Merges 11 commits from main into pd, including:
- trained-roofline latency backend (inference-sim#616)
- MaxOutputLen population + engine auto-fill (inference-sim#621)
- MaxModelLen int64 + rope_scaling extraction (inference-sim#606)
- MoE-aware roofline latency (inference-sim#561)
- MaxModelLen enforcement + oracle knowledge boundary (inference-sim#579, inference-sim#587)
- Dead HardwareCalib fields removal (inference-sim#596)
- Default example model switch to Qwen3-14B (inference-sim#608)
- CI/CD updates (inference-sim#600, inference-sim#601, inference-sim#607)

Conflict resolutions:
- CLAUDE.md: kept both INV-9 (main) and INV-PD-* (pd) invariants
- cmd/root.go: merged LengthCappedRequests counter + DroppedKVAllocations
- sim/bundle.go: added trained-roofline backend + kept disaggregation deciders
- sim/cluster/metrics.go: added LengthCappedRequests field
- docs: merged invariants and results documentation from both branches
- sim/cluster/disaggregation_test.go: added MaxModelLen param to NewModelHardwareConfig calls
- sim/cluster/cluster_event.go: rewrote comment to avoid INV-9 test false positive

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* feat(sim): MaxModelLen enforcement and MaxOutputLen budget (#567) (#579)

Add vLLM-equivalent max_model_len enforcement at three layers:

1. Startup validation: ceil(MaxModelLen/BlockSize) <= TotalKVBlocks
2. Enqueue guard: input >= MaxModelLen rejected (matching vLLM serving.py:1542);
   input + MaxOutputLen > MaxModelLen rejected when client declares budget
3. Runtime stop: force-complete at ProgressIndex >= MaxModelLen (defense-in-depth)

Key design decisions:
- Oracle Knowledge Boundary (INV-9): control plane never reads OutputTokens.
  Uses MaxOutputLen (client budget) or input-only check. Runtime stop handles
  output growth. Verified by behavioral + structural grep tests.
- Auto-derive from HF max_position_embeddings for roofline/crossmodel backends,
  with rope_scaling blacklist (excludes su/longrope/llama3 per vLLM), yarn
  special-case using original_max_position_embeddings, and KV-feasible capping.
- Overflow-safe ceiling division in startup validation (R11).
- R3 validation at CLI (logrus.Fatalf) and constructor (panic).

New tests (12): BC-1 through BC-5, BC-7 conservation with drops, boundary tests
(input==MaxModelLen, exact fit), R3 constructor panic, INV-9 structural enforcement.

Partially addresses #529 (reasoning workload livelock) for roofline/crossmodel.
Blackbox gap tracked in #578.

Closes: #567

Co-authored-by: Claude Opus 4.6 (1M context) <[email protected]>

* feat(latency): MoE-aware roofline latency model (#559) (#561)

* feat(sim): add MoEExpertFFNDim and SharedExpertFFNDim to ModelConfig

Two new fields for MoE-aware roofline: per-routed-expert FFN dimension
and total shared-expert FFN dimension. Both default to 0 (dense model).
Zero-value safe for all existing construction sites (R4 audit: all
dense model configs use zero-valued MoE fields).

Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* feat(latency): parse MoE per-expert and shared-expert dims from HF config

Extends GetModelConfigFromHF to parse moe_intermediate_size,
shared_expert_intermediate_size, and n_shared_experts. Expert count
resolution chain extended to include num_routed_experts (DeepSeek-V3).

Implements BC-15 through BC-18 from the MoE roofline design.
Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* feat(latency): add MoE consistency validation to ValidateRooflineConfig

Validates: experts>0 requires active>0, active<=total, non-negative
MoE dimensions. Catches inconsistent MoE configs at construction time.

Implements BC-12, BC-13, BC-14 from MoE roofline design.
Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(sim): address convergence review findings (I-1, I-2)

I-1: Align SharedExpertFFNDim JSON tag to shared_expert_intermediate_size
     (matches HF config field name convention, consistent with other tags).
I-2: Add negative NumLocalExperts validation in ValidateRooflineConfig
     (R3 compliance — all numeric parameters validated).

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* feat(latency): MoE-aware FLOPs, active weight bandwidth, and smoke tests

MoE FLOPs (Task 4): calculateTransformerFlops now computes routed
(top_k), shared, and gate MLP FLOPs for MoE models. Dense models
use unchanged code path (NumLocalExperts=0 guard).

Active weights (Task 5): calculateMemoryAccessBytes uses top_k
(active experts) for per-step weight bandwidth, matching vLLM's
fused_moe kernel behavior. Includes shared expert and gate weights.

Smoke tests (Task 7): Mixtral-8x7B and DeepSeek-V3 step time smoke
tests plus dense regression anchor (TP=1=12151µs, TP=2=6820µs).

Implements BC-1 through BC-6, BC-10 from MoE roofline design.
Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): use per-expert FFN dim for MoE KV capacity weight estimation

Fixes the critical bug where DeepSeek-V3's general intermediate_size
(18432) was used as per-expert dim (should be 2048), overestimating
MLP weights by ~9× and returning zero usable KV blocks.

Changes:
- KVCapacityParams gains MoEExpertFFNDim and SharedExpertFFNDim fields
- NewKVCapacityParams gains 2 new positional args (R4 enforced)
- computeModelWeightBytes uses per-expert dim when nonzero, falls back
  to IntermediateDim (Mixtral convention)
- ExtractKVCapacityParams propagates new fields, extends expert count
  chain to include num_routed_experts (parity with GetModelConfigFromHF)

Implements BC-7 (per-expert dim fix), BC-9 (param cross-validation),
BC-11 (dense unchanged). Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): convergence review round 2 — R23 parity, documentation, R15

I-1: Align expert count resolution threshold between GetModelConfigFromHF
and ExtractKVCapacityParams. Both now use >1 threshold (single-expert
models are dense-equivalent). Fixes R23 code path parity violation.

I-2: Add precondition comments to calculateTransformerFlops and
calculateMemoryAccessBytes documenting ValidateRooflineConfig requirement.

I-3: Document SharedExpertFFNDim "total dim" semantics — correct due to
SwiGLU linearity (N × (3 × d × e) == 3 × d × (N × e)).

I-4: Add R15 staleness notes to hardening-validation-cleanup-plan.md and
pr2-kv-capacity-auto-calculate-plan.md (NewKVCapacityParams now 6-arg).

I-5: Document active vs total weight distinction in calculateMemoryAccessBytes
to prevent future R23 regression.

Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): align MoE threshold to > 1 across all consumption paths (R23)

Parsing layer already used > 1 (single-expert models are
dense-equivalent). Consumption paths (calculateTransformerFlops,
calculateMemoryAccessBytes, crossmodel isMoE, ValidateRooflineConfig,
computeModelWeightBytes) now use > 1 as well, matching the documented
design intent and resolving the R23 code path parity violation.

Also fixes stale doc comment in ExtractKVCapacityParams ("> 0" → "> 1").

Round 3 convergence review fixes.
Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(cmd): update stale MoE warning, gofmt alignment, R15 crossmodel plan

- cmd/root.go: Replace misleading "assumes dense transformers" warning
  with accurate MoE info message (roofline now models per-expert FLOPs)
- sim/model_hardware_config.go: Run gofmt to fix struct field alignment
- docs/plans/pr472b-crossmodel-backend-plan.md: Add R15 staleness note
  for threshold change (> 0 → > 1)

Round 4 convergence review fixes.
Part of #559

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* refactor(latency): port llm-optimizer single-crossover roofline physics

Replace dual-ceiling model (GEMM + vector ceilings) with single-crossover:
  step_time = max(total_flops / (peak * MFU), total_bytes / peak_bandwidth)

Remove bandwidth haircut (BwEffConstant no longer used in step time).
Remove all overhead terms (TOverheadMicros, PerLayerOverhead, AllReduceLatency).

Keeps BLIS's superior model-awareness: actual IntermediateDim, SwiGLU
3-matrix MLP, MoE support, FlashAttention-aware memory model.

Motivation: BLIS roofline has 215% ITL MAPE vs llm-optimizer's 36.5%.
The dual ceiling + bandwidth haircut + overhead stacking caused ~3x
systematic over-prediction for memory-bound decode steps.

Design: docs/plans/2026-03-09-roofline-llm-optimizer-port-design.md

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* config: update MFU values to llm-optimizer defaults (0.45/0.30)

MfuPrefill: 0.65 → 0.45, MfuDecode: 0.12 → 0.30 for all GPU entries.
These values match llm-optimizer's defaults which achieve 36.5% ITL MAPE
on the sim-to-real evaluation (discussion #522).

Other HardwareCalib fields (BwEffConstant, overheads) remain unchanged
for backward compatibility — they are no longer used by rooflineStepTime()
but may be consumed by other callers.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* docs: add roofline llm-optimizer port design and implementation plan

Design doc: decision record for porting llm-optimizer's single-crossover
roofline physics into BLIS.
Implementation plan: 3 tasks (physics rewrite, MFU update, verification).

Motivation: discussion #522 sim-to-real accuracy validation.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): load weights once per step in roofline (unified forward pass)

vLLM chunked prefill processes all tokens (prefill + decode) in a single
forward pass — weights are loaded from HBM once per step, not once per
phase. The previous implementation loaded weights independently for
prefill and decode phases, doubling the memory-bound term for mixed
batches (~2x over-prediction).

Sources: vLLM V1 blog ("all selected requests are flattened and
concatenated into one long super-sequence for that single forward pass"),
Sarathi-Serve OSDI'24 ("cost of loading model weights from HBM is
amortized across all prompts in a batch").

Adds TestRooflineStepTime_MixedBatch_WeightsLoadedOnce which verifies
the overhead of adding prefill to a decode step is much less than a
full weight load (7µs vs 4166µs).

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): use 2-matrix MLP in roofline FLOPs and weight calculation

Change MLP factor from 3 (SwiGLU gate+up+down) to 2 (up+down) in both
calculateTransformerFlops and calculateMemoryAccessBytes, matching
llm-optimizer's formulation.

For models like Llama-2-70B where IntermediateDim=28672, the 3-matrix
formula produced 31% more MLP weight bytes than llm-optimizer's
2-matrix formula, directly inflating memory-bound decode predictions.

Applies to both dense and MoE paths (routed + shared expert FLOPs/weights).

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* config: bump MFU values to 0.55/0.35 to reduce roofline over-prediction

MfuPrefill: 0.45 → 0.55 (reduces compute-bound prefill/TTFT predictions ~18%)
MfuDecode: 0.30 → 0.35 (reduces near-crossover decode predictions ~14%)

Motivation: after porting llm-optimizer single-crossover physics, BLIS
roofline still over-predicts by ~50% MAPE. Higher MFU reflects observed
H100 tensor core utilization for large prefill GEMMs and batched decode.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): restore SwiGLU 3-matrix MLP, revert MFU bump

Revert MFU values to llm-optimizer defaults (0.45/0.30) — the bump
to 0.55/0.35 went the wrong direction (both models under-predict).

Restore 3-matrix MLP (gate + up + down) for SwiGLU, replacing the
2-matrix formula copied from llm-optimizer. SwiGLU actually has 3
weight matrices that all need HBM loading: this is the physically
correct formula and increases weight bytes by ~37%, which reduces
the under-prediction from ~50% toward the target.

Dense and MoE paths both updated consistently (R23).

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* feat(latency): conditional SwiGLU detection via HiddenAct field

Add mlpMatrixCount() helper that returns 3 for SwiGLU (silu/swiglu/geglu)
or 2 for standard (gelu/relu) MLP. Parsed from HF config's hidden_act
field. Empty defaults to SwiGLU since most modern LLMs use it.

Both calculateTransformerFlops and calculateMemoryAccessBytes now use
nMat instead of hardcoded 3, correctly handling non-SwiGLU models.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): revert to 2-matrix MLP convention matching llm-optimizer

3-matrix with raw intermediate_size over-predicts for models like
Llama2-70B whose intermediate_size (28672) exceeds the standard SwiGLU
(2/3 × 4d) convention. Using nMat=2 matches llm-optimizer's approach
where 2 × d × intermediate ≈ physical weight count for most models.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): remove MoE-specific branches from roofline step time

Roofline now treats MoE models identically to dense (matching
llm-optimizer which has no MoE-specific handling). MoE fields
(NumLocalExperts, MoEExpertFFNDim, SharedExpertFFNDim) are still
used by KV capacity (kv_capacity.go) for GPU memory budgeting.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): MoE roofline scales weights by E, FLOPs by top_k

Mixtral was under-predicted by ~10x because the dense treatment loaded
1 expert's MLP weights instead of all 8. Fix:
- Weight bandwidth: E × MLP weights (all experts loaded from HBM per step)
- FLOPs: top_k × MLP FLOPs (only active experts compute per token)

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): use MoEExpertFFNDim in roofline when set

For DeepSeek-V3 style models where intermediate_size (18432) differs
from per-expert dim (2048), use MoEExpertFFNDim for MoE weight and
FLOP calculations. Falls back to IntermediateDim when unset (Mixtral).

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(latency): address PR #561 review — revert crossmodel scope, fix docs

- Revert crossmodel MoE threshold from > 1 back to > 0 (scope violation:
  crossmodel behavioral change doesn't belong in a roofline PR)
- Fix design doc table and CLI comment claiming roofline models shared
  experts and gate FLOPs (it doesn't — only KV capacity does)
- Fix HiddenAct comments that incorrectly claim it selects 3-matrix vs
  2-matrix MLP (mlpMatrixCount always returns 2)
- Document intentional 2-matrix (roofline) vs 3-matrix (KV capacity)
  design choice with cross-references in both files

Co-Authored-By: Claude Opus 4.6 <[email protected]>

---------

Co-authored-by: Claude Opus 4.6 <[email protected]>
Co-authored-by: Srinivasan Parthasarathy <[email protected]>

* fix(sim): PR #567 follow-up — validation gaps, LengthCappedRequests counter, INV-9 extension (#580) (#587)

- Add negative MaxModelLen validation in NewSimulator (BC-1: defense-in-depth for struct literal bypass)
- Add LengthCappedRequests metric counter across 5-file pattern (BC-2, BC-3, BC-4)
- Add end-to-end sim.Run() test for BC-5 runtime length cap path
- Extend INV-9 structural test to scan sim/cluster/ control-plane files (BC-6)
- Add negative MaxOutputLen validation in EnqueueRequest (BC-7: R3 gap)
- Add gemma3 model_type exclusion for rope_scaling (BC-9: matches vLLM)
- Add rope_scaling parse-failure warnings for malformed HF configs (BC-8)
- Fix kvFeasibleMax comment accuracy (blockSizeTokens is configurable, not 16)

Fixes #580

Co-authored-by: Claude <[email protected]>

* refactor(sim): remove dead HardwareCalib fields — BwEffConstant, TOverheadMicros, PerLayerOverhead, AllReduceLatency (#596)

These fields became dead code after the roofline physics port (llm-optimizer
single-crossover model). No runtime code path reads them; ValidateRooflineConfig
enforced BwEffConstant > 0 on a value nothing consumed. Removing them eliminates
config-file clutter and prevents future contributors from assuming they're active.

Fixes #590

Co-authored-by: Claude Opus 4.6 <[email protected]>

* Configure claude on  GH Actions (#600)

Signed-off-by: Jing Chen <[email protected]>

* Enable claude on PRs (#601)

Signed-off-by: Jing Chen <[email protected]>

* ignore training and actions runner (#607)

Signed-off-by: Srinivasan Parthasarathy <[email protected]>

* fix(sim): PR #580 deferred items — rope_scaling extraction, MaxModelLen int64, tests, docs (#606)

Complete 7 deferred hardening items from issue #580 (PR #587 handoff):

1. Extract applyRopeScaling as a pure function with 26 table-driven test
   cases covering blacklist (su/longrope/llama3), mrope fall-through,
   gemma3 substring match (handles text_config pivot), yarn original base,
   overflow guards, NaN/Inf defense, degenerate inputs.

2. Change MaxModelLen from int to int64 for consistency with ProgressIndex,
   TotalKVBlocks, BlockSizeTokens. Updates 6 type sites, removes redundant
   int64() casts, adds int64() widening at EnqueueRequest comparison sites.

3. Add cluster-mode MaxModelLen drop test (BC-6): Guard 1a (input >= limit)
   and Guard 1b (input + budget > limit), INV-1 conservation, inFlightRequests
   drain, Metrics.Requests map cleanup.

4. Add chunked prefill + MaxModelLen interaction test (BC-7): verifies no
   spurious force-completion during multi-chunk prefill (TotalOutputTokens=49,
   LengthCappedRequests=0, TTFT recorded).

5. Add glossary entries for MaxModelLen and Oracle Knowledge Boundary (INV-9).

6. Refine rope_scaling documentation with explicit blacklist details.

7. Fix pre-existing gemma3 bug: ParseHFConfig's text_config pivot overwrites
   model_type from "gemma3" to "gemma3_text", making the exact-match check
   dead code. Changed to strings.Contains to match vLLM's substring semantics.

Related to #580. Discovered issues: #602, #603, #604, #605.

Co-authored-by: Claude <[email protected]>

* feat(latency): add trained-roofline backend with roofline basis functions × learned corrections (#616)

* feat(latency): register trained-roofline backend name (BC-1)

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* feat(latency): add PostDecodeFixedOverhead to interface + implement TrainedRooflineLatencyModel (BC-3,6,7,8,9,11,15)

- Add PostDecodeFixedOverhead() int64 to LatencyModel interface
- Existing backends (blackbox, roofline, crossmodel) return 0
- Simulator recordRequestCompletion adds PostDecodeFixedOverhead to E2E
- TrainedRooflineLatencyModel: 6 roofline basis functions with learned corrections
- Zero heap allocations in StepTime (19ns/op, 0 allocs/op)

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* feat(latency): wire trained-roofline factory in NewLatencyModel (BC-2,13,14)

- Full validation: TP, NumLayers, NumHeads, HiddenDim, IntermediateDim,
  TFlopsPeak, BwPeakTBs, NumHeads%TP, NumKVHeads%TP, 7 beta coefficients
- Derives architecture features at construction: headDim, dKV, dFF, kEff
- Table-driven error tests for all validation paths

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* test(latency): add monotonicity behavioral tests for trained-roofline (BC-4,5)

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* feat(latency): add trained-roofline defaults + CLI loading (BC-10,12)

- Add trained_roofline_defaults section to defaults.yaml with 7 betas + 3 alphas
- Add TrainedRooflineDefaults struct to cmd/default_config.go
- CLI handling: 4 sites in cmd/root.go (loading block, zero-coefficients guard,
  HFConfig parsing, help text)

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* docs: add trained-roofline to latency model documentation

- CLAUDE.md: "Four modes", file tree, Key Data Flow
- sim/config.go: Backend field comment
- sim/latency/latency.go: package doc
- docs/concepts/core-engine.md: "four latency model backends"
- docs/concepts/glossary.md: "Four modes" + trained-roofline description
- Plan committed alongside implementation

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* fix(sim): guard PostDecodeFixedOverhead for zero-output requests + fix ITL contamination

- PostDecodeFixedOverhead only applied when len(OutputTokens) > 0
- RequestITLs computed from itlSum directly (not lat-FirstTokenTime) to
  avoid contaminating per-token average ITL with fixed overhead
- Add zero-alpha warning for trained-roofline CLI path

Caught by code review Step 4.5.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* docs(guide): comprehensive trained-roofline section in latency models guide

- Add trained-roofline section with formula, alpha model, accuracy caveats
- Update comparison table to 4 backends
- Update recommendation: trained-roofline is now the default for new models
- Update pluggable architecture to show 4 interface methods
- Fix cross-model description accuracy

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* fix: convergence Round 1 fixes — extension recipe, slice copy, zero-alloc test, config ref

- Extension recipe: 3→4 methods, added bundle.go + CLI wiring touch points,
  added trained-roofline as 4th example
- Factory: defensive copy of beta/alpha slices to enforce "frozen" contract
- Test: add TestTrainedRoofline_StepTime_ZeroAllocs using testing.AllocsPerRun
- Configuration reference: add trained-roofline to --latency-model flag description

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* docs: add trained-roofline to quickstart + document non-blocking overhead pattern

- Quickstart: add trained-roofline example (recommended for new models)
- recordRequestCompletion: document that E2E includes non-blocking
  PostDecodeFixedOverhead and OutputTokenProcessingTime, explaining why
  RequestCompletionTimes exceeds RequestLeftEvent timestamp

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* docs: self-audit — update models.md, roofline.md, tutorial.md for trained-roofline

All documentation working copies now mention trained-roofline consistently.
Source-of-truth map: 12/12 working copies updated.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <[email protected]>

* feat(sim): populate MaxOutputLen on all workload paths + engine auto-fill (#621)

* feat(sim): add MaxOutputLen auto-fill in EnqueueRequest (BC-1..BC-4)

- Auto-fill MaxOutputLen = maxModelLen - len(InputTokens) when client
  omits budget (MaxOutputLen==0) and maxModelLen > 0
- Mirrors vLLM input_processor.py:554 safety cap
- No auto-fill when client sets budget (BC-2), unlimited mode (BC-3),
  or input exceeds context (BC-4)

Refs: #572

Co-Authored-By: Claude <[email protected]>

* feat(workload): set MaxOutputLen on all request construction sites (BC-5..BC-7)

- generator.go: MaxOutputLen = len(outputTokens) (synthetic/multimodal)
- replay.go: MaxOutputLen = len(outputTokens) (trace v2 replay)
- reasoning.go: MaxOutputLen = len(outputTokens) (multi-turn reasoning)
- Matches inference-perf pattern: max_tokens = sampled output length

Fixes #572

Co-Authored-By: Claude <[email protected]>

* docs(sim): update EnqueueRequest doc comment for auto-fill preprocessing

Co-Authored-By: Claude <[email protected]>

* docs(test): update stale MaxOutputLen=0 comments for auto-fill semantics

- Three tests referenced 'input-only check' for MaxOutputLen=0
- After auto-fill, MaxOutputLen is set to maxModelLen - input
- Tests still pass numerically; comments now reflect actual behavior

Co-Authored-By: Claude <[email protected]>

---------

Co-authored-by: Claude <[email protected]>

* docs: switch default example model to public Qwen/Qwen3-14B (#608)

* docs: switch default example model to public qwen/qwen2.5-7b-instruct

Replace gated meta-llama/llama-3.1-8b-instruct with publicly available
qwen/qwen2.5-7b-instruct in all user-facing docs (README, quickstart,
tutorial, guides, reference, CLAUDE.md, CONTRIBUTING.md). Roofline/crossmodel
examples now work without HF authentication.

Set qwen default TP=1 in defaults.yaml so examples use the default without
explicit --tp flags. Update KV block count, coefficient examples, and prose
references to match TP=1 values.

Fixes #545

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* chore(defaults): update vllm version to v0.11.0 for 4 models (H100 TP=1)

Update default and trained-coefficient vllm_version for
qwen2.5-7b-instruct, qwen3-14b, llama-3.1-8b-instruct, and
qwen2.5-3b-instruct to vllm/vllm-openai:v0.11.0.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* docs: switch default example model from qwen2.5-7b to qwen3-14b

Qwen3-14B (Qwen/Qwen3-14B) is a newer, publicly available model with
pre-trained coefficients already in defaults.yaml. Update all
documentation examples and references accordingly.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix: address review comments — stale refs, tutorial throughput

- Fix "LLaMA 3.1 8B" comment in experimentation.md (issue #3)
- Update stale llama-3.1-8b/132,139 refs in configuration.md (issue #4)
- Recalibrate tutorial for qwen3-14b throughput: ~2.5 req/s per
  instance, target 20 req/s (was 57 req/s / 500 req/s for llama)
- Scale experimentation.md example to match (20 req/s, not 400)

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* docs: add HF_TOKEN tip to quickstart and README for gated models

Roofline/trained-roofline/crossmodel modes auto-fetch from HuggingFace,
which fails for gated models without authentication. Add a lightweight
tip after the first roofline example in both files recommending HF_TOKEN
for gated model access and rate limit avoidance.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

---------

Co-authored-by: Claude Opus 4.6 <[email protected]>

---------

Signed-off-by: Jing Chen <[email protected]>
Signed-off-by: Srinivasan Parthasarathy <[email protected]>
Co-authored-by: Srinivasan Parthasarathy <[email protected]>
Co-authored-by: Claude Opus 4.6 (1M context) <[email protected]>
Co-authored-by: Dipanwita Guhathakurta <[email protected]>
Co-authored-by: Jing Chen <[email protected]>