hf2q

Pure-Rust CLI for converting HuggingFace models to hardware-optimized formats — and serving them through an OpenAI-compatible HTTP API on Apple Silicon. No C++ at build, test, or runtime (ADR-008 sovereignty rule); the inference path runs entirely on mlx-native Metal kernels we own end-to-end.

Serving reliability is part of correctness. The canonical SlotAware launchers for native Qwen, Gemma, and DeepSeek give each agent an independent full logical context while sharing model weights. Qwen SlotAware prefill advances in bounded GPU transactions so active streams can decode and cancellation can be observed between chunks. A fatal Metal command-buffer/watchdog/ignored-submission error, or an independently observed transaction deadline that never returns, fails the affected Qwen, Gemma, or DeepSeek worker closed; the process must be recreated rather than submitting more work to a poisoned queue. See Full-context agentic serving, the shipping contract, and the family ADRs for the exact supported surface and current evidence.

# Convert a HuggingFace model to a Q4_K_M GGUF (auto-downloads via --repo)
hf2q convert \
  --repo google/gemma-4-26b-it \
  --quant q4_k_m \
  --output models/gemma-4-26b-it-q4_k_m/out.gguf

# Serve it over an OpenAI-compatible HTTP API
hf2q serve --model models/gemma-4-26b-it-q4_k_m/out.gguf --port 8080

What it does

hf2q is two tools fused into one binary:

1. A conversion pipeline. Read HuggingFace config.json + *.safetensors, normalize tensor names per architecture, run quantization (legacy block Q4_0 / Q8_0, K-quants Q{2..6}_K_{S,M,L}, imatrix-weighted K-quants including imatrix-adaptive, or mixed-bit dynamic-quant-*) and emit GGUF or mlx-lm safetensors. No llama.cpp (the pinned upstream GGUF engine, referred to throughout as "the peer" — see NOTICE) or candle is involved at build, test or runtime (ADR-008 — "candle divorce"; sovereignty rule in docs/arch-onboarding.md).

2. An inference + serving engine. Load a GGUF, run prefill + speculative-or-vanilla decode on the GPU via mlx-native, expose it through an OpenAI-style /v1/chat/completions, /v1/embeddings and /v1/models HTTP API. Supports tools / function-calling, streaming SSE, vision (qwen3vl), grammar-constrained sampling, and a persistent block-prefix KV cache.

Supported architectures today: Gemma 4 (dense + MoE), Qwen 3.5 / 3.6 (dense + MoE + multi-token-prediction), Qwen 3.8-27B text (dense + multi-token-prediction), DeepSeek-V4-Flash-0731 (compressed-attention MoE), Qwen 3-VL (vision + text), and BERT / Nomic-BERT (embedding-only). Each lives under a single src/inference/models/<arch>/ module — the arch-registry (src/arch/) is the single source of truth for tensor catalogs, quality thresholds, smoke prompts and MTP/vision flags.

Install

hf2q is a Cargo crate. Apple Silicon is currently the only supported target — the inference path is Metal-only.

git clone git@github.com:robertelee78/hf2q.git
cd hf2q
GIT_COMMIT_SHA="$(git rev-parse HEAD)" cargo build --release --locked
./target/release/hf2q --help
./target/release/hf2q setup

The explicit commit identity is required for immutable remote-conversion receipts when building from a checkout. Published packages and future native release artifacts embed their own source identity.

The source tree now contains the reviewed standalone installer, updater, rollback, data-preserving uninstaller, and protected signed-byte release rail. The rail builds a locked packed-source candidate, signs and notarizes its exact bytes, and publishes only that verified candidate through a complete draft release. Model/cache/performance qualification is separate and remains owned by the relevant model and serving decisions. The standalone channel is not advertised yet: https://hf2q.us/install.sh remains unavailable until the credentialed candidate, exact public binary, and stable release record pass their installed-artifact gates. Source/Cargo installs remain unmanaged by hf2q update.

hf2q setup inventories the selected Apple-Silicon host and records defaults that the existing convert and serve commands consume. It downloads, converts, loads, and serves nothing. Complete automation is non-interactive:

hf2q setup --accept-defaults

The canonical Qwen3.8 guide profile records Q4_K_M conversion, localhost port 8081, and inflight-batched serving with one active slot. Interactive setup can change those choices for another workload or model family. Explicit command flags still win, and existing scheduler environment overrides retain their precedence. Use the global --state-root /absolute/path option for a custom config root and pass the same option to later convert or serve commands. See docs/setup.md for the exact prompts, schema, precedence, filesystem, and failure contract.

The exact mlx-native declaration in Cargo.toml resolves from crates.io. For local mlx-native development place a path override in a gitignored .cargo/config.toml (template at Cargo.toml:217+) — out-of-the-box cargo build does NOT path-pin to a sibling checkout.

cargo build requires:

• macOS with Metal Performance Shaders (M1 or newer).
• A working Rust toolchain at the version pinned in Cargo.toml (rust-version = "1.88.0").
• Per-arch disk floor for convert (src/arch/entries/): 100 GB for Qwen 3.5 dense, 150 GB for Qwen 3.5 MoE. Smoke preflight refuses to start below disk_floor_gb + 10.

For the complete first-run path, use Get started with hf2q and Qwen3.8. It pins jenerallee78/Qwen3.8-27B-Abliterated-SFT to an exact source revision and walks through native Q4_K_M conversion, text serving, direct API use, and an optional OpenCode connection. It does not introduce a second onboarding or model-preparation workflow around the existing commands.

hf2q doctor enumerates the runtime checks (hardware detection, disk space, optional RuVector backend); run it after cargo install if anything misbehaves.

Shell completion is explicit. Generate the current clap grammar with hf2q completions --shell <bash|zsh|fish> and install or source that output through the shell or package manager that owns it. Ordinary hf2q invocations, including --help, --version, and setup, do not write completion files or shell startup configuration. Generated scripts are static snapshots, so regenerate them after upgrading when a new command such as chat is added.

CLI subcommands

Run hf2q <command> --help for the full flag surface.

Quantization variants

The hf2q convert pipeline accepts two families of --quant <name> values, parsed via QuantSelector::from_name:

Reserved names surface as typed errors with actionable hints: --quant dwq → "reserved for the future DWQ-train pipeline"; --quant apex (unqualified) → suggests apex-balanced etc.; --quant tq1_0/tq2_0 → "recognized ftype but out of v1 scope".

Quick start: convert + serve a model

The hf2q convert pipeline reads a Hugging Face model directory (config.json + safetensors + tokenizer assets) and emits a single GGUF that loads in the stock peer engine and in hf2q serve. The source can be an explicit local path or a canonical Hub repository ID/URL. Remote conversion uses hf2q's in-process hf-hub client, resolves a branch or tag to one exact commit before transferring files, verifies the exact index-selected source inventory, and writes a schema-v3 conversion receipt. It never invokes Python, hf, or huggingface-cli.

At serve time, Qwen3.5/Qwen3.6 reads its tokenizer and chat template from the GGUF metadata; a sibling tokenizer.json is not required. Vision uses a separate projector GGUF. Compatible externally produced text/projector pairs are accepted from standard architecture, multimodal-token, profile, width, tensor, and forward-warmup checks. When exact source or artifact hashes are present, hf2q additionally requires those identities to match.

When serve --mmproj successfully binds that projector to the loaded chat model, /v1/models advertises input_modalities: ["text", "image"] and the attached vision_projector on the chat-model row. The projector is not a separate selectable language model. Stock OpenCode custom-provider entries still need the equivalent local modalities.input: ["text", "image"] declaration because its generic OpenAI-compatible provider does not infer custom model capabilities from /v1/models.

# 1. Resolve, download, verify, and convert the official Hub source to
# Q5_K_M. Streaming convert keeps peak memory ~5 GB
#    even on a 48 GB-source 26 B-param model. ~8-15 min on M-series.
hf2q convert google/gemma-4-26b-a4b-it \
  --quant q5_k_m \
  -o ./out/gemma4-26b-q5_k_m.gguf

# `--repo` is retained as a compatibility spelling for the same native path.
hf2q convert --repo google/gemma-4-26b-a4b-it \
  --quant q5_k_m \
  -o ./out/gemma4-26b-q5_k_m.gguf

# To convert an existing local checkout, use an existing path or make the
# local intent explicit with ./, ../, or an absolute path.
hf2q convert ./models/google-gemma-4-26b-a4b-it \
  --quant q5_k_m \
  -o ./out/gemma4-26b-q5_k_m.gguf

# 2a. Test load with the stock peer engine (single-shot generation):
llama-cli -m ./out/gemma4-26b-q5_k_m.gguf \
  -p "What is the capital of France?" -n 64 --temp 0 --seed 42

# 2b. Serve with hf2q's OpenAI-compatible HTTP API:
hf2q serve --model ./out/gemma4-26b-q5_k_m.gguf --port 8080

# 3. Use it (OpenAI SDK works out of the box)
curl -X POST http://localhost:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"gemma4","messages":[{"role":"user","content":"hello"}]}'

Full-context agentic serving

The native Gemma 4, Qwen 3.6/3.8, and DeepSeek-V4 workers are intended for OpenAI-compatible coding clients such as OpenCode. Their canonical launchers default to four independent agent slots. Every slot receives the complete configured logical context; model weights are shared, while KV, recurrent state, token ledgers, template state, and tool-call state remain isolated per conversation. One shared physical KV budget governs demand-grown residency—it never divides the advertised context by the slot count.

Start the launcher for the model family you want to serve:

# Gemma 4 (default port 8082)
./scripts/serve_gemma4_opencode.sh

# Qwen 3.6 (default port 8081)
./scripts/serve_qwen36_opencode.sh

# Qwen 3.8-27B multimodal (default port 8081)
./scripts/serve_qwen38_opencode.sh

# Disable the launcher's exact, cost-gated MTP/history speculation if needed
QWEN38_SPECULATION=off ./scripts/serve_qwen38_opencode.sh

# DeepSeek-V4 (default port 8081; run one large family at a time)
./scripts/serve_deepseek4_opencode.sh

# A different explicitly supported GGUF can be served without hf2q provenance:
MODEL=./out/DeepSeek-V4-Flash-0731.gguf PORT=8090 \
  ./scripts/serve_deepseek4_opencode.sh

curl http://127.0.0.1:8081/v1/models

For DeepSeek-V4 through OpenCode's generic OpenAI-compatible provider, use an explicit agent profile instead of relying on client defaults. The locally validated starting point is temperature=0.55, top_p=0.95, and the model's max reasoning variant:

{
  "agent": {
    "build": {"temperature": 0.55, "top_p": 0.95, "variant": "max"},
    "plan": {"temperature": 0.55, "top_p": 0.95, "variant": "max"}
  },
  "provider": {
    "local": {
      "models": {
        "Deepseek v4 Flash 0731 Source": {
          "reasoning": true,
          "interleaved": "reasoning_content",
          "variants": {"max": {"reasoningEffort": "max"}}
        }
      }
    }
  }
}

hf2q accepts reasoning_effort (low, high, or max) directly on a DeepSeek chat-completion request and retains the older chat_template_kwargs.reasoning_effort form for compatibility. A supplied integer seed now drives a decode-step-indexed deterministic sampler; identical rendered prompts and sampling settings reproduce across worker threads.

The canonical getting-started guide uses the selected text checkpoint jenerallee78/Qwen3.8-27B-Abliterated-SFT. Create it natively from its immutable source revision before using that launcher:

mkdir -p "$HOME/.local/share/hf2q/models/qwen3.8"
hf2q convert jenerallee78/Qwen3.8-27B-Abliterated-SFT \
  --revision 08c2f075b43bc06456382db6b918a3dcabdcf4dd \
  --quant q4_k_m \
  --output "$HOME/.local/share/hf2q/models/qwen3.8/Qwen3.8-27B-Abliterated-SFT-Q4_K_M.gguf"

See the getting-started guide for the complete convert, serve, API, and OpenCode sequence. This first path is text-only; Qwen3.8 vision remains a separate candidate surface.

Native text conversion and serving are accepted. Vision is a separate candidate surface: hf2q converts the 333-tensor tower into a paired projector, binds it to the text architecture and width, and runs the production image graph before language-model admission. The exact candidate has passed unary and SSE image chat, two-image ordering, an image-driven tool call and tool-result continuation, same-image single-flight/cache reuse, disconnect isolation, and the official 4,096 by 4,096 processor maximum. Vision is not yet performance-accepted—the official-maximum cold path remains materially slower than desired—so those results are candidate evidence, not a release-wide speed claim. Speculative MTP decode remains disabled and outside the accepted server surface.

Foreground launchers use the live operator dashboard automatically when stderr is an interactive terminal. Runtime work stays in place instead of forming a log wall: each request shows its slot and phase, cached/new prompt tokens, prefill percentage and ETA, and decode rate. Use --operator-ui plain for the traditional log stream, or --operator-ui dashboard to require the dashboard and fail early when the terminal cannot support it. Pipes, CI, services, and --log-format json remain plain and machine-readable automatically.

Point the client's OpenAI-compatible base URL at the selected launcher's http://127.0.0.1:<port>/v1 endpoint and select the model ID returned by /v1/models (normally the GGUF file stem). Set MAX_SLOTS=1 for one agent or MAX_SLOTS=8 for an eight-slot hardware experiment; four is the release-validated default. DeepSeek's CONTEXT_LEN override changes the full logical context of each slot; Gemma and Qwen use the context declared by their GGUF. KV_CACHE_BUDGET_BYTES independently caps aggregate physical KV high-water. Requests that cannot safely fit wait or fail explicitly instead of silently receiving a shorter context.

Use /readyz, not merely /health or /v1/models, as the generation readiness probe. /health is process liveness. Present by public 0.1.5 and strengthened in the 0.1.6 candidate, a fatal Metal command-buffer/watchdog/ignored-submission error (including device-loss reports), or an independently observed transaction deadline, terminates every active and queued request for the affected Qwen, Gemma, or DeepSeek worker once, rejects new work with HTTP 503, and keeps /readyz unavailable. A slow SSE consumer is cancelled locally instead of blocking other slots. A supervisor must recreate the process/device generation; an in-process slot reset is not safe recovery from a poisoned Metal queue.

Qwen3.5/Qwen3.6/Qwen3.8 SlotAware chat uses at most 2,048 prompt tokens per GPU prefill transaction. Active decoders run before the next cold transaction, multiple cold prompts rotate fairly, and cache/ledger state advances only after the verifier full-attention cursors agree. Ordinary prefixes may omit speculative metadata; a Qwen3.8 prefix that advertises reusable MTP state must have equal target and MTP cursors at the exact published token count. The canonical Qwen3.8 launcher enables exact, independently cost-gated fixed-K3 MTP and request-history lookup with QWEN38_SPECULATION=auto; unsupported request semantics remain on ordinary target decode. Run scripts/qwen38_speculation_ab.sh for the exact-output OFF/AUTO performance gate. For a bound Qwen3.8 projector, image requests carry soft-token embeddings, the explicit DeepStack layout, and 3D positions through the same scheduler-yielding prefill state; the payload is validated before its first GPU transaction. Image-bearing KV remains exact-image isolated. A later first-image turn may reuse only the causally earlier text-only snapshot when every soft/DeepStack position is in the suffix and all four cached mRoPE axes are proven to be ordinary text.

The canonical Qwen launcher also bounds a still-open reasoning span at 2,048 tokens and continues decoding the answer. For smaller max_tokens values the default adapts to retain answer capacity. Set THINKING_TOKEN_BUDGET=0 to disable this policy, or send the vLLM-compatible thinking_token_budget field per request. Qwen3-VL remains a distinct model family rather than an approximate fallback through another Qwen text family.

Long Gemma 4 text prefills use 4,096-token transactions and split at the stable-prefix boundary. Decode runs before each Mixed prefill step, and all configured HB, hybrid, dense, and MLX per-slot cursors are committed only after the complete transaction succeeds. Cross-slot cold and retained-prefix batches share the same 4,096-row aggregate Metal-transaction ceiling; lanes over that bound remain FIFO and return to scheduler-backed resumable states. When several compatible long-text states are installed, one transaction shares the 4,096 rows across those lanes instead of multiplying the bound by the number of slots. The 4,096-token ceiling is present by public 0.1.5 and must pass exact fresh-versus-reused bounded real-model parity again from the packed 0.1.6 candidate before release; it is not inherited from Qwen. Long Gemma soft-token prefill remains fail-closed until it has a resumable graph.

On the target M5 Max host, the launcher defaults to the schema-v2, source-bound deepseek4-agentic-q2 reproduction that passed the strict coherence, throughput, tool-use, and long-prefix cache gates. It enables operator progress telemetry and rejects unsafe port or memory state before mapping the approximately 100 GiB model. When a competing process exceeds the 8 GiB RSS ceiling, the macOS launcher refines that value with physical footprint so reclaimable WebKit/IOAccelerator mappings do not create a false positive. If the probe is unavailable or malformed, the original RSS upper bound remains authoritative and the launcher still fails closed.

Unary and streaming chat completions support reasoning content, OpenAI tools, required/automatic tool choice, parallel DSML invokes, cancellation, and usage telemetry. Growing transcripts reuse the live native KV/recurrent prefix; DeepSeek's old-reasoning canonicalization restores a prompt-tail checkpoint, so normal agent turns do not prefill the full context again. DeepSeek serving is slot-aware and uses bounded admission/decode waves so several agents make progress without duplicating model weights. Embeddings and multimodal messages remain unsupported for DeepSeek and fail explicitly rather than selecting another family or runtime.

DeepSeek production prefill now uses the exact gathered-attention path for every nonempty prompt and retained-prefix suffix. The older dense kernel is a diagnostic oracle only: it produced a different, incoherent first-token ordering on stock-client structured-tool prompts and did not provide a speed advantage. Native template rendering emits the model's canonical JSON spacing, and the DSML grammar accepts both that spaced form and compact JSON while still rejecting null for required strings. This covers nested question and todowrite payloads in required and automatic tool-choice modes, including recovery after prior invalid calls, SSE, and tool-result continuation.

The canonical launcher does not inject a repetition penalty when the client omits one (HF2Q_DEFAULT_REPETITION_PENALTY=1.0). The former hidden 1.05 default distorted constrained strings and did not stop client-side action loops. Set REP_PENALTY only for a measured workload; an explicit request value still wins.

With an already-running DeepSeek server, run the focused real-model gate:

BASE_URL=http://127.0.0.1:8081 \
  ./scripts/test_deepseek4_structured_tools.sh

The gate defaults to three temperature-0.55 repetitions per required and automatic question/todowrite case, validates meaningful non-null nested strings, exercises two-prior-failure recovery, checks SSE and continuation, and requires retained-prefix cache use. An isolated stock OpenCode 1.18.18 coding gate additionally completed a Rust edit and same-session continuation without a repeated tool loop. These are candidate-source and local-artifact proofs; publication still requires the protected exact-package hardware gate.

DeepSeek cold and meaningful retained-prefix suffix work advances at native atomic verifier boundaries. At most two cold prefills own the single scratch arena concurrently. In a lopsided cohort with a runnable decoder, mixed work caps the next prefill slice at two 128-token native windows and runs up to the normal eight-token decode quantum before the next slice. When a filling cold cohort still has another cold request queued, unary cold-wave decode is deferred through the draining phase while any cold prefill remains because its output cannot be delivered before the cohort barrier; full 2,048-token prefill transactions resume. Streaming and warm decode remain responsive. Once no runnable visible decoder remains, prefill also returns to the full transaction. If a decoder becomes terminal, completion stays parked until the barrier lifts so its physical cache cannot be reused before a tool-result continuation. Cached-suffix work is not counted as cold-cohort work. With no cold barrier active, staggered warm work may join an existing decoder whenever another physical slot is free. Cancelling a cached suffix rolls back to a valid, position-consistent pre-request turn anchor; poisoned or inconsistent state still resets fully.

When two to four compatible warm cached suffixes are already runnable, their sequence-local attention and cache writes remain independent while the layer-local FFN/MoE runs once across at most 2,048 aggregate rows. hf2q never waits to form this cohort and never skips an older incompatible request. Cold, mixed, recovery-tail, final-head, and decode work retain the serial path. The release gate requires B=2/3/4 exact state, logits, and subsequent-token parity, an alternating-order N=4 median speedup under Nominal thermals, and an observed cooperative transaction in both full four-agent waves.

After prefill drains, pure decode advances in 64-token slot quanta to amortize session swaps and scheduler publication across a full cohort. This does not widen genuinely mixed work: a runnable decoder beside prefill remains clamped to the eight-token/two-window interactive budget above.

Large DeepSeek MoE prefills also pair the routed expert gate and up projections through the family-neutral schedule primitive introduced in mlx-native 0.10.9 and retained by the pinned mlx-native 0.11.0. That primitive constructs the expert routing schedule once, then encodes the two existing quantized projections; it is not a new approximate arithmetic kernel. Decode-sized work, forced matvec/slotted diagnostics, calls without scratch, and threshold-override diagnostics retain the independent projection path. This is a candidate prefill optimization until the exact packed hf2q hardware gates below prove end-to-end quality and latency; the native primitive's focused benchmark is not a substitute.

The calibrated DeepSeek release envelope measures macOS thermal state through the four atomic cold receipts, which is the phase that exercises large prefill. It does not pause or reorder the agents: cached requests may still overlap the cold tail exactly as in the frozen workload. The same live server then completes cached unary/SSE, automatic tool choice, and tool-result continuation under their unchanged latency and semantic limits. Receipt names and hashes bind the thermal boundary; any non-Nominal sample before all four cold receipts still fails closed.

scripts/test_deepseek4_cached_suffix.sh is the focused Apple-Silicon gate for that contract. It overlaps a three-transaction cached tool-result suffix with a live SSE decoder, then disconnects a separate cached suffix at transaction three and requires bounded stop, one cancellation count, no terminal Done, post-cancellation prefix reuse, readiness, and a clean fatal-log delta. Its focused receipt complements rather than replaces the unchanged four-agent agentic gate.

scripts/run_deepseek4_matched_peer.sh is the developer-only same-input peer discriminator for the frozen four-agent cold workload. It starts a fresh pinned peer for each wave, disables prompt caching, binds binary/model/ fixture/request identity, requires exact read_file semantics and zero-cache usage, and records monotonic response/cohort timing under continuous AC and thermal telemetry. The peer renders the byte-identical request as 6,695 prompt tokens versus hf2q's insertion-ordered 6,684, so both runtime-specific counts remain explicit. The script is reference evidence only; it never participates in production serving and cannot replace hf2q's exact packed-artifact cache gate.

The Qwen watchdog acceptance scripts are reproducible operator gates, not startup defaults. Existing receipts are causal local dependency-spike evidence; they are not final hf2q distribution authority. Changes to those model or serving paths require rerunning the applicable gates from a clean hf2q package resolving published mlx-native 0.11.0:

• scripts/test_qwen36_prefill_watchdog.sh enqueues the deterministic 552-token SSE lane immediately before the public 87,972-token/347-tool lane, requires decode-first progress, and validates the exact 44-transaction stable-boundary plan plus the complete tool/SSE response.
• scripts/test_qwen36_prefill_cancellation.sh runs with MAX_SLOTS=1, drops the long stream at a transaction boundary, and proves exact slot reuse.
• scripts/test_qwen36_watchdog_harness_contract.sh is the model-free negative test for the receipt parser.
• scripts/test_deepseek4_interactive_overlap.sh pairs a short decoder with the public 347-tool cold prompt, requires an eight-token interactive quantum before a legacy 2,048-token turn can monopolize the worker, and validates the complete long tool/SSE result under an uninterrupted AC-power window.
• scripts/test_agentic_cache_lifecycle.sh is the unchanged cross-family cache gate. Against a fresh Qwen, Gemma, or DeepSeek process it creates a long tool conversation, queues an exact retry while the strongest retained prefix is active, cancels the owner, requires the retry to reuse the restored checkpoint, and checks that an unrelated conversation cannot inherit private history. Run it once per process; never co-reside the large family artifacts on a 128 GiB host.
• scripts/run_agentic_cache_release_gate.sh is the model-qualification wrapper used by the manual Cache lifecycle workflow. It consumes the exact signed standalone candidate, runs DeepSeek, Gemma, Qwen, and the Qwen3.8 short/long decode discriminator sequentially under continuous AC and caffeinate guards, verifies each GGUF against a protected SHA-256, and emits a source/crate/binary/model-bound qualification manifest. Publication does not consume that manifest. Its calibrated four-slot Gemma waves run before the long overlap/lifecycle soak, retain the default latency limits, and bind continuous Nominal thermal telemetry across every cold, cached, automatic-tool, and tool-result turn.

The governing decisions and the old-failure-versus-final-artifact distinction are recorded in docs/adr/ADR-019-mlx-native-encoder-architecture.md, docs/adr/ADR-027-qwen35-tq-kv-cache-and-persist-family.md, and docs/adr/ADR-040-continuous-batching-reopen.md.

Test the 0.1.7 serving release

Build and verify the exact checkout before loading a model:

cargo check --locked --all-targets --all-features
cargo build --release --locked
cargo audit

# These are the focused serving contracts. CI also runs the library,
# conversion, LCP, fixture, readiness, and parser-negative suites listed in
# .github/workflows/ci.yml.
cargo test --locked --bin hf2q --all-features \
  qwen35_bounded_prefill_watchdog_tests -- --test-threads=1
cargo test --locked --bin hf2q --all-features \
  prompt_cache_ -- --test-threads=1
cargo test --locked --bin hf2q --all-features \
  gemma4_bounded_prefill_tests -- --test-threads=1
cargo test --locked --bin hf2q --all-features \
  engine_supervisor::tests -- --test-threads=1
cargo test --locked --bin hf2q --all-features deepseek4 -- \
  --skip real_artifact_tests
bash scripts/test_qwen36_watchdog_harness_contract.sh

Then start exactly one family from the same checkout. Setting HF2Q_BIN prevents a launcher from accidentally selecting an older repository build:

# Choose one launcher and leave it in the foreground.
HF2Q_BIN="$PWD/target/release/hf2q" ./scripts/serve_qwen36_opencode.sh
HF2Q_BIN="$PWD/target/release/hf2q" MMPROJ=/nonexistent \
  ./scripts/serve_gemma4_opencode.sh
HF2Q_BIN="$PWD/target/release/hf2q" ./scripts/serve_deepseek4_opencode.sh

In another terminal, verify readiness and run the matching four-agent gate:

curl --fail http://127.0.0.1:8081/readyz
BASE_URL=http://127.0.0.1:8081 FAMILY=qwen36 AGENTS=4 \
  ./scripts/test_full_context_agent_slots.sh

curl --fail http://127.0.0.1:8082/readyz
BASE_URL=http://127.0.0.1:8082 FAMILY=gemma4 AGENTS=4 \
  ./scripts/test_full_context_agent_slots.sh

curl --fail http://127.0.0.1:8081/readyz
BASE_URL=http://127.0.0.1:8081 FAMILY=deepseek4 AGENTS=4 \
  ./scripts/test_full_context_agent_slots.sh

Run one model at a time. A battery-powered run is useful for functional testing but is not performance authority; the release latency gates require AC power, clear thermal status, and the exact artifact/power receipts described in docs/shipping-contract.md.

For MoE models, pass an APEX tier instead of a standard ftype:

hf2q convert ./models/Qwen3.5-35B-A3B \
  --quant apex-balanced \
  -o ./out/qwen35-apex-balanced.gguf

The driver looks up the fingerprint manifest and, on match, logs [hf2q apex] auto-detected APEX config: vendor/apex-quant/configs/<file> before quantizing — confirming the exact per-tensor overlay in use.

I-tier APEX (imatrix-aware quantization)

The apex-i-* tiers (apex-i-quality, apex-i-balanced, apex-i-compact) require per-row activation-importance data (imatrix). Two ways to supply it:

# A. In-tree: hf2q runs its own forward pass over a calibration corpus.
#    Stage 3.0 supports Gemma 4 only; other arches use option B.
hf2q convert ./models/google-gemma-4-26b-a4b-it \
  --quant apex-i-balanced \
  --imatrix-corpus cdv3 \
  -o ./out/gemma4-26b-apex-i-balanced.gguf

# B. Pre-computed: pass an external `.imatrix.gguf` (works for any
#    supported arch — Qwen 3.5/3.6 MoE included).
llama-imatrix -m ./out/qwen35-f16.gguf \
  -f data/calibration/cdv3.txt \
  -o /tmp/qwen35.imatrix.gguf
hf2q convert ./models/Qwen3.5-35B-A3B \
  --quant apex-i-balanced \
  --imatrix /tmp/qwen35.imatrix.gguf \
  -o ./out/qwen35-apex-i-balanced.gguf

The in-tree path (option A) writes a temporary F16 GGUF, drives the forward pass over cdv3 (bartowski's calibration corpus, baked into the binary), and consumes the resulting per-tensor sum-of-squared-activations to choose the per-layer mix. Wall time is dominated by the forward pass: roughly seconds per 512-token chunk × ~100 chunks on a 26B-A4B Gemma 4 model = operator-coffee-time, not CI-time.

Optional flags:

• --imatrix-out <path> — write the computed (or loaded) imatrix to disk for reuse across multiple --quant apex-i-* runs against the same base model.
• --imatrix-n-ctx <N> — override the default 512-token chunk size (matches stock llama-imatrix -c 512). Larger N means fewer, longer chunks per forward-pass loop; useful when matching imatrices produced by stock llama-imatrix -c <other>. Must be > 0; passing 0 surfaces a typed ConvertError::ImatrixNCtxInvalid.

Architecture

A full source-grounded architecture map lives in docs/ARCHITECTURE.md. One-paragraph version:

   ┌──────────────┐    ┌──────────────────┐    ┌──────────────┐
HF │ input/       │ -> │ models/<arch>/   │ -> │ backends/    │
   │ - safetensors│    │ - tensor rename  │    │ - gguf       │
   │ - config     │    │ - MoE merge      │    │ - safetensors│
   └──────────────┘    │ - DWQ targets    │    └──────────────┘
                       └──────────────────┘            │ GGUF
                                                       v
                                              ┌──────────────────┐
                                              │ inference/       │
                                              │ - load + warmup  │
                                              │ - forward (mlx)  │
                                              │ - KV cache (TQ)  │
                                              │ - spec-decode    │
                                              └──────────────────┘
                                                       │
                                              ┌──────────────────┐
                                              │ serve/           │
                                              │ - OpenAI HTTP    │
                                              │ - SSE streaming  │
                                              │ - block-prefix$  │
                                              │ - multi-model    │
                                              └──────────────────┘

Historical performance snapshot

The following numbers are the matched 2026-05-17 M5 Max snapshot, not a claim about every later commit or model artifact. Re-run the linked protocol for a current purchasing or deployment decision; correctness and release gates do not treat these historical medians as continuously verified.

Re-bench at the recorded HEAD on M5 Max against the peer (build 389ff61d7, -fa 1) with identical GGUFs. 3-run median; hf2q uses default config including the HF2Q_NO_FA hybrid-attn fix from commit 03328ee5. See docs/peer-parity-baselines-2026-04-26.md for the full thermal-fair alt-pair protocol used by ADR-029 baselines.

• Decode (Gemma-4 26B-A4B Q6_K) — tg200 1.01× peer-FA AHEAD (hf2q 105.2 t/s vs llama-bench 104.32 t/s); tg2000 0.97× peer-FA (hf2q 93.5 t/s vs 96.69 t/s). The historical ADR-029 iter-175 ~1.05× AHEAD across tg200/tg2000/tg5000 claim was measured at a pre-HF2Q_NO_FA HEAD; re-bench at current main shows it holding at tg200 only.
• Prefill (Gemma-4 26B) — crossover regime: pp1800 0.96× peer-FA (hf2q 2734 t/s vs llama-bench 2837 t/s); pp3700 1.24× peer-FA AHEAD (hf2q 2703 t/s vs 2181 t/s). hf2q's prefill rate drops ~1% from pp1800→pp3700 while the peer's drops ~23% (FA tile-skip helps less at longer K), so the cross-over sits early in this range. The historical 1.07-1.09× AHEAD claim across the whole range no longer holds at current main.
• Decode (Qwen 3.6 35B-A3B APEX-Q5_K_M) — tg200 1.29× peer-FA AHEAD (hf2q 130.6 t/s vs 101.31 t/s). Historical ADR-028 ~1.34× measurement is within ~4% of current re-bench (thermal / build drift).
• KV-cache footprint — TurboQuant 8-bit (ADR-007 + ADR-027 iter-34) drops F32 K/V allocations entirely on Qwen 3.6 35B-A3B at 32K context, 340 MiB vs 1.34 GiB F32-only baseline = 3.94× memory savings. This is the only major performance claim with an in-tree regression pin: full_attn_bytes_breakdown_tq_on_drops_f32_at_qwen36_32k (plus its 8K sibling) in src/inference/models/qwen35/kv_cache.rs asserts f32_k_v_bytes == 0 with TQ active and the exact 340,787,200-byte total vs the 1,342,177,280-byte F32-only baseline (3.94×) at the 32K shape. Run: cargo test --locked --bin hf2q full_attn_bytes_breakdown_tq_on_drops_f32 (Metal device required; the lib target is a narrow facade, so the pin lives in the bin's unit tests, not tests/).

Regression protection for the decode path: 8 parity tests (V2/V3 unbatched + V3 batched), coherence_smoke (2 cells), 200-token byte-identity verification. No automated bench-vs-peer gate is currently in CI — these numbers are operator-driven re-bench, not continuously verified.

Note: DWQ at the production-default perturb=1.0 is mathematically equivalent to the underlying K-quant baseline (ADR-020 finding 2026-05-08); DWQ wins materialize only at lower perturb values that move the scales/biases off the K-quant projection.

Performance work is investigation-driven and tracked in numbered ADR-029 (Gemma 4 decode), ADR-028 (peer-parity baseline), ADR-030 (speculative decode) iter-logs under docs/.

Repository layout

src/
├── arch/          single source of truth for per-arch conformance
├── backends/      GGUF + mlx-lm safetensors writers
├── calibrate/     DWQ training, autograd, imatrix
├── inference/     per-arch forward graphs, spec-decode, vision
├── input/         HF config + safetensors loaders, HF Hub download
├── intelligence/  hardware probe, auto-quant heuristics, RuVector
├── ir/            internal tensor / metadata representation
├── models/        per-arch tensor rename + MoE merge
├── quality/       cosine / KL / perplexity scorers
├── quantize/      Q-format codecs (legacy / K-quant / DWQ / mixed)
└── serve/         OpenAI HTTP API, block-prefix KV cache, multi-model
docs/              architectural decisions + operator/runbook evidence
tests/             integration, parity, packaging, and regression gates
scripts/           launchers, benchmarks, incident repros, and runbooks

Development

cargo build              # debug build
cargo test               # full test suite
cargo build --release    # release binary
cargo run -- doctor      # diagnostic

The project is TDD-heavy: every ADR closes only when its acceptance tests + smoke prompts pass. New architectures must be onboarded via the checklist in docs/arch-onboarding.md — registry entry + tensor catalog + smoke prompt before any forward-pass code lands.

Documentation index

• docs/getting-started.md — canonical Qwen3.8 convert, serve, API, and optional OpenCode journey.
• docs/ARCHITECTURE.md — source-grounded architecture map.
• docs/converting-a-model.md — generic convert reference.
• docs/converting-qwen35.md — Qwen 3.5/3.6 specifics.
• docs/chat.md — diagnostic chat, local server discovery, and explicit model switching.
• docs/operating-kv-cache.md — TurboQuant KV cache operator guide.
• docs/operator-env-vars.md — every HF2Q_* env var, what it gates.
• docs/adr/ADR-043-foreground-serve-dashboard.md — live foreground serve UX, nonblocking telemetry, privacy, and terminal acceptance contract.
• docs/shipping-contract.md — default, supported, experimental, and investigation-only product surfaces.
• docs/adr/ADR-019-mlx-native-encoder-architecture.md — Metal encoder ownership and pool-less worker lifetime contract.
• docs/adr/ADR-027-qwen35-tq-kv-cache-and-persist-family.md — Qwen hybrid cache, bounded prefill, cancellation, and watchdog containment.
• docs/adr/ADR-040-continuous-batching-reopen.md — full-context slot scheduling.
• docs/ADR-*.md — architectural decisions, rationale, failed spikes, and verification status.

License

Dual-licensed under Apache-2.0 OR MIT (Cargo.toml license field; LICENSE-APACHE and LICENSE-MIT files at repo root). See docs/adr/ADR-008-candle-divorce.md for the dependency philosophy.