model-pr-history-knowledge
ResearchUse when an SGLang, vLLM, TensorRT-LLM, or TokenSpeed serving/model optimization task needs prior model-family PR evidence. Query and read the PR-driven history docs under model-pr-optimization-history before choosing source paths, fast paths, kernel/fusion ideas, regression risks, or validation lanes.
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How to use this skill
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Model PR History Knowledge
This is a PR-driven knowledge base for model optimization history. It is not a set of per-model skills. Each model family keeps bilingual docs with inspected PR diffs, implementation file coverage, timelines, changed files, code excerpts, and validation/risk notes.
Use it before patching model-specific serving paths, choosing an SGLang SOTA optimization target, or explaining why a framework already has a faster path.
Query
Run commands from this directory:
python3 scripts/query.py --list
python3 scripts/query.py --framework sglang --model qwen3-core --paths-only
python3 scripts/query.py --framework sglang --model qwen3-core "fused qk norm rope"
python3 scripts/query.py --framework vllm "DeepSeek-V4 fused norm router" --limit 5
python3 scripts/query.py --framework tokenspeed qwen35 --paths-only
Useful options:
--framework sglang|vllm|tensorrt_llm|tokenspeed: restrict to one serving framework.--model <slug>: restrict to one model family directory.--lang en|zh|both: select English, Chinese, or both docs.--paths-only: print the exact docs to read without snippets.--limit N: bound search results.
Workflow
- Infer the model-family slug from the user's model id, checkpoint path, or
SGLang source path. If unsure, run
scripts/query.py "<model name>". - Read the matching SGLang history first for SGLang patch work. Read competitor
history too when vLLM, TensorRT-LLM, or TokenSpeed is the leading competitor
or its trace suggests a missing SGLang fast path. If the doc opens with a
dated
PR Backfill Auditsection, read it first: it lists the most recent PR-numbered merges that are not yet folded into the older timeline / diff-audit cards. - Extract only actionable evidence:
- model implementation files and symbols
- PRs that changed the hot source path
- prior fusions, overlap work, quantization, MoE, attention, cache, sampler, or loader changes
- open/watch PRs that may explain a known gap or pending support issue
- validation lanes and regression risks implied by the PR cards
- Save a short note in the active run artifacts, for example
history/model-pr-history-notes.md, with paths read, PR numbers, source files, and the decision each item influenced. - Do not copy long PR cards into the final answer. Cite paths and summarize the relevant implementation/risk.
Model Slugs
Current frameworks:
sglangvllmtensorrt_llmtokenspeed
Current model-family slugs include:
deepseek-ocr, deepseek-ocr-2, deepseek-v3-r1, deepseek-v31, deepseek-v32,
deepseek-v4, ernie45, gemma4, glm-vlm-ocr, glm45, glm46-glm47, glm5-glm51,
gpt-oss, intern-s1, internvl35, jina-reranker-m0, kimi, ling25, llada21,
llama31, llama33-70b, llama4, mimo-v2-flash, minimax, mistral-small-4,
mixtral-quark-int4fp8-moe, nemotron-super, qwen-vlm-omni-asr, qwen3-coder,
qwen3-core, qwen3-next, qwen35, ring25, step35
SOTA Loop Contract
For sglang-sota-humanize-loop, this knowledge base is an early context
source:
- Read it after model identification and before patch planning.
- Include the history paths and key PR evidence in
analysis/root-cause.mdorhistory/model-pr-history-notes.md. - If the profiler points at a known model path, check whether the history has prior changes on that file before writing a new patch.
- If a competitor is faster, search that competitor's model history for the same model family and stage before assuming the gap is kernel-local. Refresh live source/PRs for the exact target commit before patch planning when the comparison depends on latest upstream behavior.