mamba-architecture

Mamba - Selective State Space Models

Quick start

Mamba is a state-space model architecture achieving O(n) linear complexity for sequence modeling.

Installation:

# Install causal-conv1d (optional, for efficiency)
pip install causal-conv1d>=1.4.0

# Install Mamba
pip install mamba-ssm
# Or both together
pip install mamba-ssm[causal-conv1d]

Prerequisites: Linux, NVIDIA GPU, PyTorch 1.12+, CUDA 11.6+

Basic usage (Mamba block):

import torch
from mamba_ssm import Mamba

batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")

model = Mamba(
    d_model=dim,      # Model dimension
    d_state=16,       # SSM state dimension
    d_conv=4,         # Conv1d kernel size
    expand=2          # Expansion factor
).to("cuda")

y = model(x)  # O(n) complexity!
assert y.shape == x.shape

Common workflows

Workflow 1: Language model with Mamba-2

Complete LM with generation:

from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
from mamba_ssm.models.config_mamba import MambaConfig
import torch

# Configure Mamba-2 LM
config = MambaConfig(
    d_model=1024,           # Hidden dimension
    n_layer=24,             # Number of layers
    vocab_size=50277,       # Vocabulary size
    ssm_cfg=dict(
        layer="Mamba2",     # Use Mamba-2
        d_state=128,        # Larger state for Mamba-2
        headdim=64,         # Head dimension
        ngroups=1           # Number of groups
    )
)

model = MambaLMHeadModel(config, device="cuda", dtype=torch.float16)

# Generate text
input_ids = torch.randint(0, 1000, (1, 20), device="cuda", dtype=torch.long)
output = model.generate(
    input_ids=input_ids,
    max_length=100,
    temperature=0.7,
    top_p=0.9
)

Workflow 2: Use pretrained Mamba models

Load from HuggingFace:

from transformers import AutoTokenizer
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

# Load pretrained model
model_name = "state-spaces/mamba-2.8b"
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")  # Use compatible tokenizer
model = MambaLMHeadModel.from_pretrained(model_name, device="cuda", dtype=torch.float16)

# Generate
prompt = "The future of AI is"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
output_ids = model.generate(
    input_ids=input_ids,
    max_length=200,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.2
)
generated_text = tokenizer.decode(output_ids[0])
print(generated_text)

Available models:

• state-spaces/mamba-130m
• state-spaces/mamba-370m
• state-spaces/mamba-790m
• state-spaces/mamba-1.4b
• state-spaces/mamba-2.8b

Workflow 3: Mamba-1 vs Mamba-2

Mamba-1 (smaller state):

from mamba_ssm import Mamba

model = Mamba(
    d_model=256,
    d_state=16,      # Smaller state dimension
    d_conv=4,
    expand=2
).to("cuda")

Mamba-2 (multi-head, larger state):

from mamba_ssm import Mamba2

model = Mamba2(
    d_model=256,
    d_state=128,     # Larger state dimension
    d_conv=4,
    expand=2,
    headdim=64,      # Head dimension for multi-head
    ngroups=1        # Parallel groups
).to("cuda")

Key differences:

• State size: Mamba-1 (d_state=16) vs Mamba-2 (d_state=128)
• Architecture: Mamba-2 has multi-head structure
• Normalization: Mamba-2 uses RMSNorm
• Distributed: Mamba-2 supports tensor parallelism

Workflow 4: Benchmark vs Transformers

Generation speed comparison:

# Benchmark Mamba
python benchmarks/benchmark_generation_mamba_simple.py \
  --model-name "state-spaces/mamba-2.8b" \
  --prompt "The future of machine learning is" \
  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2

# Benchmark Transformer
python benchmarks/benchmark_generation_mamba_simple.py \
  --model-name "EleutherAI/pythia-2.8b" \
  --prompt "The future of machine learning is" \
  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2

Expected results:

• Mamba: 5× faster inference
• Memory: No KV cache needed
• Scaling: Linear with sequence length

When to use vs alternatives

Use Mamba when:

• Need long sequences (100K+ tokens)
• Want faster inference than Transformers
• Memory-constrained (no KV cache)
• Building streaming applications
• Linear scaling important

Advantages:

• O(n) complexity: Linear vs quadratic
• 5× faster inference: No attention overhead
• No KV cache: Lower memory usage
• Million-token sequences: Hardware-efficient
• Streaming: Constant memory per token

Use alternatives instead:

• Transformers: Need best-in-class performance, have compute
• RWKV: Want RNN+Transformer hybrid
• RetNet: Need retention-based architecture
• Hyena: Want convolution-based approach

Common issues

Issue: CUDA out of memory

Reduce batch size or use gradient checkpointing:

model = MambaLMHeadModel(config, device="cuda", dtype=torch.float16)
model.gradient_checkpointing_enable()  # Enable checkpointing

Issue: Slow installation

Install binary wheels (not source):

pip install mamba-ssm --no-build-isolation

Issue: Missing causal-conv1d

Install separately:

pip install causal-conv1d>=1.4.0

Issue: Model not loading from HuggingFace

Use MambaLMHeadModel.from_pretrained (not AutoModel):

from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
model = MambaLMHeadModel.from_pretrained("state-spaces/mamba-2.8b")

Advanced topics

Selective SSM: See references/selective-ssm.md for mathematical formulation, state-space equations, and how selectivity enables O(n) complexity.

Mamba-2 architecture: See references/mamba2-details.md for multi-head structure, tensor parallelism, and distributed training setup.

Performance optimization: See references/performance.md for hardware-aware design, CUDA kernels, and memory efficiency techniques.

Hardware requirements

• GPU: NVIDIA with CUDA 11.6+
• VRAM:
  • 130M model: 2GB
  • 370M model: 4GB
  • 790M model: 8GB
  • 1.4B model: 14GB
  • 2.8B model: 28GB (FP16)
• Inference: 5× faster than Transformers
• Memory: No KV cache (lower than Transformers)

Performance (vs Transformers):

• Speed: 5× faster inference
• Memory: 50% less (no KV cache)
• Scaling: Linear vs quadratic

Resources

• Paper (Mamba-1): https://arxiv.org/abs/2312.00752 (Dec 2023)
• Paper (Mamba-2): https://arxiv.org/abs/2405.21060 (May 2024)
• GitHub: https://github.com/state-spaces/mamba ⭐ 13,000+
• Models: https://huggingface.co/state-spaces
• Docs: Repository README and wiki

Agent operating procedure

1. Check the environment. Check GPU type, memory and driver/CUDA versions (nvidia-smi), framework versions, and dataset location and size.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.
4. Execute the full task using the instructions and references above.
5. Validate the result. Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
CUDA out-of-memory	Reduce batch size, enable gradient accumulation/checkpointing or mixed precision, or shard the model.
Loss is NaN or diverges	Lower the learning rate, check data for invalid values, and enable gradient clipping.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never claim training results without logs; estimate compute cost before launching large jobs.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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