ds4-ssd

KO | EN

gitlite — search

#python #typescript #go #machine-learning #macos #java #windows #awesome #cli #pytorch #security #mcp #openai #open-source

(★ 30)

DeepSeek V4 Flash specific inference engine. SSD MoE expert paging (slot-bank) + disk KV cache for long agent sessions. Metal-first, narrow & high-performance.

ds4-ssd 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# ds4-ssd

`ds4-ssd` is an alpha fork of antirez's DwarfStar 4 (`ds4`) inference engine
for DeepSeek V4 Flash. The fork keeps the narrow, self-contained DS4 runtime and
adds an SSD-streamed routed-MoE sidecar path for Apple Silicon systems where a
fully resident model is not practical.

The main alpha feature is SSD streaming: dense tensors stay in a normal GGUF,
while routed experts live in a sidecar directory and are paged through a
slot-bank cache. Resident full-GGUF mode is still supported for high-memory
machines. Apple Silicon optimizations include NAX, the Apple neural-accelerator
backed `matmul2d` path used by Metal on M5-class hardware, plus Apple Neural
Engine routed-MLP prefill paths where the measured profile says ANE wins.

This branch is intentionally narrower than the research branch. It keeps the
runtime, Metal shaders, GGUF tools, correctness tests, sidecar smoke, and core
docs, while dropping profiling scripts, handoff notes, session exports, and
bench-only ANE probes from the public alpha tree.

## Status

Alpha means:

- SSD sidecar mode is the release headline.
- Resident full-GGUF mode remains available.
- Correctness vectors and an executable 16K sidecar smoke are the current test
  bar.
- Broader mode coverage, CI, and performance regression automation are planned
  for the next stage.

The in-repo `gguf-tools/deepseek4-quantize` tool builds resident GGUFs. It does
not yet emit the sidecar layout. For this alpha, use the prebuilt sidecar
package at
[anemll/dsv4-iq2xxs-expert-major](https://huggingface.co/anemll/dsv4-iq2xxs-expert-major).

## Build

On macOS:

```sh
make
```

This builds:

- `./ds4`: CLI runner.
- `./ds4-server`: OpenAI/Anthropic/Responses-compatible local server.
- `./ds4-bench`: throughput sweeps.
- `./ds4-eval`: evaluation helper.
- `./ds4-agent`: local coding-agent frontend.

`metal/` is a required build input. Do not prune it.

CUDA sources are inherited from upstream DS4 and kept in tree, but the alpha
validation focus is Apple Silicon SSD streaming.

## Run SSD Sidecar Mode

Download the prebuilt sidecar package:

```sh
./download_model.sh sidecar
```

Or the native MXFP4 package (bit-exact MXFP4 routed experts, ~156 GB,
[anemll/DSv4-Flash-MXFP4-native-flash](https://huggingface.co/anemll/DSv4-Flash-MXFP4-native-flash)):

```sh
./download_model.sh mxfp4
./ds4 -m models/DSv4-Flash-MXFP4-native-flash --ssd-cache auto -p "Hello"
```

`--ssd-cache` sizes the resident expert slot bank (`auto`, or an explicit value
like `32GB`). Any size is safe: on RAM-limited machines the bank is clamped so
prefill cannot overflow memory and auto-shrinks after prefill so decode-miss
reads stay served by the OS file cache.

Then set `DS4_SIDECAR_DIR` to the sidecar package root containing
`manifest.json` and `dense/model-dense.gguf`:

```sh
export DS4_SIDECAR_DIR="$PWD/models/dsv4-iq2xxs-expert-major"
```

Run the package root directly. DS4 detects the dense GGUF and sidecar metadata;
no explicit `--moe-sidecar` or `--moe-mode` flag is needed:

```sh
./ds4 \
  -m "$DS4_SIDECAR_DIR" \
  --moe-slot-bank 8 \
  --ctx 8192 \
  -p "Hello"
```

`--ctx 8192` is the KV window in this conservative first-run example. Leave the
Metal raw-KV cap automatic so it follows the prefill chunk size and server
checkpoint frontiers stay aligned.

Start with `--moe-slot-bank 8` and raise it once you confirm there is headroom.
The slot bank is the cap on resident routed-expert slots, so a larger value
trades RAM for fewer SSD reads. `--moe-slot-bank 64 --ctx 32768` is a
high-memory setting, not the safest default.

For cache-budget comparisons, especially against upstream SSD-streaming runs,
use `--ssd-cache` instead of manually choosing a slot count. Explicit sizes set
the target routed-expert slot-bank budget, while `auto` sizes the slot bank from
currently available memory after dense weights and context buffers are
estimated:

```sh
./ds4 -m "$DS4_SIDECAR_DIR" --ssd-cache 32G --ctx 32768 -p "Hello"
./ds4 -m "$DS4_SIDECAR_DIR" --ssd-cache 64G --ctx 32768 -p "Hello"
./ds4 -m "$DS4_SIDECAR_DIR" --ssd-cache auto --ctx 32768 -p "Hello"
```

Run the committed sidecar smoke:

```sh
DS4_SIDECAR_DIR=/path/to/dsv4-iq2xxs-expert-major make sidecar-smoke
```

To confirm SSD streaming is active, look for these startup lines:

```text
applied sidecar tuning profile
Flash-MoE sidecar loaded
Flash-MoE slot banks allocated
```

If `-m` points at a directory containing `manifest.json` and
`dense/model-dense.gguf`, DS4 auto-detects SSD sidecar mode, rewrites the model
path to the dense GGUF, and enables sidecar slot-bank mode internally. If you
pass only `-m /path/to/full-model.gguf`, DS4 is in resident/full-GGUF mode.

See [docs/SIDECAR.md](docs/SIDECAR.md). For the external expert-sidecar
export wrapper, see [docs/SIDECAR_EXPORT.md](docs/SIDECAR_EXPORT.md); the
prebuilt Hugging Face sidecar remains the turnkey low-RAM package.

Machine-specific defaults for M5, M5 Max, M3 Ultra, and M1 Max are selected
from `ds4_profile.json`. Profiles set defaults only; exported environment
variables still win. Profiles choose ANE only for chunk shapes where it has
measured faster than GPU or NAX on that machine. See
[docs/PROFILES.md](docs/PROFILES.md) and
[docs/STREAMING_KNOBS.md](docs/STREAMING_KNOBS.md).

### Experimental Pro Support

DeepSeek V4 Pro sidecar support is experimental. For Pro agent runs, use
`--nothink`, keep the slot bank at or below 32 slots while tuning, and keep
shared-down decode prefetch enabled:

```sh
DS4_FLASH_MOE_DECODE_PREFETCH_SHARED_DOWN=1 ./ds4-agent \
  -m ~/Models/DSv4Pro-flash/ \
  --moe-slot-bank 32 \
  --ctx 32768 \
  --nothink
```

Larger Pro slot banks can consume enough memory bandwidth and residency budget
to collapse decode throughput, so only raise `--moe-slot-bank` after measuring
reuse and decode stalls on your machine.

For diagnostic fanout tests, add `--moe-expert-topk 4`. This is different from
`--moe-prefetch-topk`: it changes the actual routed expert count for both
prefill and decode, so quality and logits are expected to change.

`--no-int8` is optional. Normal runs use the fastest measured profile path.
For quality-preserving runs, pass `--no-int8`; it disables current int8 dense,
NAX, Flash-MoE, and ANE accelerator paths, using NAX-half where safe and GPU
fallbacks otherwise. `--quality` implies `--no-int8`.

## Run Resident GGUF Mode

Download a resident GGUF:

```sh
./download_model.sh q2-imatrix
```

An alternate resident GGUF,
[Huihui-DeepSeek-V4-Flash-BF16-abliterated-ds4-IQ2_XXS.gguf](https://huggingface.co/huihui-ai/Huihui-DeepSeek-V4-Flash-abliterated-ds4-GGUF/resolve/main/Huihui-DeepSeek-V4-Flash-BF16-abliterated-ds4-IQ2_XXS.gguf),
is available from
[huihui-ai/Huihui-DeepSeek-V4-Flash-abliterated-ds4-GGUF](https://huggingface.co/huihui-ai/Huihui-DeepSeek-V4-Flash-abliterated-ds4-GGUF):

```sh
./download_model.sh huihui-iq2xxs
```

Then run:

```sh
./ds4 -p "Hello"
```

Resident mode loads the full model file and is meant for high-memory machines.
It is still useful for baseline comparison, server use, and systems with enough
RAM to hold the selected quantization. The Huihui IQ2_XXS resident GGUF can be
used on a 96 GB M3 Ultra, but memory headroom is tight; run with little to
nothing else active. Reaped/pruned resident models are still under investigation.

See [docs/RESIDENT.md](docs/RESIDENT.md) and
[docs/MODEL_SETUP.md](docs/MODEL_SETUP.md).

## Validate

The alpha validation gate is:

```sh
make clean
make
./ds4_test --server --metal-kernels
make ane-smoke
DS4_SIDECAR_DIR=/path/to/dsv4-iq2xxs-expert-major make sidecar-smoke
```

The sidecar smoke uses `tests/test-vectors/prompts/long_code_audit.txt`, a
shorter 4K-class prompt, and generates 64 deterministic tokens with a 4K
prefill chunk cap.

## Docs

- [docs/MODEL_SETUP.md](docs/MODEL_SETUP.md): model files, downloads, and
  sidecar package expectations.
- [docs/SIDECAR.md](docs/SIDECAR.md): SSD streaming mode and smoke test.
- [docs/SIDECAR_EXPORT.md](docs/SIDECAR_EXPORT.md): external expert-sidecar
  export wrapper and its dense-GGUF caveats.
- [docs/STREAMING_KNOBS.md](docs/STREAMING_KNOBS.md): SSD sidecar slot-bank,
  prefill, I/O, ANE, and profile knobs.
- [docs/RESIDENT.md](docs/RESIDENT.md): full-GGUF resident mode.
- [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md): runtime layout and accelerator
  paths.
- [docs/PROFILES.md](docs/PROFILES.md): machine-specific tuning defaults and
  override rules.
- [docs/ANE_KERNELS.md](docs/ANE_KERNELS.md): experimental Apple Neural Engine
  kernel families and private API notes.
- [docs/PERFORMANCE.md](docs/PERFORMANCE.md): current benchmark stance.
- [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md): common first-run failures.
- [docs/DWARFSTAR4_REFERENCE.md](docs/DWARFSTAR4_REFERENCE.md): original DS4
  README retained for reference.

## Attribution

`ds4-ssd` is derived from antirez's DwarfStar 4 / `ds4` work and keeps the DS4
model-specific design: GGUF loading, prompt rendering, KV handling, server API,
and DeepSeek V4 Flash validation. The project also depends conceptually on the
GGUF, quantization, and kernel work pioneered by `llama.cpp` and GGML.

The SSD-streaming direction is also indebted to Apple's
[LLM in a flash: Efficient Large Language Model Inference with Limited Memory](https://machinelearning.apple.com/research/efficient-large-language)
paper and to the original [danveloper/flash-moe](https://github.com/danveloper/flash-moe)
work by Claude Opus 4.6 and Daniel Woods. Read the
[original Flash-MoE paper](https://github.com/danveloper/flash-moe/blob/main/paper/flash_moe.pdf)
for the full story of how they built that engine in 24 hours.

The Apple Neural Engine path uses GPU-side int8 dequantization/packing together
with ANE MLP execution through private Apple APIs and additional scheduling
optimizations. GPU int8 dequantization for this class of local inference was
pioneered by Liu Liu (Draw Things, @liuliu), and the private ANE API path was
first documented publicly by @maderix.

Keep the repository `LICENSE` with redistributions and preserve attribution to
antirez, llama.cpp, GGML, and their contributors.

ds4-ssd

ds4-ssd 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

ds4-ssd

ds4-ssd 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README