cambium-llm 0.1.1

Advanced LLM architecture augmentation library - surgical model expansion with minimal compute

Cambium 🌱

Advanced LLM Architecture Augmentation Library

Expand your LLMs like nature intended - surgically, efficiently, and beautifully.

Cambium is an open-source Python library that enables surgical expansion of Large Language Models (LLMs). Named after the plant tissue responsible for secondary growth, Cambium allows developers and researchers to add new layers or architecture blocks to existing models using familiar PyTorch-like APIs.

🌱 Our Vision

We believe that experimenting with LLM architecture should not require a multi-million-dollar compute cluster.

Today, the frontier of language-model research is largely locked behind paywalls of scale—prohibitive GPU hours, massive infrastructure, and closed-source decisions made by a handful of organizations. Cambium exists to change that. We want anyone with curiosity and a laptop to be able to ask: "What if I add a new attention mechanism here?" or "What happens if I widen the hidden dimensions of layer 12?"—and actually try it, on real models, in minutes.

By making surgical model expansion lightweight, reproducible, and accessible, we hope to empower students, indie researchers, and open-source teams to push the boundaries of LLM architecture without the traditional barriers of cost and scale. Every new idea deserves a fair shot, regardless of the size of your GPU budget.

🎯 The Core Idea

Traditional: Train new 7B model from scratch (expensive 💰)
Cambium:     Expand 2B → 4B, preserve weights, train only new (efficient 🌱)

Key Benefits:

• 🔄 Preserve pretrained knowledge - Keep all original weights intact
• 📈 Modular expansion - Add capacity where needed
• 🎛️ Staged training - Progressive unfreezing strategies
• ⚡ Minimal compute - Train only new parameters initially
• 🤝 Framework compatible - Works with HF Transformers, TRL, PEFT

📦 Installation

# Basic installation
pip install cambium-llm

# With training dependencies (recommended)
pip install "cambium-llm[train]"

# Development installation
pip install "cambium-llm[dev]"

🚀 Quick Start

from cambium import ExpandableModel, InterleavedExpansion
from cambium.training import StagedTrainer
import torch

# 1. Load base model
model = ExpandableModel.from_pretrained("HuggingFaceTB/SmolLM2-135M", dtype=torch.float32)

# 2. Expand with 4 new transformer blocks
model.expand(InterleavedExpansion(num_layers=4, initialization="identity"))

# 3. Setup staged training
trainer = StagedTrainer(model)
trainer.add_phase(name="warmup", freeze="original", lr=1e-4, epochs=2)
trainer.add_phase(name="finetune", freeze="none", lr=1e-6, epochs=1)

# 4. Train
trainer.train(train_dataloader, eval_dataloader)

# 5. Save
model.save_expanded("./my-expanded-model")

📚 Expansion Strategies

1. Interleaved Block Expansion (LLaMA-Pro style)

Insert new transformer blocks between existing ones:

from cambium import InterleavedExpansion

expander = InterleavedExpansion(
    num_layers=4,
    initialization="identity",  # Near-identity init
)
model.expand(expander)

# Original: [Block0] → [Block1] → [Block2]
# Expanded: [Block0] → [New0] → [Block1] → [New1] → [Block2]

2. Width Expansion

Increase hidden dimensions:

from cambium.strategies import WidthExpansion

expander = WidthExpansion(
    hidden_dim_multiplier=1.5,  # 768 → 1152
    initialization="copy",
)
model.expand(expander)

3. Parallel Adapters

Add parallel pathways:

from cambium.strategies import ParallelAdapterExpansion

expander = ParallelAdapterExpansion(
    adapter_type="bottleneck",
    bottleneck_dim=256,
    target_layers=[20, 21, 22, 23],  # Last 4 layers
)
model.expand(expander)

4. Custom Block Expansion

Define and insert your own architecture blocks:

from cambium import CustomBlockExpansion
from cambium.blocks import CambiumBlock, SwiGLUBlock
import torch.nn as nn

# Use a template
model.expand(CustomBlockExpansion(
    block_class=SwiGLUBlock,
    num_layers=4,
    residual_connection=True,
))

# Or define your own
class MyBlock(CambiumBlock):
    required_config_keys = ["hidden_size"]

    def __init__(self, config, layer_idx=0):
        super().__init__()
        self.proj = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(self, hidden_states, **kwargs):
        return self.proj(hidden_states)

model.expand(CustomBlockExpansion(
    block_class=MyBlock,
    num_layers=2,
    residual_connection=True,
))

🎓 Training Strategies

Phase 1: Warmup New Layers

trainer.add_phase(
    name="warmup",
    freeze="original",  # Freeze all original weights
    lr=1e-4,
    epochs=2,
)

Phase 2: Progressive Unfreezing

trainer.add_phase(
    name="unfreeze",
    unfreeze_groups=[-4, -3, -2, -1],  # Last 4 layer groups
    lr=5e-5,
    epochs=1,
)

Phase 3: Full Fine-tuning

trainer.add_phase(
    name="finetune",
    freeze="none",  # Unfreeze all
    lr=1e-6,
    discriminative_lr={
        "embeddings": 1e-8,
        "original_layers": 1e-6,
        "new_layers": 1e-5,
    },
    epochs=1,
)

🔧 Supported Models

Model	Block Expansion	Width Expansion	Adapters
LLaMA 2/3	✅	✅	✅
Gemma / Gemma 3	✅	⚠️	⚠️
Mistral	✅	⚠️	⚠️
Qwen2 / Qwen3	✅	⚠️	⚠️

✅ Verified end-to-end for block expansion (InterleavedExpansion) on all listed models. ⚠️ Width expansion and adapters are currently verified only on LLaMA-family models.

More models coming soon!

📖 Documentation

• 01 - Quickstart Guide
• 02 - Interleaved Expansion
• 03 - Staged Training
• 04 - Complete Workflow
• 05 - Width Expansion
• 06 - Parallel Adapters
• 07 - Custom Blocks

🏗️ Architecture

cambium/
├── core/              # Low-level surgical operations
│   ├── expansion.py   # Model surgery engine
│   ├── initialization.py  # Smart init strategies
│   └── freezing.py    # Advanced freezing logic
├── strategies/        # Expansion strategies
│   ├── block_expansion.py
│   ├── width_expansion.py
│   ├── parallel_adapters.py
│   └── custom_expansion.py  # Custom block insertion
├── blocks/            # Custom block definitions
│   ├── base.py        # CambiumBlock ABC, ResidualWrapper
│   └── templates.py   # Pre-built blocks (SwiGLU, etc.)
├── training/          # Training utilities
│   ├── staged_trainer.py
│   └── utilities.py
├── models/            # Model wrappers
│   └── expandable.py
├── utils/             # Helper utilities
│   ├── memory.py
│   └── validation.py
└── exceptions.py      # Error classes

🧪 Testing

# Run tests
pytest tests/

# Run with coverage
pytest --cov=cambium tests/

# Run specific test file
pytest tests/unit/test_expansion.py -v

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Clone the repository
git clone https://github.com/SorawitChok/Cambium.git
cd cambium

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

📊 Comparison with Alternatives

Approach	Method	Pros	Cons
Cambium	Add new blocks	Full capacity increase	More memory
LoRA	Low-rank adapters	Minimal params	Limited capacity
IA³	Learned scaling	Preserves structure	Architecture-specific
Prefix Tuning	Learned prefixes	No model changes	Limited expressiveness
Full Fine-tune	Train all params	Best performance	Expensive

When to use Cambium:

• You need more model capacity than adapters can provide
• You want to add architectural components (new attention types, etc.)
• You have compute for training new layers but not full models
• You want progressive training strategies

📄 Citation

If you use Cambium in your research, please cite:

@software{cambium2026,
  title={Cambium: Advanced LLM Architecture Augmentation},
  author={Sorawit Chokphantavee, Sirawit Chokphantavee, and Cambium Team},
  year={2026},
  url={https://github.com/SorawitChok/Cambium}
}

📜 License

Cambium is released under the Apache License 2.0. See LICENSE for details.

🙏 Acknowledgments

Cambium builds on the excellent work of:

• Hugging Face Transformers
• PyTorch
• LLaMA-Pro (inspiration for interleaved expansion)

💬 Community

• GitHub Issues: Bug reports and feature requests
• Discussions: Q&A and general discussion

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Grow your models like nature intended 🌱