AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
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一分钟完成验证Local LLM inference for Apple Silicon. One small native engine runs the safetensors checkpoint as downloaded. Signed, notarized installer in Releases.
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取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
Run a local LLM on your Mac with one small native engine.
Veizik opens the safetensors checkpoint you already downloaded — Qwen2.5 and Qwen3 today — and runs it on the Apple GPU. No Python environment, no framework, no converted copy of the model. Prompts and generated text stay on the machine.
Who this is for
Developers and teams who run open models on Apple Silicon and want one signed binary instead of a framework stack: a quick chat, a one-shot run, or a local HTTP server with OpenAI-style request and response shapes for your own tools.查看完整 README ↗Run a local LLM on your Mac with one small native engine. Veizik opens the safetensors checkpoint you already downloaded — Qwen2.5 and Qwen3 today — and runs it on the Apple GPU. No Python environment, no framework, no converted copy of the model. Prompts and generated text stay on the machine.
Developers and teams who run open models on Apple Silicon and want one signed binary instead of a framework stack: a quick chat, a one-shot run, or a local HTTP server with OpenAI-style request and response shapes for your own tools.
$ veizik chat qwen2.5-1.5b-instruct
vz > What is a safetensors checkpoint? Answer in two sentences.
A Safetensors checkpoint is a pre-optimized model checkpoint that is designed to be compatible with …
| Model | Hugging Face checkpoint | Chat |
|---|---|---|
| qwen2.5-0.5b / -instruct | Qwen/Qwen2.5-0.5B / -Instruct | instruct only |
| qwen2.5-1.5b / -instruct | Qwen/Qwen2.5-1.5B / -Instruct | instruct only |
| qwen2.5-7b / -instruct | Qwen/Qwen2.5-7B / -Instruct | instruct only |
| qwen3-1.7b / -base | Qwen/Qwen3-1.7B / -Base | qwen3-1.7b |
Context: 32,768 tokens declared, 30,000 measured. Device-memory figures are on the benchmarks board; they carry to this build by §R measured-neutrality — the generated GPU kernels are byte-identical to the measured cores and the steady-state probe held device memory within 1%. Checkpoints are not included — veizik pull fetches them from Hugging Face under Alibaba Cloud's Apache-2.0 licence.
curl -LO https://github.com/veizikhq/veizik-metal/releases/download/v2026.10.02-r10/veizik-metal-2026.10.02-r10.dmg
shasum -a 256 veizik-metal-2026.10.02-r10.dmg # d74d9f2f6b167679df1294325c12921709363f3d6aa75633f2edfd043ca9f678
M=$(hdiutil attach -nobrowse veizik-metal-2026.10.02-r10.dmg | awk -F'\t' '/Volumes/{print $NF}' | tail -1)
"$M/install.sh" && hdiutil detach "$M" # the mount point is read from the attach, not assumed
export PATH="$HOME/.local/bin:$PATH"
veizik activate # opens veizik.com/activate — sign in (email link, Google or GitHub) and approve this Mac once
# no browser on this Mac (SSH)? on your account page create a key (Browser-free activation) and set a CLI password, then:
# veizik activate --key vzek_… --password-stdin < pwfile
veizik pull qwen2.5-1.5b-instruct # 2.8 GiB from Hugging Face, verified; an existing ~/.cache/huggingface copy is reused
veizik chat qwen2.5-1.5b-instruct # interactive; or:
veizik run qwen3-1.7b "The capital of France is" --tokens 32
veizik run qwen3-1.7b "Write a haiku about rain" --temperature 0.8 --seed 7 # sampling is opt-in; omit the flags for greedy output
veizik serve qwen2.5-1.5b-instruct --port 8000 # then POST http://127.0.0.1:8000/v1/chat/completions
The installer verifies SHA256SUMS and the Developer ID signatures before copying; the disk image is notarized by Apple. What needs the network: creating the account, activating the device, downloading a checkpoint, and the first run of each session (a short-lived licence lease held in memory). Generation itself does not.
--temperature above 0 the output is sampled; the same --seed and settings on the same Mac give the same text again. veizik serve gives the same text as veizik run for the same prompt and settings, checked token by token at 4K, 8K, 16K and 30K-token prompts. The same holds at 1, 4 and 16 concurrent requests.https://veizik.com/benchmarks — every published row names the device, model, token counts, the other engine's version and settings, how each side was timed, and the spread. Rows measured on earlier builds of the same cores are marked. Reproduce with veizik bench <model> and the row's command.
veizik serve takes a model name to serve just that one, or no name at all to host every model installed on the machine (variants such as -instruct and -base each run their own engine), routing each request by its "model" field; /v1/models lists what it is hosting. Sampler fields (temperature, top_p, top_k, seed, stop, penalties) are passed to the engine, which applies them or answers with an error naming the one it does not support. Streaming (stream: true) is served by the resident engine as Server-Sent Events; 501 is returned only for tools combined with streaming, or when no resident engine is available and the server falls back to one process per request. /v1/embeddings answers 501 in this release.context_length_exceeded, naming the limit and the size asked for; the server and every other request carry on./no_think to the prompt for a direct reply.veizik doctor prints the installation it is running from.veizik doctor · https://veizik.com/docs#troubleshooting · Issues and Discussions in this repository · [email protected]veizik 1,681,456 B · install.sh 9,809 B · models.json 9,076 B · models.json.sig 64 B · coreids.json 1,373 B · MANIFEST.txt 490 B · LICENSE 6,029 B · NOTICE.md 1,859 B · entitlement_enums.json 739 B · models/ (4 run cores + 4 resident serve cores + activation helpers, op-stream-hardened rebuilds of the r6.x cores -- render-identical, different bytes). Verify with shasum -a 256 -c SHA256SUMS.