Engram's ability to memorize long-tail factual knowledge using the PopQA dataset.

The script compares four configurations on Exact Match (EM) accuracy: - Base — no adapter (raw backbone) - +Engram — Engram adapter only - +LoRA — LoRA adapter only - +Engram+LoRA — combined

Running the Benchmark

# Train Engram only, then evaluate
python examples/engram_knowledge_memory.py --mode train

# Train Engram + LoRA, then evaluate
python examples/engram_knowledge_memory.py --mode train --train_lora

# Evaluate only (load previously saved adapters)
python examples/engram_knowledge_memory.py --mode eval \
    --engram_path outputs/popqa_benchmark/engram \
    --lora_path outputs/popqa_benchmark/lora

# Distributed training (DDP, 8 GPUs)
torchrun --nproc_per_node=8 examples/engram_knowledge_memory.py

# Distributed training (DeepSpeed ZeRO-2)
torchrun --nproc_per_node=8 examples/engram_knowledge_memory.py --use_deepspeed

What the Script Does

1. Loads PopQA — 14,267 factual QA pairs (80/20 train/test split).
2. Loads backbone in 4-bit — uses bitsandbytes NF4 quantization to fit large models (default: Qwen/Qwen3.6-27B) on a single GPU.
3. Builds Engram adapter — sparse embeddings, configurable embedding_dim and target_layers.
4. Trains with EngramTrainer — handles sparse gradients, mixed optimizer, and Engram data collator automatically.
5. (Optional) Trains LoRA adapter — uses standard peft.LoraConfig + Trainer.
6. Evaluates EM accuracy — greedy-decodes answers on held-out questions, compares against ground-truth via normalized exact match.
7. Prints comparison table — shows accuracy and delta vs. base model.

Configuration Highlights

# Key CLI arguments
--model "Qwen/Qwen3.6-27B"      # Backbone model
--embedding_dim 1280              # Engram embedding dimension
--target_layers 2 15              # Which layers to inject
--batch_size 1                    # Per-device batch size (4-bit is memory-heavy)
--grad_accum 8                    # Gradient accumulation steps
--learning_rate 2e-4              # Learning rate for Engram
--num_epochs 3                    # Training epochs
--entropy_loss_weight 0.01        # Gating entropy regularization
--lora_r 16                       # LoRA rank (if --train_lora)

Distributed Training Details

The script automatically detects distributed environment variables (LOCAL_RANK, WORLD_SIZE, RANK) set by torchrun. Key behaviors:

• DDP mode (default): DDP forces dense gradients (its gradient bucket mechanism flattens all gradients before all-reduce). nn.Embedding(sparse=True) is automatically overridden to sparse=False, and MixedOptimizer is replaced with standard AdamW. For sparse SparseAdam benefits, train on a single GPU.
• DeepSpeed mode (--use_deepspeed): Same as DDP — unsupported sparse gradients. Falls back to dense embeddings with standard AdamW.
• Device-agnostic: When running on Ascend NPU, use get_distributed_backend() from engram_peft.utils.device to detect the correct HCCL backend (see NPU Distributed Training section).