~/llamay $ # one static binary — this is the whole install
~/llamay $ llamay version
llamay         v0.1.58-1-gb14f550
build          go1.26.8 darwin/arm64, cgo on, tags metal
revision       b14f5501578e
kernels        neon+i8mm
gpu            metal built in; metal:Apple M4
formats        23: F32, F16, Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, Q8_1, Q2_K, Q3_K, Q4_K, Q5_K, Q6_
K, Q8_K, IQ2_XXS, IQ2_XS, IQ3_XXS, IQ4_NL, IQ3_S, IQ2_S, IQ4_XS, BF16, MXFP4
architectures  17 entries, 39 names: azmx (azmx-one, llama, mistral), qwen2 (qwen3), gemma,
gemma2, gemma3 (gemma3_text), azmx-code (azmx_code, azmxcode), gpt-oss (gptoss, gpt_oss, ope
nai-moe), phi3 (phi3.5), stablelm (stablelm2), gpt2, falcon (rw), gptneox (gpt_neox, gpt-neo
x, neox), phi2, qwen3moe (qwen3_moe), qwen2moe (qwen2_moe), azmx-ocr (ocr, ctc), azmx-embed
(bert, embedding)
~/llamay $ # models are named, not pathed, once they are in the store
~/llamay $ llamay models
name                                     size  architecture           store
all-minilm:latest                    43.8 MiB  bert (embedder)        llamay
gemma2:2b                             1.6 GiB  gemma2 (decoder)       llamay
gemma3:270m                         278.0 MiB  gemma3 (decoder)       llamay
gpt2:q8_0                           169.4 MiB  gpt2 (decoder)         llamay
qwen2.5:0.5b                        379.4 MiB  qwen2 (decoder)        llamay
smollm2:135m                        258.3 MiB  llama (decoder)        llamay
azmx-code-test:latest                 1.1 GiB  gemma3 (decoder)       ollama

6 in ~/.llamay/models, 1 in ~/.ollama/models
~/llamay $ # and that is the entire setup
~/llamay $ llamay run -m gemma2:2b -chat -n 110 -p "Explain what a KV cache is to someone wh
o writes web servers. Two sentences."
A KV cache is a memory-based store used by web servers to quickly retrieve frequently access
ed data. It works by storing key-value pairs (like website configurations, user sessions, or
 static content) and using those pairs to bypass the slower database or disk access for fast
er responses.


prompt 25 tokens in 2.507s (10.0 tok/s) · generated 58 in 15.61s (3.7 tok/s) · stop: eog