[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQ6G0JC4RSNMTPRQWEK6R4":3,"skill-same:01M6KQ6G0JC4RSNMTPRQWEK6R4":96},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":13,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":67,"install":68,"resources":81},"01M6KQ6G0JC4RSNMTPRQWEK6R4","mamba-architecture",null,"State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fmamba\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"architecture","mamba architecture","Mamba","kv cache","sequences","orchestra","with","Model Architecture","Efficient Inference","multi","state-space","long","head","token","design","models","complexity","context","model","Transformers","HuggingFace","cache","linear","state","research","2026-11-10 10:12:30",false,"---\nname: mamba-architecture\ndescription: State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Model Architecture, Mamba, State Space Models, SSM, Linear Complexity, Long Context, Efficient Inference, Hardware-Aware, Alternative To Transformers]\ndependencies: [mamba-ssm, torch, transformers, causal-conv1d]\n---\n\n# Mamba - Selective State Space Models\n\n## Quick start\n\nMamba is a state-space model architecture achieving O(n) linear complexity for sequence modeling.\n\n**Installation**:\n```bash\n# Install causal-conv1d (optional, for efficiency)\npip install causal-conv1d>=1.4.0\n\n# Install Mamba\npip install mamba-ssm\n# Or both together\npip install mamba-ssm[causal-conv1d]\n```\n\n**Prerequisites**: Linux, NVIDIA GPU, PyTorch 1.12+, CUDA 11.6+\n\n**Basic usage** (Mamba block):\n```python\nimport torch\nfrom mamba_ssm import Mamba\n\nbatch, length, dim = 2, 64, 16\nx = torch.randn(batch, length, dim).to(\"cuda\")\n\nmodel = Mamba(\n    d_model=dim,      # Model dimension\n    d_state=16,       # SSM state dimension\n    d_conv=4,         # Conv1d kernel size\n    expand=2          # Expansion factor\n).to(\"cuda\")\n\ny = model(x)  # O(n) complexity!\nassert y.shape == x.shape\n```\n\n## Common workflows\n\n### Workflow 1: Language model with Mamba-2\n\n**Complete LM with generation**:\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nfrom mamba_ssm.models.config_mamba import MambaConfig\nimport torch\n\n# Configure Mamba-2 LM\nconfig = MambaConfig(\n    d_model=1024,           # Hidden dimension\n    n_layer=24,             # Number of layers\n    vocab_size=50277,       # Vocabulary size\n    ssm_cfg=dict(\n        layer=\"Mamba2\",     # Use Mamba-2\n        d_state=128,        # Larger state for Mamba-2\n        headdim=64,         # Head dimension\n        ngroups=1           # Number of groups\n    )\n)\n\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\n\n# Generate text\ninput_ids = torch.randint(0, 1000, (1, 20), device=\"cuda\", dtype=torch.long)\noutput = model.generate(\n    input_ids=input_ids,\n    max_length=100,\n    temperature=0.7,\n    top_p=0.9\n)\n```\n\n### Workflow 2: Use pretrained Mamba models\n\n**Load from HuggingFace**:\n```python\nfrom transformers import AutoTokenizer\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n\n# Load pretrained model\nmodel_name = \"state-spaces\u002Fmamba-2.8b\"\ntokenizer = AutoTokenizer.from_pretrained(\"EleutherAI\u002Fgpt-neox-20b\")  # Use compatible tokenizer\nmodel = MambaLMHeadModel.from_pretrained(model_name, device=\"cuda\", dtype=torch.float16)\n\n# Generate\nprompt = \"The future of AI is\"\ninput_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\noutput_ids = model.generate(\n    input_ids=input_ids,\n    max_length=200,\n    temperature=0.7,\n    top_p=0.9,\n    repetition_penalty=1.2\n)\ngenerated_text = tokenizer.decode(output_ids[0])\nprint(generated_text)\n```\n\n**Available models**:\n- `state-spaces\u002Fmamba-130m`\n- `state-spaces\u002Fmamba-370m`\n- `state-spaces\u002Fmamba-790m`\n- `state-spaces\u002Fmamba-1.4b`\n- `state-spaces\u002Fmamba-2.8b`\n\n### Workflow 3: Mamba-1 vs Mamba-2\n\n**Mamba-1** (smaller state):\n```python\nfrom mamba_ssm import Mamba\n\nmodel = Mamba(\n    d_model=256,\n    d_state=16,      # Smaller state dimension\n    d_conv=4,\n    expand=2\n).to(\"cuda\")\n```\n\n**Mamba-2** (multi-head, larger state):\n```python\nfrom mamba_ssm import Mamba2\n\nmodel = Mamba2(\n    d_model=256,\n    d_state=128,     # Larger state dimension\n    d_conv=4,\n    expand=2,\n    headdim=64,      # Head dimension for multi-head\n    ngroups=1        # Parallel groups\n).to(\"cuda\")\n```\n\n**Key differences**:\n- **State size**: Mamba-1 (d_state=16) vs Mamba-2 (d_state=128)\n- **Architecture**: Mamba-2 has multi-head structure\n- **Normalization**: Mamba-2 uses RMSNorm\n- **Distributed**: Mamba-2 supports tensor parallelism\n\n### Workflow 4: Benchmark vs Transformers\n\n**Generation speed comparison**:\n```bash\n# Benchmark Mamba\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"state-spaces\u002Fmamba-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n\n# Benchmark Transformer\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"EleutherAI\u002Fpythia-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n```\n\n**Expected results**:\n- **Mamba**: 5× faster inference\n- **Memory**: No KV cache needed\n- **Scaling**: Linear with sequence length\n\n## When to use vs alternatives\n\n**Use Mamba when**:\n- Need long sequences (100K+ tokens)\n- Want faster inference than Transformers\n- Memory-constrained (no KV cache)\n- Building streaming applications\n- Linear scaling important\n\n**Advantages**:\n- **O(n) complexity**: Linear vs quadratic\n- **5× faster inference**: No attention overhead\n- **No KV cache**: Lower memory usage\n- **Million-token sequences**: Hardware-efficient\n- **Streaming**: Constant memory per token\n\n**Use alternatives instead**:\n- **Transformers**: Need best-in-class performance, have compute\n- **RWKV**: Want RNN+Transformer hybrid\n- **RetNet**: Need retention-based architecture\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: CUDA out of memory**\n\nReduce batch size or use gradient checkpointing:\n```python\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\nmodel.gradient_checkpointing_enable()  # Enable checkpointing\n```\n\n**Issue: Slow installation**\n\nInstall binary wheels (not source):\n```bash\npip install mamba-ssm --no-build-isolation\n```\n\n**Issue: Missing causal-conv1d**\n\nInstall separately:\n```bash\npip install causal-conv1d>=1.4.0\n```\n\n**Issue: Model not loading from HuggingFace**\n\nUse `MambaLMHeadModel.from_pretrained` (not `AutoModel`):\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nmodel = MambaLMHeadModel.from_pretrained(\"state-spaces\u002Fmamba-2.8b\")\n```\n\n## Advanced topics\n\n**Selective SSM**: See [references\u002Fselective-ssm.md](references\u002Fselective-ssm.md) for mathematical formulation, state-space equations, and how selectivity enables O(n) complexity.\n\n**Mamba-2 architecture**: See [references\u002Fmamba2-details.md](references\u002Fmamba2-details.md) for multi-head structure, tensor parallelism, and distributed training setup.\n\n**Performance optimization**: See [references\u002Fperformance.md](references\u002Fperformance.md) for hardware-aware design, CUDA kernels, and memory efficiency techniques.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA with CUDA 11.6+\n- **VRAM**:\n  - 130M model: 2GB\n  - 370M model: 4GB\n  - 790M model: 8GB\n  - 1.4B model: 14GB\n  - 2.8B model: 28GB (FP16)\n- **Inference**: 5× faster than Transformers\n- **Memory**: No KV cache (lower than Transformers)\n\n**Performance** (vs Transformers):\n- **Speed**: 5× faster inference\n- **Memory**: 50% less (no KV cache)\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (Mamba-1): https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.00752 (Dec 2023)\n- Paper (Mamba-2): https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.21060 (May 2024)\n- GitHub: https:\u002F\u002Fgithub.com\u002Fstate-spaces\u002Fmamba ⭐ 13,000+\n- Models: https:\u002F\u002Fhuggingface.co\u002Fstate-spaces\n- Docs: Repository README and wiki\n\n\n","# Mamba - Selective State Space Models\n\n## Quick start\n\nMamba is a state-space model architecture achieving O(n) linear complexity for sequence modeling.\n\n**Installation**:\n```bash\n# Install causal-conv1d (optional, for efficiency)\npip install causal-conv1d>=1.4.0\n\n# Install Mamba\npip install mamba-ssm\n# Or both together\npip install mamba-ssm[causal-conv1d]\n```\n\n**Prerequisites**: Linux, NVIDIA GPU, PyTorch 1.12+, CUDA 11.6+\n\n**Basic usage** (Mamba block):\n```python\nimport torch\nfrom mamba_ssm import Mamba\n\nbatch, length, dim = 2, 64, 16\nx = torch.randn(batch, length, dim).to(\"cuda\")\n\nmodel = Mamba(\n    d_model=dim,      # Model dimension\n    d_state=16,       # SSM state dimension\n    d_conv=4,         # Conv1d kernel size\n    expand=2          # Expansion factor\n).to(\"cuda\")\n\ny = model(x)  # O(n) complexity!\nassert y.shape == x.shape\n```\n\n## Common workflows\n\n### Workflow 1: Language model with Mamba-2\n\n**Complete LM with generation**:\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nfrom mamba_ssm.models.config_mamba import MambaConfig\nimport torch\n\n# Configure Mamba-2 LM\nconfig = MambaConfig(\n    d_model=1024,           # Hidden dimension\n    n_layer=24,             # Number of layers\n    vocab_size=50277,       # Vocabulary size\n    ssm_cfg=dict(\n        layer=\"Mamba2\",     # Use Mamba-2\n        d_state=128,        # Larger state for Mamba-2\n        headdim=64,         # Head dimension\n        ngroups=1           # Number of groups\n    )\n)\n\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\n\n# Generate text\ninput_ids = torch.randint(0, 1000, (1, 20), device=\"cuda\", dtype=torch.long)\noutput = model.generate(\n    input_ids=input_ids,\n    max_length=100,\n    temperature=0.7,\n    top_p=0.9\n)\n```\n\n### Workflow 2: Use pretrained Mamba models\n\n**Load from HuggingFace**:\n```python\nfrom transformers import AutoTokenizer\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n\n# Load pretrained model\nmodel_name = \"state-spaces\u002Fmamba-2.8b\"\ntokenizer = AutoTokenizer.from_pretrained(\"EleutherAI\u002Fgpt-neox-20b\")  # Use compatible tokenizer\nmodel = MambaLMHeadModel.from_pretrained(model_name, device=\"cuda\", dtype=torch.float16)\n\n# Generate\nprompt = \"The future of AI is\"\ninput_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\noutput_ids = model.generate(\n    input_ids=input_ids,\n    max_length=200,\n    temperature=0.7,\n    top_p=0.9,\n    repetition_penalty=1.2\n)\ngenerated_text = tokenizer.decode(output_ids[0])\nprint(generated_text)\n```\n\n**Available models**:\n- `state-spaces\u002Fmamba-130m`\n- `state-spaces\u002Fmamba-370m`\n- `state-spaces\u002Fmamba-790m`\n- `state-spaces\u002Fmamba-1.4b`\n- `state-spaces\u002Fmamba-2.8b`\n\n### Workflow 3: Mamba-1 vs Mamba-2\n\n**Mamba-1** (smaller state):\n```python\nfrom mamba_ssm import Mamba\n\nmodel = Mamba(\n    d_model=256,\n    d_state=16,      # Smaller state dimension\n    d_conv=4,\n    expand=2\n).to(\"cuda\")\n```\n\n**Mamba-2** (multi-head, larger state):\n```python\nfrom mamba_ssm import Mamba2\n\nmodel = Mamba2(\n    d_model=256,\n    d_state=128,     # Larger state dimension\n    d_conv=4,\n    expand=2,\n    headdim=64,      # Head dimension for multi-head\n    ngroups=1        # Parallel groups\n).to(\"cuda\")\n```\n\n**Key differences**:\n- **State size**: Mamba-1 (d_state=16) vs Mamba-2 (d_state=128)\n- **Architecture**: Mamba-2 has multi-head structure\n- **Normalization**: Mamba-2 uses RMSNorm\n- **Distributed**: Mamba-2 supports tensor parallelism\n\n### Workflow 4: Benchmark vs Transformers\n\n**Generation speed comparison**:\n```bash\n# Benchmark Mamba\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"state-spaces\u002Fmamba-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n\n# Benchmark Transformer\npython benchmarks\u002Fbenchmark_generation_mamba_simple.py \\\n  --model-name \"EleutherAI\u002Fpythia-2.8b\" \\\n  --prompt \"The future of machine learning is\" \\\n  --topp 0.9 --temperature 0.7 --repetition-penalty 1.2\n```\n\n**Expected results**:\n- **Mamba**: 5× faster inference\n- **Memory**: No KV cache needed\n- **Scaling**: Linear with sequence length\n\n## When to use vs alternatives\n\n**Use Mamba when**:\n- Need long sequences (100K+ tokens)\n- Want faster inference than Transformers\n- Memory-constrained (no KV cache)\n- Building streaming applications\n- Linear scaling important\n\n**Advantages**:\n- **O(n) complexity**: Linear vs quadratic\n- **5× faster inference**: No attention overhead\n- **No KV cache**: Lower memory usage\n- **Million-token sequences**: Hardware-efficient\n- **Streaming**: Constant memory per token\n\n**Use alternatives instead**:\n- **Transformers**: Need best-in-class performance, have compute\n- **RWKV**: Want RNN+Transformer hybrid\n- **RetNet**: Need retention-based architecture\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: CUDA out of memory**\n\nReduce batch size or use gradient checkpointing:\n```python\nmodel = MambaLMHeadModel(config, device=\"cuda\", dtype=torch.float16)\nmodel.gradient_checkpointing_enable()  # Enable checkpointing\n```\n\n**Issue: Slow installation**\n\nInstall binary wheels (not source):\n```bash\npip install mamba-ssm --no-build-isolation\n```\n\n**Issue: Missing causal-conv1d**\n\nInstall separately:\n```bash\npip install causal-conv1d>=1.4.0\n```\n\n**Issue: Model not loading from HuggingFace**\n\nUse `MambaLMHeadModel.from_pretrained` (not `AutoModel`):\n```python\nfrom mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\nmodel = MambaLMHeadModel.from_pretrained(\"state-spaces\u002Fmamba-2.8b\")\n```\n\n## Advanced topics\n\n**Selective SSM**: See [references\u002Fselective-ssm.md](references\u002Fselective-ssm.md) for mathematical formulation, state-space equations, and how selectivity enables O(n) complexity.\n\n**Mamba-2 architecture**: See [references\u002Fmamba2-details.md](references\u002Fmamba2-details.md) for multi-head structure, tensor parallelism, and distributed training setup.\n\n**Performance optimization**: See [references\u002Fperformance.md](references\u002Fperformance.md) for hardware-aware design, CUDA kernels, and memory efficiency techniques.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA with CUDA 11.6+\n- **VRAM**:\n  - 130M model: 2GB\n  - 370M model: 4GB\n  - 790M model: 8GB\n  - 1.4B model: 14GB\n  - 2.8B model: 28GB (FP16)\n- **Inference**: 5× faster than Transformers\n- **Memory**: No KV cache (lower than Transformers)\n\n**Performance** (vs Transformers):\n- **Speed**: 5× faster inference\n- **Memory**: 50% less (no KV cache)\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (Mamba-1): https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.00752 (Dec 2023)\n- Paper (Mamba-2): https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.21060 (May 2024)\n- GitHub: https:\u002F\u002Fgithub.com\u002Fstate-spaces\u002Fmamba ⭐ 13,000+\n- Models: https:\u002F\u002Fhuggingface.co\u002Fstate-spaces\n- Docs: Repository README and 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