executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
Explorer les Skills
Productivité · SKILL.md
distributed-blocks
distributed-blocksOperate Petals RemoteSequential block sequences, local block loading, quantization, tensor-parallel conversion, dtype constraints, and speculative Llama block internals.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentation de la Skill
GitHubname: distributed-blocks description: "Operate Petals RemoteSequential block sequences, local block loading, quantization, tensor-parallel conversion, dtype constraints, and speculative Llama block internals." disable-model-invocation: true metadata: disco-role: operating license: MIT
Petals Distributed Blocks
Use this sub-skill for block-level Petals tasks involving RemoteSequential, remote transformer block chains, hidden-state tensors, load_pretrained_block, QuantType, convert_block, tensor-parallel devices, DHT prefix alignment, or speculative Llama internals.
Read references/api-reference.md, references/workflows.md, and references/troubleshooting.md. Use python scripts/inspect_block_api.py --help for a no-network API inspection.
Route ordinary .generate() usage to client-inference, server launch flags to server-swarms, prompt training to prompt-tuning, and benchmarks to benchmarks-maintenance.
Checklist: RemoteSequential.forward() takes hidden states shaped [batch, seq, hidden]; block slices must be inside model layer bounds; clients and servers must agree on DHT prefix and block indices; deep prompts must match sliced block count; quantization requires a compatible optional backend.