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LocalAI/backend/go/vllm-cpp/README.md
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mudler-agentandEttore Di Giacinto b540c3e1fd chore(vllm-cpp): bump to 967883486 (ABI v30), add hf_overrides and Tev1 entries, fix vllm-cpp gallery installs (#12379)
* chore(vllm-cpp): bump vllm.cpp to 967883486 (ABI v30)

Moves the pin from c3bebc357 to 967883486. On top of the Nimble decision
adapter and the Qwen3.5 vision-loader fix, this brings Tev1 on
/v1/systemone and vllm_decide (opt-in through a "Tev1Model" architecture
in config.json), a tokenizer/ subdirectory fallback so the Laya HF
snapshot loads as downloaded, a stop-token fix, a logprobs fix under async
scheduling and a pinned parakeet.cpp fetch for the diarization build.

ABI v30 only adds the diarization and speaker-attributed ASR entry
points; no existing struct or signature changed, so the purego mirrors
keep their layout and only abiVersion moves to 30. Between 4479dc99f and
967883486 vllm.h changed only in a comment.

v30 turns VLLM_CPP_WITH_DIARIZATION on by default. The fetch is pinned
now, but ON still downloads parakeet.cpp at configure time and links a
second ggml into libvllm for calls this backend never makes, so build
with the option off: the symbols stay present as refusing stubs.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-sonnet-5-5

* feat(vllm-cpp): add the hf_overrides engine arg

vLLM parity: engine_args.hf_overrides is a JSON object of top-level
config.json keys merged over the model directory's own config.json. The
main use is opting a published checkpoint into an engine adapter its
config does not name, such as {"architectures": ["Tev1Model"]} on the
Tev1 snapshots, which declare Qwen3_5ForConditionalGeneration.

The C ABI has no override input and the engine reads config.json from
the directory it is given, so Load builds a private overlay directory:
the merged config.json plus a symlink to every other entry of the model
directory, and passes that to the engine. The download is never written.
Free, a failed load and the next Load remove the overlay.
validModelPath and the DFlash draft resolution still see the real
directory.

A value that is not a JSON object, a .gguf model or a directory without
config.json fails the load instead of being skipped like an unknown
engine_args key, because loading the unmodified config would serve a
different architecture than the one configured.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-sonnet-5-5

* fix(gallery): nest vllm-cpp artifacts under overrides

artifacts: is a model-config key, and the installer reads model-config
keys only from overrides:. Five vllm-cpp entries (laya, gliner25-decide,
qwen3-vl-4b, cua-s1-forms and gliner2.5) declared it at the entry top
level, where it is silently dropped: the install reports success, writes
a config whose model is the bare HF repo id and downloads nothing, and
vllm-cpp (which does not infer artifacts) then fails the first load with
"model path not found".

Move each block under overrides:, and add a guard test that refuses a
top-level artifacts: key in the index.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-sonnet-5-5

* feat(gallery): add Tev1 4B and 0.8B on vllm-cpp

Two decisions entries for Together AI's Tev1 checkpoints, pinned to the
current HF revisions. Tev1 is autoregressive: vllm.cpp answers
/v1/systemone by scoring the option letters, and the same engine still
serves chat completions. The published config.json names
Qwen3_5ForConditionalGeneration, so each entry sets
hf_overrides: {architectures: [Tev1Model]} to enable the decision route
without editing the download. known_usecases is [decisions] only, since
a declared decisions list is authoritative for reservation.

The descriptions state what was checked: agreement with transformers on
CPU over seven questions (4B 7/7, max probability difference 0.0004;
0.8B 6/7 with one near tie), CPU-only for the decision route, and a
fine-tune license the model card says is still being finalized, so no
license key is set.

The Decisions API page lists both entries, drops the note that Tev1
does not serve /v1/systemone and documents the 24-option limit (Ollama
allows 26).

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-sonnet-5-5

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-09-30 20:22:49 +02:00

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9.2 KiB
Markdown

# vllm-cpp backend
LocalAI backend for [vllm.cpp](https://github.com/mudler/vllm.cpp),
the LocalAI-team C++20 port of vLLM (paged KV cache, continuous batching,
safetensors + GGUF loading, CUDA / CPU / Metal / Vulkan) with no Python at
inference time.
It serves two things: text generation, and MiniMax-H3 joint video+audio
generation.
The backend dlopens the engine's stable C ABI (`libvllm`, `include/vllm.h`,
ABI v30) through purego:
- `Load` -> `vllm_engine_load`: accepts a `.gguf` file or a HF-style model
directory (`config.json` + safetensors). `context_size` maps to
`max_model_len`; `options: ["block_size:<n>", "num_blocks:<n>",
"max_num_seqs:<n>"]` size the KV cache and scheduler admission.
- `Predict` -> `vllm_complete` (blocking).
- `PredictStream` -> `vllm_complete_stream`; concurrent gRPC requests batch
continuously in the engine's shared AsyncLLM scheduler.
- Chat / tool calling rides the SAME code path as the llama.cpp autoparser:
with `use_tokenizer_template: true` the backend implements
`PredictRich`/`PredictStreamRich` over the ABI v3 chat entry points
(`vllm_chat` / `vllm_chat_stream`). The ENGINE applies the model's chat
template (GGUF `tokenizer.chat_template` or `tokenizer_config.json`),
decides when a tool call engages (`tool_choice: auto` lowers to a LAZY
structural-tag decode constraint; `required`/named force one), parses tool
calls with its streaming Hermes-style parser, and the backend maps each
`chat.completion.chunk` onto `ChatDelta`/`ToolCallDelta` protos.
- Without structured messages the plain path applies:
`PredictOptions.Grammar` -> the ABI's `structured_grammar` (GBNF) for
LocalAI's Go-side grammar-constrained tool calling; JSON-schema / regex /
choice constraints are also exposed by the ABI.
`patches/` carries fixes the pinned engine SHA does not have yet, applied to
the clone the same way `longcat-video` patches its upstream. `git apply` is
unguarded on purpose: a patch that stops applying must fail the clone loudly
rather than leave a pin silently missing a fix it is documented to carry. Each
patch header says what retires it.
The struct mirrors in `govllmcpp.go` are hand-written against one ABI version,
and the engine refuses to load against any other. Moving `VLLM_CPP_VERSION` in
the Makefile therefore means updating `abiVersion` plus the mirrors (and their
offsets in `vllmcpp_test.go`) in the same change; `make abi-check` compares the
pinned header against the bindings and the library build runs it first.
The Makefile builds libvllm with `-DVLLM_CPP_WITH_DIARIZATION=OFF`. vllm.cpp
turns that option ON by default since ABI v30, and ON fetches a pinned
parakeet.cpp (with its own ggml) at configure time. This backend does not bind
the diarization entry points, so OFF adds no dependency and changes nothing it
serves: those calls exist in libvllm but refuse with "not compiled in". If a
future change binds them, pin the parakeet.cpp source with
`-DVLLM_CPP_PARAKEET_CPP_DIR` and make `package.sh` bundle what it links.
Model config example:
```yaml
name: qwen3-vllm
backend: vllm-cpp
context_size: 8192
parameters:
model: Qwen3-4B # model dir (safetensors) or .gguf file
options:
- max_num_seqs:16
```
## hf_overrides
`engine_args.hf_overrides` (vLLM parity) is a JSON object of top-level
`config.json` keys merged over the model directory's `config.json`. The C ABI
has no override input and the engine reads `config.json` from the directory it
is given, so `Load` builds an overlay (`hfoverrides.go`): a temp dir with the
merged `config.json` plus a symlink to every other entry of the model dir, and
passes the overlay as `model_path`. `validModelPath` and the DFlash draft
resolution still run against the real model dir. `Free` (and a failed load, or
the next `Load`) removes the overlay. A value that is not an object, a `.gguf`
model, or a dir without `config.json` fails the load instead of being ignored,
because loading the unmodified config would serve another architecture.
```yaml
engine_args:
hf_overrides:
architectures: ["Tev1Model"] # opt a Qwen3.5-declared Tev1 snapshot into the Tev1 adapter
```
## MiniMax-H3 video+audio generation
`GenerateVideo` -> `vllm_video_generate` (ABI v12). H3 renders picture and sound
together, so the output MP4 carries a real AAC track.
The video engine is a SECOND handle (`vllm_video_engine`), not a mode of the
text one, because H3 is a checkpoint SET rather than a model directory: the DiT,
the text encoder and two VAEs are separate artifacts, and vllm.cpp has the two
loaders refuse each other's checkpoints. `Load` takes the video branch when the
model config carries any of the video options below; `parameters.model` is the
DiT and everything else is named in `options:`.
```yaml
name: minimax-h3-fl2va-q4
backend: vllm-cpp
cuda: true
known_usecases: [video]
parameters:
model: minimax-h3/MiniMax-H3-FL2VA-Q4_K_M.gguf
options:
- video_encoder:minimax-h3/qwen3vl-32B-MiniMax-H3-Q4_K_M.gguf
- video_tokenizer:minimax-h3/tokenizer.json
- video_vae:minimax-h3/video_vae.safetensors
- video_vae_config:minimax-h3/video_vae_config.json
- audio_vae:minimax-h3/audio_vae.safetensors
- audio_vae_config:minimax-h3/audio_vae_config.json
- video_partition:fl2va
- video_device:cuda
- video_dequant_bf16:true
- video_width:1344
- video_height:768
- video_num_frames:124
```
Three things are worth knowing before touching this path.
**The partition is declared, not detected, and a mismatch does not fail
cleanly.** The FL2VA DiT serves `t2va` and `fl2va`; `ref2va` is a different
checkpoint. The community GGUF/NVFP4 quantisations strip the release metadata
and the two DiTs are byte-structurally identical, so the engine refuses every
generate until `video_partition` says which one it has. Handing reference
conditioning to an FL2VA DiT renders for hours and returns a coloured lattice
over the frame, so `checkPartitionConditioning` refuses that combination here,
before the engine is called.
**ffmpeg comes from the host.** libvllm writes the frames and the WAV and
COMPOSES the mux argv, then spawns nothing — that process boundary is upstream's
decision. `muxVideo` takes the composed argv, substitutes `argv[0]` with the
resolved binary and execs it; the backend image is `FROM scratch` and carries no
ffmpeg, the same arrangement `vibevoice-cpp` uses for transcoding. ffmpeg also
converts a `start_image`/`end_image` upload into the binary PPM at the exact
output canvas the engine requires, since libvllm vendors neither an image codec
nor a resampler.
**It is slow.** Roughly 176 s per denoise step at 1344x768 on a 20-SM device, so
the 50-step default is hours. Nothing here imposes a deadline.
Geometry mirrors the engine so the two agree: the canvas is truncated onto a
32-pixel grid, the frame count sits on the 17n+5 grid, and an unspecified canvas
with a keyframe is derived from that image's aspect on a 768-pixel short edge
(`MiniMaxH3ResolveShape`, `minimax_h3_planner.cpp`).
## Apple Silicon: the MLX GEMM provider (ON by default, gated to prefill)
`BUILD_TYPE=metal` builds vllm.cpp's MLX provider for the dense GEMM
(`VLLM_CPP_MLX=on`, the default here). It is on because upstream now SHAPE-GATES
it to prefill; it was briefly off in this branch's history, and that was correct
at the time for an ungated provider.
The gate matters more than the flag. MLX's steel GEMM wins prefill but loses
decode, because the provider pays an `mx::eval` synchronisation plus an output
memcpy on every call and decode makes ~112 calls *per token*. Measured on an
Apple M4, Qwen3-1.7B-bf16 warm at p=512 g=128:
| configuration | prefill TTFT | warm throughput |
|---|--:|--:|
| MLX **gated to prefill** (pin >= 89c46aeb) | **524.5 ms** | **24.37 tok/s, 97.6% of MLX-LM** |
| MLX ungated (older pins) | 537 ms | 12.7 tok/s |
| MLX off | 602 ms | 23.9 tok/s, 95.9% |
Ratios are against an MLX-LM baseline measured INTERLEAVED with ours over four
ABBA blocks (its spread 0.34%, ours 0.12%). An earlier revision of this file
claimed 99.1%; that used a two-run MLX-LM baseline containing an outlier and
overstated us by about 1.5 points.
**`VLLM_CPP_VERSION` and this flag are coupled.** Moving the pin back before
`89c46aeb` while leaving `VLLM_CPP_MLX=on` would take the middle row — roughly
half throughput. If you roll the pin back, roll the default back with it.
One caveat: MLX's GEMM is not bit-identical to the native kernel, so an MLX build
produces a different greedy sequence than a non-MLX one. That is a property of the
provider, not of the gate, and it predates this packaging. Full disposition in
vllm.cpp `docs/BENCHMARKS.md`.
Build knobs:
- `VLLM_CPP_MLX=off` builds Metal without the provider: ~124 MB smaller, and
96.4% of MLX-LM instead of 99.1%.
- `MLX_VERSION` pins the wheel (default `0.29.4`). MLX is consumed as the
prebuilt pip wheel because building it from source needs `xcrun metal`, i.e. a
full Xcode the macOS runners do not have.
Packaging vendors `libmlx.dylib`, `mlx.metallib` and MLX's MIT license into
`package/lib/`, and rewrites `libvllm.dylib`'s rpath to `@loader_path/lib`
(re-signing it, since `install_name_tool` invalidates the signature). The
metallib must stay beside `libmlx.dylib`: MLX looks for it there.
Testing: `make test` runs the unit specs; export `VLLM_CPP_MODEL=<model>` (and
optionally `VLLM_CPP_LIBRARY=<libvllm path>`) to enable the e2e specs.