القائمة
Checklist for adding support for a new GGUF model architecture (or a variant of an existing one) to SharpInference.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Drive the architect→workhorse flow — turn an approved plan into delegated Sonnet implementation with test and review gates, keeping the main (Opus/Fable) thread as coordinator.
Work a GitHub issue end-to-end — fetch it, verify the claim, turn it into an architect spec, and drive the delegated implementation flow with correct issue references in branches and commits.
Cross-check SharpInference numerics against llama.cpp and run accuracy gates. Use when output quality or numerics are in question — new architecture bring-up, kernel or quantization changes, KV-compression work, "model produces garbage" reports.
CLI run recipes for specific models and modes — VibeThinker, Ornith-1.0, Gemma 4 vision, image generation + upscaling, perplexity gating, whole-turn JSON-schema structured output, DSpark speculative decoding, download-model presets. Use when actually running inference for one of these.
Required workflow when adding, editing, or removing Vulkan GLSL shader consts — the committed precompiled SPIR-V table must be regenerated or tests fail on drift.
| name | new-arch |
| description | Checklist for adding support for a new GGUF model architecture (or a variant of an existing one) to SharpInference. |
First check it isn't already covered: many "new" models reduce to an existing arch (e.g. Ornith-1.0 is qwen35/qwen35moe — see CLAUDE.md). Inspect the GGUF before writing any code:
dotnet run --project src/SharpInference.Cli -c Release -- list-metadata -m model.gguf
dotnet run --project src/SharpInference.Cli -c Release -- list-tensors -m model.gguf
docs/<arch>-plan.md before coding; docs/qwen35moe-plan.md
is the template (tensor layout, hparams, forward-pass math, phased milestones).
This is an architect-agent task.src/SharpInference.Core/ModelGraph.cs: arch name dispatch, hparam
mapping, tensor-name wiring. Tokenizer + chat template usually come free via
Microsoft.ML.Tokenizers + JinjaChatTemplate; add a ToolCallAdapter entry
only if the model family has its own tool-call wire format.IForwardPass implementation whenever the math
allows: dense → ForwardPass/CudaForwardPass/GpuForwardPass; MoE →
HybridForwardPass/CudaHybridForwardPass; hybrid Gated-DeltaNet →
HybridGdnForwardPass/CudaHybridGdnForwardPass/VulkanHybridGdnForwardPass.
A genuinely new block type means new kernels in all backends you support —
scope CPU-first, GPU after parity.parity-check skill and
scripts/generate-reference-logits.ps1).scripts/download-model.ps1 preset (extend the ValidateSet),
CLAUDE.md architecture list, README.parity-check skill, levels 2 and 4). New-arch work is not done until greedy
tokens match llama.cpp at temp 0 on at least one real model.Plan doc and design → architect agent. Steps 2–5 → implementer agent(s), one
per step, sequential (each depends on the previous). Validation → test-runner
plus the parity-check skill in a background agent.