[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADV3NTTD3M01D8DYVJ8XBE":3,"skill-same:01M6ADV3NTTD3M01D8DYVJ8XBE":156},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":67,"ownerHtmlUrl":68,"ownerBlogUrl":69,"ownerDescription":70,"updatedAt":71,"install":72,"resources":85},"01M6ADV3NTTD3M01D8DYVJ8XBE","model-architecture-torchtitan",null,"Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan\u002FSKILL.md",[],[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","model architecture torchtitan","torchtitan","model","Model Architecture","when","research","checkpointing","custom","orchestra","at","pp","native","FSDP2","context","parallel","pipeline","Llama","Distributed Training","tensor","compile","training","pytorch","distributed","2026-11-10 10:12:30",false,"---\nname: model-architecture-torchtitan\ndescription: Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Model Architecture, Distributed Training, TorchTitan, FSDP2, Tensor Parallel, Pipeline Parallel, Context Parallel, Float8, Llama, Pretraining]\n  dependencies: [torch>=2.6.0, torchtitan>=0.2.0, torchao>=0.5.0]\n---\n\n# TorchTitan - PyTorch Native Distributed LLM Pretraining\n\n## Quick start\n\nTorchTitan is PyTorch's official platform for large-scale LLM pretraining with composable 4D parallelism (FSDP2, TP, PP, CP), achieving 65%+ speedups over baselines on H100 GPUs.\n\n**Installation**:\n```bash\n# From PyPI (stable)\npip install torchtitan\n\n# From source (latest features, requires PyTorch nightly)\ngit clone https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftorchtitan\ncd torchtitan\npip install -r requirements.txt\n```\n\n**Download tokenizer**:\n```bash\n# Get HF token from https:\u002F\u002Fhuggingface.co\u002Fsettings\u002Ftokens\npython scripts\u002Fdownload_hf_assets.py --repo_id meta-llama\u002FLlama-3.1-8B --assets tokenizer --hf_token=...\n```\n\n**Start training on 8 GPUs**:\n```bash\nCONFIG_FILE=\".\u002Ftorchtitan\u002Fmodels\u002Fllama3\u002Ftrain_configs\u002Fllama3_8b.toml\" .\u002Frun_train.sh\n```\n\n## Common workflows\n\n### Workflow 1: Pretrain Llama 3.1 8B on single node\n\nCopy this checklist:\n\n```\nSingle Node Pretraining:\n- [ ] Step 1: Download tokenizer\n- [ ] Step 2: Configure training\n- [ ] Step 3: Launch training\n- [ ] Step 4: Monitor and checkpoint\n```\n\n**Step 1: Download tokenizer**\n\n```bash\npython scripts\u002Fdownload_hf_assets.py \\\n  --repo_id meta-llama\u002FLlama-3.1-8B \\\n  --assets tokenizer \\\n  --hf_token=YOUR_HF_TOKEN\n```\n\n**Step 2: Configure training**\n\nEdit or create a TOML config file:\n\n```toml\n# llama3_8b_custom.toml\n[job]\ndump_folder = \".\u002Foutputs\"\ndescription = \"Llama 3.1 8B training\"\n\n[model]\nname = \"llama3\"\nflavor = \"8B\"\nhf_assets_path = \".\u002Fassets\u002Fhf\u002FLlama-3.1-8B\"\n\n[optimizer]\nname = \"AdamW\"\nlr = 3e-4\n\n[lr_scheduler]\nwarmup_steps = 200\n\n[training]\nlocal_batch_size = 2\nseq_len = 8192\nmax_norm = 1.0\nsteps = 1000\ndataset = \"c4\"\n\n[parallelism]\ndata_parallel_shard_degree = -1  # Use all GPUs for FSDP\n\n[activation_checkpoint]\nmode = \"selective\"\nselective_ac_option = \"op\"\n\n[checkpoint]\nenable = true\nfolder = \"checkpoint\"\ninterval = 500\n```\n\n**Step 3: Launch training**\n\n```bash\n# 8 GPUs on single node\nCONFIG_FILE=\".\u002Fllama3_8b_custom.toml\" .\u002Frun_train.sh\n\n# Or explicitly with torchrun\ntorchrun --nproc_per_node=8 \\\n  -m torchtitan.train \\\n  --job.config_file .\u002Fllama3_8b_custom.toml\n```\n\n**Step 4: Monitor and checkpoint**\n\nTensorBoard logs are saved to `.\u002Foutputs\u002Ftb\u002F`:\n```bash\ntensorboard --logdir .\u002Foutputs\u002Ftb\n```\n\n### Workflow 2: Multi-node training with SLURM\n\n```\nMulti-Node Training:\n- [ ] Step 1: Configure parallelism for scale\n- [ ] Step 2: Set up SLURM script\n- [ ] Step 3: Submit job\n- [ ] Step 4: Resume from checkpoint\n```\n\n**Step 1: Configure parallelism for scale**\n\nFor 70B model on 256 GPUs (32 nodes):\n```toml\n[parallelism]\ndata_parallel_shard_degree = 32  # FSDP across 32 ranks\ntensor_parallel_degree = 8        # TP within node\npipeline_parallel_degree = 1      # No PP for 70B\ncontext_parallel_degree = 1       # Increase for long sequences\n```\n\n**Step 2: Set up SLURM script**\n\n```bash\n#!\u002Fbin\u002Fbash\n#SBATCH --job-name=llama70b\n#SBATCH --nodes=32\n#SBATCH --ntasks-per-node=8\n#SBATCH --gpus-per-node=8\n\nsrun torchrun \\\n  --nnodes=32 \\\n  --nproc_per_node=8 \\\n  --rdzv_backend=c10d \\\n  --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \\\n  -m torchtitan.train \\\n  --job.config_file .\u002Fllama3_70b.toml\n```\n\n**Step 3: Submit job**\n\n```bash\nsbatch multinode_trainer.slurm\n```\n\n**Step 4: Resume from checkpoint**\n\nTraining auto-resumes if checkpoint exists in configured folder.\n\n### Workflow 3: Enable Float8 training for H100s\n\nFloat8 provides 30-50% speedup on H100 GPUs.\n\n```\nFloat8 Training:\n- [ ] Step 1: Install torchao\n- [ ] Step 2: Configure Float8\n- [ ] Step 3: Launch with compile\n```\n\n**Step 1: Install torchao**\n\n```bash\nUSE_CPP=0 pip install git+https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fao.git\n```\n\n**Step 2: Configure Float8**\n\nAdd to your TOML config:\n```toml\n[model]\nconverters = [\"quantize.linear.float8\"]\n\n[quantize.linear.float8]\nenable_fsdp_float8_all_gather = true\nprecompute_float8_dynamic_scale_for_fsdp = true\nfilter_fqns = [\"output\"]  # Exclude output layer\n\n[compile]\nenable = true\ncomponents = [\"model\", \"loss\"]\n```\n\n**Step 3: Launch with compile**\n\n```bash\nCONFIG_FILE=\".\u002Fllama3_8b.toml\" .\u002Frun_train.sh \\\n  --model.converters=\"quantize.linear.float8\" \\\n  --quantize.linear.float8.enable_fsdp_float8_all_gather \\\n  --compile.enable\n```\n\n### Workflow 4: 4D parallelism for 405B models\n\n```\n4D Parallelism (FSDP + TP + PP + CP):\n- [ ] Step 1: Create seed checkpoint\n- [ ] Step 2: Configure 4D parallelism\n- [ ] Step 3: Launch on 512 GPUs\n```\n\n**Step 1: Create seed checkpoint**\n\nRequired for consistent initialization across PP stages:\n```bash\nNGPU=1 CONFIG_FILE=.\u002Fllama3_405b.toml .\u002Frun_train.sh \\\n  --checkpoint.enable \\\n  --checkpoint.create_seed_checkpoint \\\n  --parallelism.data_parallel_shard_degree 1 \\\n  --parallelism.tensor_parallel_degree 1 \\\n  --parallelism.pipeline_parallel_degree 1\n```\n\n**Step 2: Configure 4D parallelism**\n\n```toml\n[parallelism]\ndata_parallel_shard_degree = 8   # FSDP\ntensor_parallel_degree = 8       # TP within node\npipeline_parallel_degree = 8     # PP across nodes\ncontext_parallel_degree = 1      # CP for long sequences\n\n[training]\nlocal_batch_size = 32\nseq_len = 8192\n```\n\n**Step 3: Launch on 512 GPUs**\n\n```bash\n# 64 nodes x 8 GPUs = 512 GPUs\nsrun torchrun --nnodes=64 --nproc_per_node=8 \\\n  -m torchtitan.train \\\n  --job.config_file .\u002Fllama3_405b.toml\n```\n\n## When to use vs alternatives\n\n**Use TorchTitan when:**\n- Pretraining LLMs from scratch (8B to 405B+)\n- Need PyTorch-native solution without third-party dependencies\n- Require composable 4D parallelism (FSDP2, TP, PP, CP)\n- Training on H100s with Float8 support\n- Want interoperable checkpoints with torchtune\u002FHuggingFace\n\n**Use alternatives instead:**\n- **Megatron-LM**: Maximum performance for NVIDIA-only deployments\n- **DeepSpeed**: Broader ZeRO optimization ecosystem, inference support\n- **Axolotl\u002FTRL**: Fine-tuning rather than pretraining\n- **LitGPT**: Educational, smaller-scale training\n\n## Common issues\n\n**Issue: Out of memory on large models**\n\nEnable activation checkpointing and reduce batch size:\n```toml\n[activation_checkpoint]\nmode = \"full\"  # Instead of \"selective\"\n\n[training]\nlocal_batch_size = 1\n```\n\nOr use gradient accumulation:\n```toml\n[training]\nlocal_batch_size = 1\nglobal_batch_size = 32  # Accumulates gradients\n```\n\n**Issue: TP causes high memory with async collectives**\n\nSet environment variable:\n```bash\nexport TORCH_NCCL_AVOID_RECORD_STREAMS=1\n```\n\n**Issue: Float8 training not faster**\n\nFloat8 only benefits large GEMMs. Filter small layers:\n```toml\n[quantize.linear.float8]\nfilter_fqns = [\"attention.wk\", \"attention.wv\", \"output\", \"auto_filter_small_kn\"]\n```\n\n**Issue: Checkpoint loading fails after parallelism change**\n\nUse DCP's resharding capability:\n```bash\n# Convert sharded checkpoint to single file\npython -m torch.distributed.checkpoint.format_utils \\\n  dcp_to_torch checkpoint\u002Fstep-1000 checkpoint.pt\n```\n\n**Issue: Pipeline parallelism initialization**\n\nCreate seed checkpoint first (see Workflow 4, Step 1).\n\n## Supported models\n\n| Model | Sizes | Status |\n|-------|-------|--------|\n| Llama 3.1 | 8B, 70B, 405B | Production |\n| Llama 4 | Various | Experimental |\n| DeepSeek V3 | 16B, 236B, 671B (MoE) | Experimental |\n| GPT-OSS | 20B, 120B (MoE) | Experimental |\n| Qwen 3 | Various | Experimental |\n| Flux | Diffusion | Experimental |\n\n## Performance benchmarks (H100)\n\n| Model | GPUs | Parallelism | TPS\u002FGPU | Techniques |\n|-------|------|-------------|---------|------------|\n| Llama 8B | 8 | FSDP | 5,762 | Baseline |\n| Llama 8B | 8 | FSDP+compile+FP8 | 8,532 | +48% |\n| Llama 70B | 256 | FSDP+TP+AsyncTP | 876 | 2D parallel |\n| Llama 405B | 512 | FSDP+TP+PP | 128 | 3D parallel |\n\n## Advanced topics\n\n**FSDP2 configuration**: See [references\u002Ffsdp.md](references\u002Ffsdp.md) for detailed FSDP2 vs FSDP1 comparison and ZeRO equivalents.\n\n**Float8 training**: See [references\u002Ffloat8.md](references\u002Ffloat8.md) for tensorwise vs rowwise scaling recipes.\n\n**Checkpointing**: See [references\u002Fcheckpoint.md](references\u002Fcheckpoint.md) for HuggingFace conversion and async checkpointing.\n\n**Adding custom models**: See [references\u002Fcustom-models.md](references\u002Fcustom-models.md) for TrainSpec protocol.\n\n## Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftorchtitan\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.06511\n- ICLR 2025: https:\u002F\u002Ficlr.cc\u002Fvirtual\u002F2025\u002Fposter\u002F29620\n- PyTorch Forum: https:\u002F\u002Fdiscuss.pytorch.org\u002Fc\u002Fdistributed\u002Ftorchtitan\u002F44\n\n","# TorchTitan - PyTorch Native Distributed LLM Pretraining\n\n## Quick start\n\nTorchTitan is PyTorch's official platform for large-scale LLM pretraining with composable 4D parallelism (FSDP2, TP, PP, CP), achieving 65%+ speedups over baselines on H100 GPUs.\n\n**Installation**:\n```bash\n# From PyPI (stable)\npip install torchtitan\n\n# From source (latest features, requires PyTorch nightly)\ngit clone https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftorchtitan\ncd torchtitan\npip install -r requirements.txt\n```\n\n**Download tokenizer**:\n```bash\n# Get HF token from https:\u002F\u002Fhuggingface.co\u002Fsettings\u002Ftokens\npython scripts\u002Fdownload_hf_assets.py --repo_id meta-llama\u002FLlama-3.1-8B --assets tokenizer --hf_token=...\n```\n\n**Start training on 8 GPUs**:\n```bash\nCONFIG_FILE=\".\u002Ftorchtitan\u002Fmodels\u002Fllama3\u002Ftrain_configs\u002Fllama3_8b.toml\" .\u002Frun_train.sh\n```\n\n## Common workflows\n\n### Workflow 1: Pretrain Llama 3.1 8B on single node\n\nCopy this checklist:\n\n```\nSingle Node Pretraining:\n- [ ] Step 1: Download tokenizer\n- [ ] Step 2: Configure training\n- [ ] Step 3: Launch training\n- [ ] Step 4: Monitor and checkpoint\n```\n\n**Step 1: Download tokenizer**\n\n```bash\npython scripts\u002Fdownload_hf_assets.py \\\n  --repo_id meta-llama\u002FLlama-3.1-8B \\\n  --assets tokenizer \\\n  --hf_token=YOUR_HF_TOKEN\n```\n\n**Step 2: Configure training**\n\nEdit or create a TOML config file:\n\n```toml\n# llama3_8b_custom.toml\n[job]\ndump_folder = \".\u002Foutputs\"\ndescription = \"Llama 3.1 8B training\"\n\n[model]\nname = \"llama3\"\nflavor = \"8B\"\nhf_assets_path = \".\u002Fassets\u002Fhf\u002FLlama-3.1-8B\"\n\n[optimizer]\nname = \"AdamW\"\nlr = 3e-4\n\n[lr_scheduler]\nwarmup_steps = 200\n\n[training]\nlocal_batch_size = 2\nseq_len = 8192\nmax_norm = 1.0\nsteps = 1000\ndataset = \"c4\"\n\n[parallelism]\ndata_parallel_shard_degree = -1  # Use all GPUs for FSDP\n\n[activation_checkpoint]\nmode = \"selective\"\nselective_ac_option = \"op\"\n\n[checkpoint]\nenable = true\nfolder = \"checkpoint\"\ninterval = 500\n```\n\n**Step 3: Launch training**\n\n```bash\n# 8 GPUs on single node\nCONFIG_FILE=\".\u002Fllama3_8b_custom.toml\" .\u002Frun_train.sh\n\n# Or explicitly with torchrun\ntorchrun --nproc_per_node=8 \\\n  -m torchtitan.train \\\n  --job.config_file .\u002Fllama3_8b_custom.toml\n```\n\n**Step 4: Monitor and checkpoint**\n\nTensorBoard logs are saved to `.\u002Foutputs\u002Ftb\u002F`:\n```bash\ntensorboard --logdir .\u002Foutputs\u002Ftb\n```\n\n### Workflow 2: Multi-node training with SLURM\n\n```\nMulti-Node Training:\n- [ ] Step 1: Configure parallelism for scale\n- [ ] Step 2: Set up SLURM script\n- [ ] Step 3: Submit job\n- [ ] Step 4: Resume from checkpoint\n```\n\n**Step 1: Configure parallelism for scale**\n\nFor 70B model on 256 GPUs (32 nodes):\n```toml\n[parallelism]\ndata_parallel_shard_degree = 32  # FSDP across 32 ranks\ntensor_parallel_degree = 8        # TP within node\npipeline_parallel_degree = 1      # No PP for 70B\ncontext_parallel_degree = 1       # Increase for long sequences\n```\n\n**Step 2: Set up SLURM script**\n\n```bash\n#!\u002Fbin\u002Fbash\n#SBATCH --job-name=llama70b\n#SBATCH --nodes=32\n#SBATCH --ntasks-per-node=8\n#SBATCH --gpus-per-node=8\n\nsrun torchrun \\\n  --nnodes=32 \\\n  --nproc_per_node=8 \\\n  --rdzv_backend=c10d \\\n  --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \\\n  -m torchtitan.train \\\n  --job.config_file .\u002Fllama3_70b.toml\n```\n\n**Step 3: Submit job**\n\n```bash\nsbatch multinode_trainer.slurm\n```\n\n**Step 4: Resume from checkpoint**\n\nTraining auto-resumes if checkpoint exists in configured folder.\n\n### Workflow 3: Enable Float8 training for H100s\n\nFloat8 provides 30-50% speedup on H100 GPUs.\n\n```\nFloat8 Training:\n- [ ] Step 1: Install torchao\n- [ ] Step 2: Configure Float8\n- [ ] Step 3: Launch with compile\n```\n\n**Step 1: Install torchao**\n\n```bash\nUSE_CPP=0 pip install git+https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fao.git\n```\n\n**Step 2: Configure Float8**\n\nAdd to your TOML config:\n```toml\n[model]\nconverters = [\"quantize.linear.float8\"]\n\n[quantize.linear.float8]\nenable_fsdp_float8_all_gather = true\nprecompute_float8_dynamic_scale_for_fsdp = true\nfilter_fqns = [\"output\"]  # Exclude output layer\n\n[compile]\nenable = true\ncomponents = [\"model\", \"loss\"]\n```\n\n**Step 3: Launch with compile**\n\n```bash\nCONFIG_FILE=\".\u002Fllama3_8b.toml\" .\u002Frun_train.sh \\\n  --model.converters=\"quantize.linear.float8\" \\\n  --quantize.linear.float8.enable_fsdp_float8_all_gather \\\n  --compile.enable\n```\n\n### Workflow 4: 4D parallelism for 405B models\n\n```\n4D Parallelism (FSDP + TP + PP + CP):\n- [ ] Step 1: Create seed checkpoint\n- [ ] Step 2: Configure 4D parallelism\n- [ ] Step 3: Launch on 512 GPUs\n```\n\n**Step 1: Create seed checkpoint**\n\nRequired for consistent initialization across PP stages:\n```bash\nNGPU=1 CONFIG_FILE=.\u002Fllama3_405b.toml .\u002Frun_train.sh \\\n  --checkpoint.enable \\\n  --checkpoint.create_seed_checkpoint \\\n  --parallelism.data_parallel_shard_degree 1 \\\n  --parallelism.tensor_parallel_degree 1 \\\n  --parallelism.pipeline_parallel_degree 1\n```\n\n**Step 2: Configure 4D parallelism**\n\n```toml\n[parallelism]\ndata_parallel_shard_degree = 8   # FSDP\ntensor_parallel_degree = 8       # TP within node\npipeline_parallel_degree = 8     # PP across nodes\ncontext_parallel_degree = 1      # CP for long sequences\n\n[training]\nlocal_batch_size = 32\nseq_len = 8192\n```\n\n**Step 3: Launch on 512 GPUs**\n\n```bash\n# 64 nodes x 8 GPUs = 512 GPUs\nsrun torchrun --nnodes=64 --nproc_per_node=8 \\\n  -m torchtitan.train \\\n  --job.config_file .\u002Fllama3_405b.toml\n```\n\n## When to use vs alternatives\n\n**Use TorchTitan when:**\n- Pretraining LLMs from scratch (8B to 405B+)\n- Need PyTorch-native solution without third-party dependencies\n- Require composable 4D parallelism (FSDP2, TP, PP, CP)\n- Training on H100s with Float8 support\n- Want interoperable checkpoints with torchtune\u002FHuggingFace\n\n**Use alternatives instead:**\n- **Megatron-LM**: Maximum performance for NVIDIA-only deployments\n- **DeepSpeed**: Broader ZeRO optimization ecosystem, inference support\n- **Axolotl\u002FTRL**: Fine-tuning rather than pretraining\n- **LitGPT**: Educational, smaller-scale training\n\n## Common issues\n\n**Issue: Out of memory on large models**\n\nEnable activation checkpointing and reduce batch size:\n```toml\n[activation_checkpoint]\nmode = \"full\"  # Instead of \"selective\"\n\n[training]\nlocal_batch_size = 1\n```\n\nOr use gradient accumulation:\n```toml\n[training]\nlocal_batch_size = 1\nglobal_batch_size = 32  # Accumulates gradients\n```\n\n**Issue: TP causes high memory with async collectives**\n\nSet environment variable:\n```bash\nexport TORCH_NCCL_AVOID_RECORD_STREAMS=1\n```\n\n**Issue: Float8 training not faster**\n\nFloat8 only benefits large GEMMs. Filter small layers:\n```toml\n[quantize.linear.float8]\nfilter_fqns = [\"attention.wk\", \"attention.wv\", \"output\", \"auto_filter_small_kn\"]\n```\n\n**Issue: Checkpoint loading fails after parallelism change**\n\nUse DCP's resharding capability:\n```bash\n# Convert sharded checkpoint to single file\npython -m torch.distributed.checkpoint.format_utils \\\n  dcp_to_torch checkpoint\u002Fstep-1000 checkpoint.pt\n```\n\n**Issue: Pipeline parallelism initialization**\n\nCreate seed checkpoint first (see Workflow 4, Step 1).\n\n## Supported models\n\n| Model | Sizes | Status |\n|-------|-------|--------|\n| Llama 3.1 | 8B, 70B, 405B | Production |\n| Llama 4 | Various | Experimental |\n| DeepSeek V3 | 16B, 236B, 671B (MoE) | Experimental |\n| GPT-OSS | 20B, 120B (MoE) | Experimental |\n| Qwen 3 | Various | Experimental |\n| Flux | Diffusion | Experimental |\n\n## Performance benchmarks (H100)\n\n| Model | GPUs | Parallelism | TPS\u002FGPU | Techniques |\n|-------|------|-------------|---------|------------|\n| Llama 8B | 8 | FSDP | 5,762 | Baseline |\n| Llama 8B | 8 | FSDP+compile+FP8 | 8,532 | +48% |\n| Llama 70B | 256 | FSDP+TP+AsyncTP | 876 | 2D parallel |\n| Llama 405B | 512 | FSDP+TP+PP | 128 | 3D parallel |\n\n## Advanced topics\n\n**FSDP2 configuration**: See [references\u002Ffsdp.md](references\u002Ffsdp.md) for detailed FSDP2 vs FSDP1 comparison and ZeRO equivalents.\n\n**Float8 training**: See [references\u002Ffloat8.md](references\u002Ffloat8.md) for tensorwise vs rowwise scaling recipes.\n\n**Checkpointing**: See [references\u002Fcheckpoint.md](references\u002Fcheckpoint.md) for HuggingFace conversion and async checkpointing.\n\n**Adding custom models**: See [references\u002Fcustom-models.md](references\u002Fcustom-models.md) for TrainSpec protocol.\n\n## Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftorchtitan\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.06511\n- ICLR 2025: https:\u002F\u002Ficlr.cc\u002Fvirtual\u002F2025\u002Fposter\u002F29620\n- PyTorch Forum: https:\u002F\u002Fdiscuss.pytorch.org\u002Fc\u002Fdistributed\u002Ftorchtitan\u002F44\n\n",{"name":5,"license":15,"metadata":48,"description":7},{"tags":49,"author":56,"version":57,"dependencies":58},[23,37,50,32,51,52,53,54,36,55],"TorchTitan","Tensor Parallel","Pipeline Parallel","Context Parallel","Float8","Pretraining","Orchestra Research","1.0.0",[59,60,61],"torch>=2.6.0","torchtitan>=0.2.0","torchao>=0.5.0","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":73,"prompt":83,"zipAvailable":84},[74,77,80],{"tool":75,"command":76},"npx","npx skills add davila7\u002Fclaude-code-templates --skill 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model-architecture-torchtitan","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan\u002FSKILL.md",true,[86,92,98,102,106,110,114,118,124,128,132,136,140,144,148,152],{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"forms.md","REFERENCE","参考文档",11854,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fforms.md",{"path":93,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":96,"sourceHtmlUrl":97},"LICENSE.txt","LICENSE","许可证",1467,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002FLICENSE.txt",{"path":99,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":100,"sourceHtmlUrl":101},"reference.md",16692,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Freference.md",{"path":103,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fcheckpoint.md",4216,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan\u002Freferences\u002Fcheckpoint.md",{"path":107,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fcustom-models.md",7383,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan\u002Freferences\u002Fcustom-models.md",{"path":111,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":112,"sourceHtmlUrl":113},"references\u002Ffloat8.md",4055,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan\u002Freferences\u002Ffloat8.md",{"path":115,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":116,"sourceHtmlUrl":117},"references\u002Ffsdp.md",3888,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan\u002Freferences\u002Ffsdp.md",{"path":119,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":122,"sourceHtmlUrl":123},"scripts\u002Fcheck_bounding_boxes_test.py","SCRIPT","脚本",8818,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Fcheck_bounding_boxes_test.py",{"path":125,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":126,"sourceHtmlUrl":127},"scripts\u002Fcheck_bounding_boxes.py",2774,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Fcheck_bounding_boxes.py",{"path":129,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":130,"sourceHtmlUrl":131},"scripts\u002Fcheck_fillable_fields.py",268,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Fcheck_fillable_fields.py",{"path":133,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":134,"sourceHtmlUrl":135},"scripts\u002Fconvert_pdf_to_images.py",1008,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Fconvert_pdf_to_images.py",{"path":137,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":138,"sourceHtmlUrl":139},"scripts\u002Fcreate_validation_image.py",1258,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Fcreate_validation_image.py",{"path":141,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":142,"sourceHtmlUrl":143},"scripts\u002Fextract_form_field_info.py",4300,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Fextract_form_field_info.py",{"path":145,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":146,"sourceHtmlUrl":147},"scripts\u002Fextract_form_structure.py",3945,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Fextract_form_structure.py",{"path":149,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":150,"sourceHtmlUrl":151},"scripts\u002Ffill_fillable_fields.py",3819,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Ffill_fillable_fields.py",{"path":153,"resourceType":120,"resourceTypeDesc":121,"sizeBytes":154,"sourceHtmlUrl":155},"scripts\u002Ffill_pdf_form_with_annotations.py",3235,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdocument-processing\u002Fpdf-anthropic\u002Fscripts\u002Ffill_pdf_form_with_annotations.py",[157,188,215,248,283,307],{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":168,"publishedAt":43},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[163],{"categoryNo":164,"categoryName":165,"taxonomyType":166,"taxonomyTypeDesc":167},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[169,170,171,172,173,174,175,176,177,24,178,179,180,181,182,183,184,185,186,187],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":195,"publishedAt":43},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[194],{"categoryNo":164,"categoryName":165,"taxonomyType":166,"taxonomyTypeDesc":167},[196,197,198,199,200,201,202,203,204,205,206,24,207,208,209,210,211,212,213,214],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"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":230,"publishedAt":43},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[221,224,225],{"categoryNo":222,"categoryName":223,"taxonomyType":166,"taxonomyTypeDesc":167},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":164,"categoryName":165,"taxonomyType":166,"taxonomyTypeDesc":167},{"categoryNo":226,"categoryName":227,"taxonomyType":228,"taxonomyTypeDesc":229},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[231,232,233,234,235,236,237,174,238,239,240,241,242,243,244,177,245,246,24,247],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":249,"name":250,"nameZh":6,"description":251,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":252,"licenseFileUrl":253,"sourceHtmlUrl":254,"categories":255,"keywords":259,"publishedAt":43},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[256],{"categoryNo":257,"categoryName":258,"taxonomyType":166,"taxonomyTypeDesc":167},"01JBSEEDCAT000000000000009","前端开发",[260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,174,282],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":284,"name":285,"nameZh":6,"description":286,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":252,"licenseFileUrl":287,"sourceHtmlUrl":288,"categories":289,"keywords":294,"publishedAt":43},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[290,293],{"categoryNo":291,"categoryName":292,"taxonomyType":166,"taxonomyTypeDesc":167},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":257,"categoryName":258,"taxonomyType":166,"taxonomyTypeDesc":167},[295,296,297,298,299,261,300,270,271,301,302,303,304,174,305,179,306],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":308,"name":309,"nameZh":6,"description":310,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":252,"licenseFileUrl":311,"sourceHtmlUrl":312,"categories":313,"keywords":321,"publishedAt":43},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[314,317,318],{"categoryNo":315,"categoryName":316,"taxonomyType":166,"taxonomyTypeDesc":167},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":164,"categoryName":165,"taxonomyType":166,"taxonomyTypeDesc":167},{"categoryNo":319,"categoryName":320,"taxonomyType":228,"taxonomyTypeDesc":229},"01JBSEEDCAT000000000000036","内容创作者",[322,323,324,325,326,327,328,329,263,330,273,207,331,332,333,334,335,243,336,24,337,338,339,340,341,185,342],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]