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RWKV-7 (March 2025). Models up to 14B parameters.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-rwkv\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"rwkv architecture","rwkv","architecture","time","orchestra","at","2025","Model Architecture","Efficient Inference","infer","project","complexity","up","context","parallel","model","Transformers","NeMo","linear","windows","2026-11-10 10:12:30",false,"---\nname: rwkv-architecture\ndescription: RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [RWKV, Model Architecture, RNN, Transformer Hybrid, Linear Complexity, Infinite Context, Efficient Inference, Linux Foundation, Alternative Architecture]\ndependencies: [rwkv, torch, transformers]\n---\n\n# RWKV - Receptance Weighted Key Value\n\n## Quick start\n\nRWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).\n\n**Installation**:\n```bash\n# Install PyTorch\npip install torch --upgrade --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu121\n\n# Install dependencies\npip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade\n\n# Install RWKV\npip install rwkv\n```\n\n**Basic usage** (GPT mode + RNN mode):\n```python\nimport os\nfrom rwkv.model import RWKV\n\nos.environ[\"RWKV_JIT_ON\"] = '1'\nos.environ[\"RWKV_CUDA_ON\"] = '1'  # Use CUDA kernel for speed\n\n# Load model\nmodel = RWKV(\n    model='\u002Fpath\u002Fto\u002FRWKV-4-Pile-1B5-20220903-8040',\n    strategy='cuda fp16'\n)\n\n# GPT mode (parallel processing)\nout, state = model.forward([187, 510, 1563, 310, 247], None)\nprint(out.detach().cpu().numpy())  # Logits\n\n# RNN mode (sequential processing, same result)\nout, state = model.forward([187, 510], None)  # First 2 tokens\nout, state = model.forward([1563], state)      # Next token\nout, state = model.forward([310, 247], state)  # Last tokens\nprint(out.detach().cpu().numpy())  # Same logits as above!\n```\n\n## Common workflows\n\n### Workflow 1: Text generation (streaming)\n\n**Efficient token-by-token generation**:\n```python\nfrom rwkv.model import RWKV\nfrom rwkv.utils import PIPELINE\n\nmodel = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')\npipeline = PIPELINE(model, \"20B_tokenizer.json\")\n\n# Initial prompt\nprompt = \"The future of AI is\"\nstate = None\n\n# Generate token by token\nfor token in prompt:\n    out, state = pipeline.model.forward(pipeline.encode(token), state)\n\n# Continue generation\nfor _ in range(100):\n    out, state = pipeline.model.forward(None, state)\n    token = pipeline.sample_logits(out)\n    print(pipeline.decode(token), end='', flush=True)\n```\n\n**Key advantage**: Constant memory per token (no growing KV cache)\n\n### Workflow 2: Long context processing (infinite context)\n\n**Process million-token sequences**:\n```python\nmodel = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')\n\n# Process very long document\nstate = None\nlong_document = load_document()  # e.g., 1M tokens\n\n# Stream through entire document\nfor chunk in chunks(long_document, chunk_size=1024):\n    out, state = model.forward(chunk, state)\n\n# State now contains information from entire 1M token document\n# Memory usage: O(1) (constant, not O(n)!)\n```\n\n### Workflow 3: Fine-tuning RWKV\n\n**Standard fine-tuning workflow**:\n```python\n# Training script\nimport pytorch_lightning as pl\nfrom rwkv.model import RWKV\nfrom rwkv.trainer import RWKVTrainer\n\n# Configure model\nconfig = {\n    'n_layer': 24,\n    'n_embd': 1024,\n    'vocab_size': 50277,\n    'ctx_len': 1024\n}\n\n# Setup trainer\ntrainer = pl.Trainer(\n    accelerator='gpu',\n    devices=8,\n    precision='bf16',\n    strategy='deepspeed_stage_2',\n    max_epochs=1\n)\n\n# Train\nmodel = RWKV(config)\ntrainer.fit(model, train_dataloader)\n```\n\n### Workflow 4: RWKV vs Transformer comparison\n\n**Memory comparison** (1M token sequence):\n```python\n# Transformer (GPT)\n# Memory: O(n²) for attention\n# KV cache: 1M × hidden_dim × n_layers × 2 (keys + values)\n# Example: 1M × 4096 × 24 × 2 = ~400GB (impractical!)\n\n# RWKV\n# Memory: O(1) per token\n# State: hidden_dim × n_layers = 4096 × 24 = ~400KB\n# 1,000,000× more efficient!\n```\n\n**Speed comparison** (inference):\n```python\n# Transformer: O(n) per token (quadratic overall)\n# First token: 1 computation\n# Second token: 2 computations\n# ...\n# 1000th token: 1000 computations\n\n# RWKV: O(1) per token (linear overall)\n# Every token: 1 computation\n# 1000th token: 1 computation (same as first!)\n```\n\n## When to use vs alternatives\n\n**Use RWKV when**:\n- Need very long context (100K+ tokens)\n- Want constant memory usage\n- Building streaming applications\n- Need RNN efficiency with Transformer performance\n- Memory-constrained deployment\n\n**Key advantages**:\n- **Linear time**: O(n) vs O(n²) for Transformers\n- **No KV cache**: Constant memory per token\n- **Infinite context**: No fixed window limit\n- **Parallelizable training**: Like GPT\n- **Sequential inference**: Like RNN\n\n**Use alternatives instead**:\n- **Transformers**: Need absolute best performance, have compute\n- **Mamba**: Want state-space models\n- **RetNet**: Need retention mechanism\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: Out of memory during training**\n\nUse gradient checkpointing and DeepSpeed:\n```python\ntrainer = pl.Trainer(\n    strategy='deepspeed_stage_3',  # Full ZeRO-3\n    precision='bf16'\n)\n```\n\n**Issue: Slow inference**\n\nEnable CUDA kernel:\n```python\nos.environ[\"RWKV_CUDA_ON\"] = '1'\n```\n\n**Issue: Model not loading**\n\nCheck model path and strategy:\n```python\nmodel = RWKV(\n    model='\u002Fabsolute\u002Fpath\u002Fto\u002Fmodel.pth',\n    strategy='cuda fp16'  # Or 'cpu fp32' for CPU\n)\n```\n\n**Issue: State management in RNN mode**\n\nAlways pass state between forward calls:\n```python\n# WRONG: State lost\nout1, _ = model.forward(tokens1, None)\nout2, _ = model.forward(tokens2, None)  # No context from tokens1!\n\n# CORRECT: State preserved\nout1, state = model.forward(tokens1, None)\nout2, state = model.forward(tokens2, state)  # Has context from tokens1\n```\n\n## Advanced topics\n\n**Time-mixing and channel-mixing**: See [references\u002Farchitecture-details.md](references\u002Farchitecture-details.md) for WKV operation, time-decay mechanism, and receptance gates.\n\n**State management**: See [references\u002Fstate-management.md](references\u002Fstate-management.md) for att_x_prev, att_kv, ffn_x_prev states, and numerical stability considerations.\n\n**RWKV-7 improvements**: See [references\u002Frwkv7.md](references\u002Frwkv7.md) for latest architectural improvements (March 2025) and multimodal capabilities.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA (CUDA 11.6+) or CPU\n- **VRAM** (FP16):\n  - 169M model: 1GB\n  - 430M model: 2GB\n  - 1.5B model: 4GB\n  - 3B model: 8GB\n  - 7B model: 16GB\n  - 14B model: 32GB\n- **Inference**: O(1) memory per token\n- **Training**: Parallelizable like GPT\n\n**Performance** (vs Transformers):\n- **Speed**: Similar training, faster inference\n- **Memory**: 1000× less for long sequences\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (RWKV): https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13048 (May 2023)\n- Paper (RWKV-7): https:\u002F\u002Farxiv.org\u002Fabs\u002F2503.14456 (March 2025)\n- GitHub: https:\u002F\u002Fgithub.com\u002FBlinkDL\u002FRWKV-LM ⭐ 12,000+\n- Docs: https:\u002F\u002Fwiki.rwkv.com\u002F\n- Models: https:\u002F\u002Fhuggingface.co\u002FBlinkDL\n- Linux Foundation AI: Official project\n- Production: Microsoft Windows, Office integration, NeMo support\n\n\n","# RWKV - Receptance Weighted Key Value\n\n## Quick start\n\nRWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).\n\n**Installation**:\n```bash\n# Install PyTorch\npip install torch --upgrade --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu121\n\n# Install dependencies\npip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade\n\n# Install RWKV\npip install rwkv\n```\n\n**Basic usage** (GPT mode + RNN mode):\n```python\nimport os\nfrom rwkv.model import RWKV\n\nos.environ[\"RWKV_JIT_ON\"] = '1'\nos.environ[\"RWKV_CUDA_ON\"] = '1'  # Use CUDA kernel for speed\n\n# Load model\nmodel = RWKV(\n    model='\u002Fpath\u002Fto\u002FRWKV-4-Pile-1B5-20220903-8040',\n    strategy='cuda fp16'\n)\n\n# GPT mode (parallel processing)\nout, state = model.forward([187, 510, 1563, 310, 247], None)\nprint(out.detach().cpu().numpy())  # Logits\n\n# RNN mode (sequential processing, same result)\nout, state = model.forward([187, 510], None)  # First 2 tokens\nout, state = model.forward([1563], state)      # Next token\nout, state = model.forward([310, 247], state)  # Last tokens\nprint(out.detach().cpu().numpy())  # Same logits as above!\n```\n\n## Common workflows\n\n### Workflow 1: Text generation (streaming)\n\n**Efficient token-by-token generation**:\n```python\nfrom rwkv.model import RWKV\nfrom rwkv.utils import PIPELINE\n\nmodel = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')\npipeline = PIPELINE(model, \"20B_tokenizer.json\")\n\n# Initial prompt\nprompt = \"The future of AI is\"\nstate = None\n\n# Generate token by token\nfor token in prompt:\n    out, state = pipeline.model.forward(pipeline.encode(token), state)\n\n# Continue generation\nfor _ in range(100):\n    out, state = pipeline.model.forward(None, state)\n    token = pipeline.sample_logits(out)\n    print(pipeline.decode(token), end='', flush=True)\n```\n\n**Key advantage**: Constant memory per token (no growing KV cache)\n\n### Workflow 2: Long context processing (infinite context)\n\n**Process million-token sequences**:\n```python\nmodel = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')\n\n# Process very long document\nstate = None\nlong_document = load_document()  # e.g., 1M tokens\n\n# Stream through entire document\nfor chunk in chunks(long_document, chunk_size=1024):\n    out, state = model.forward(chunk, state)\n\n# State now contains information from entire 1M token document\n# Memory usage: O(1) (constant, not O(n)!)\n```\n\n### Workflow 3: Fine-tuning RWKV\n\n**Standard fine-tuning workflow**:\n```python\n# Training script\nimport pytorch_lightning as pl\nfrom rwkv.model import RWKV\nfrom rwkv.trainer import RWKVTrainer\n\n# Configure model\nconfig = {\n    'n_layer': 24,\n    'n_embd': 1024,\n    'vocab_size': 50277,\n    'ctx_len': 1024\n}\n\n# Setup trainer\ntrainer = pl.Trainer(\n    accelerator='gpu',\n    devices=8,\n    precision='bf16',\n    strategy='deepspeed_stage_2',\n    max_epochs=1\n)\n\n# Train\nmodel = RWKV(config)\ntrainer.fit(model, train_dataloader)\n```\n\n### Workflow 4: RWKV vs Transformer comparison\n\n**Memory comparison** (1M token sequence):\n```python\n# Transformer (GPT)\n# Memory: O(n²) for attention\n# KV cache: 1M × hidden_dim × n_layers × 2 (keys + values)\n# Example: 1M × 4096 × 24 × 2 = ~400GB (impractical!)\n\n# RWKV\n# Memory: O(1) per token\n# State: hidden_dim × n_layers = 4096 × 24 = ~400KB\n# 1,000,000× more efficient!\n```\n\n**Speed comparison** (inference):\n```python\n# Transformer: O(n) per token (quadratic overall)\n# First token: 1 computation\n# Second token: 2 computations\n# ...\n# 1000th token: 1000 computations\n\n# RWKV: O(1) per token (linear overall)\n# Every token: 1 computation\n# 1000th token: 1 computation (same as first!)\n```\n\n## When to use vs alternatives\n\n**Use RWKV when**:\n- Need very long context (100K+ tokens)\n- Want constant memory usage\n- Building streaming applications\n- Need RNN efficiency with Transformer performance\n- Memory-constrained deployment\n\n**Key advantages**:\n- **Linear time**: O(n) vs O(n²) for Transformers\n- **No KV cache**: Constant memory per token\n- **Infinite context**: No fixed window limit\n- **Parallelizable training**: Like GPT\n- **Sequential inference**: Like RNN\n\n**Use alternatives instead**:\n- **Transformers**: Need absolute best performance, have compute\n- **Mamba**: Want state-space models\n- **RetNet**: Need retention mechanism\n- **Hyena**: Want convolution-based approach\n\n## Common issues\n\n**Issue: Out of memory during training**\n\nUse gradient checkpointing and DeepSpeed:\n```python\ntrainer = pl.Trainer(\n    strategy='deepspeed_stage_3',  # Full ZeRO-3\n    precision='bf16'\n)\n```\n\n**Issue: Slow inference**\n\nEnable CUDA kernel:\n```python\nos.environ[\"RWKV_CUDA_ON\"] = '1'\n```\n\n**Issue: Model not loading**\n\nCheck model path and strategy:\n```python\nmodel = RWKV(\n    model='\u002Fabsolute\u002Fpath\u002Fto\u002Fmodel.pth',\n    strategy='cuda fp16'  # Or 'cpu fp32' for CPU\n)\n```\n\n**Issue: State management in RNN mode**\n\nAlways pass state between forward calls:\n```python\n# WRONG: State lost\nout1, _ = model.forward(tokens1, None)\nout2, _ = model.forward(tokens2, None)  # No context from tokens1!\n\n# CORRECT: State preserved\nout1, state = model.forward(tokens1, None)\nout2, state = model.forward(tokens2, state)  # Has context from tokens1\n```\n\n## Advanced topics\n\n**Time-mixing and channel-mixing**: See [references\u002Farchitecture-details.md](references\u002Farchitecture-details.md) for WKV operation, time-decay mechanism, and receptance gates.\n\n**State management**: See [references\u002Fstate-management.md](references\u002Fstate-management.md) for att_x_prev, att_kv, ffn_x_prev states, and numerical stability considerations.\n\n**RWKV-7 improvements**: See [references\u002Frwkv7.md](references\u002Frwkv7.md) for latest architectural improvements (March 2025) and multimodal capabilities.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA (CUDA 11.6+) or CPU\n- **VRAM** (FP16):\n  - 169M model: 1GB\n  - 430M model: 2GB\n  - 1.5B model: 4GB\n  - 3B model: 8GB\n  - 7B model: 16GB\n  - 14B model: 32GB\n- **Inference**: O(1) memory per token\n- **Training**: Parallelizable like GPT\n\n**Performance** (vs Transformers):\n- **Speed**: Similar training, faster inference\n- **Memory**: 1000× less for long sequences\n- **Scaling**: Linear vs quadratic\n\n## Resources\n\n- Paper (RWKV): https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13048 (May 2023)\n- Paper (RWKV-7): https:\u002F\u002Farxiv.org\u002Fabs\u002F2503.14456 (March 2025)\n- GitHub: https:\u002F\u002Fgithub.com\u002FBlinkDL\u002FRWKV-LM ⭐ 12,000+\n- Docs: https:\u002F\u002Fwiki.rwkv.com\u002F\n- Models: https:\u002F\u002Fhuggingface.co\u002FBlinkDL\n- Linux Foundation AI: Official project\n- Production: Microsoft Windows, Office integration, NeMo support\n\n\n",{"name":5,"tags":44,"author":52,"license":15,"version":53,"description":7,"dependencies":54},[45,26,46,47,48,49,27,50,51],"RWKV","RNN","Transformer Hybrid","Linear Complexity","Infinite Context","Linux Foundation","Alternative Architecture","Orchestra Research","1.0.0",[20,55,56],"torch","transformers","main","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-rwkv",2,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates","https:\u002F\u002Fgithub.com\u002Ftransaurus","Automated works of Page Turner","2026-03-28 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Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[152],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[111,112,154,155,156,157,158,159,160,33,161,162,163,164,165,166,167,168,123,124],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":39},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[176,177,178,179,180,157,181,158,159,182,116,183,184,185,186,187,188,168,123,124],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":198,"publishedAt":39},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[195],{"categoryNo":196,"categoryName":197,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000021","资料检索与研究",[199,200,201,168,202,111,203,204,205,206,207,208,116,209,210,211,212,213,214,123],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":225,"publishedAt":39},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[221,224],{"categoryNo":222,"categoryName":223,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[111,226,163,227,107,228,229,157,21,204,200,230,231,232,233,234,235,236,237,238],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","database","api","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]