Share the Metal session graph with CUDA for text and vision, MTP and exact sampling, steering, checkpoints, and disk-only BF16 n-grams. Add bounded concurrent Linux n-gram reads, vectorized dense and expert kernels, register-held recurrent prefill state, and efficient small speculative batches.
Keep Qwen out of DeepSeek batch dispatch and exclude disk n-grams from resident memory accounting. Use normal file-backed weight loading in diagnostics and link n-gram support into the CUDA quality scorer.
Include independent kernel and memory-safety tests, mixed-prefill and recovery checks, long-context server and agent coverage, and user documentation with measured DGX Spark performance.
Use one verified GGUF per quantization, remove sidecar setup, and document disk versus resident size. Record native-reader, model, agent/server, steering, fallback, and continued-prefill QA with explicit limits.
Add bounded SSD streaming, exact short prefills, accelerated large prefills, and native session batching. Support resident tensor parallel inference across two DGX Sparks over RoCE, with aligned expert shards, split vocabulary projection, decode graphs, and shared-expert overlap.
Include primitive, state, transport, and model regression tests, plus usage documentation and measured QA results.
Implement qwen4exp loading, tokenizer and chat formatting, PLE sidecars,
Metal text and vision graphs, built-in MTP, and recurrent KV persistence.
Integrate the CLI, agent, server, download targets, and conversion tools.
Preserve the native Q4_1 encoder, Q4_K accumulation guard, and shared
quantizer build target. Keep exact-MTP rewind and vision-cache row-width
fixes. Persist valid logits and recurrent state at completed prefill chunks,
and reuse Qwen text-answer history when tools clients omit reasoning.
Retain exact reasoning replay and test continued checkpoints, cancellation,
cache eviction, and restart reuse with and without reasoning_content. Include
reproducible charts and measurements comparing throughput before and after
the checkpoint fixes on M3 Ultra.
Add Qwen trunk steering and prompt activation capture, including GDN layers.
Keep the MTP predictor outside the 48-row bank and preserve prompt dumps
through one-token prefill tails and multi-row speculative verification.
Recover Chrome target creation and retain its last page target. Preserve
full agent transcripts in stripped-session format when KV no longer fits,
while retaining the newer automatic compaction and continuation logic.
Add steering/MTP/capture, Chrome CDP recovery, and full-session save/reload
regressions. Adapt the intended changes from fork PRs #13 and #7.
Reconcile with antirez main bd66c40, preserving DeepSeek v4.1 model IDs,
tool syntax, session state, and Metal kernel dispatch. Exclude superseded
engine code and prototypes.
Co-authored-by: Andreas Parusel <aparuael@7170c824-6152-4cf1-985e-9503832ce673.fritz.box>
Co-authored-by: david <contact@davidberard.fr>
Co-authored-by: Michael Kuebbeler <cropduster@users.noreply.github.com>
Co-authored-by: Ogilthorp3 <bertrand@nepveu.name>
Co-authored-by: anemll <realanemll@gmail.com>
Adapt teaching-mode PR #391 to /hints on|off. Append the full
instruction once per context and short state changes at safe
assistant boundaries. New and resumed sessions start off;
compaction reinstates the current policy.
Use ordinary Markdown asides and the existing renderer, without
a learner profile, new tool, or persistent preference.
Tested on M5 Max IT with DeepSeek Flash and GLM Flash coding sessions,
prefix reuse, queued changes, compaction, save/switch/new, frontend
and PTY tests, and agent ASAN/UBSAN checks.
Closes#391
Co-authored-by: Salvatore Sanfilippo <antirez@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Keep the introduction and everyday usage in the README, with setup guides near the top and detailed references at the end. Document Ada/L40S support explicitly and replace stale model, memory, caching and performance claims.
Retain the root Strix Halo page as a link to its new guide.
ds4_gpu_compressor_store_batch_tensor and the score-with-APE path used to
encode one f16->f32 copy per ratio-row segment (n_tokens/4 single-threadgroup
encoders per layer: 21.5k of the 25k encoders of a 2048-token prefill). A
tiled-row kernel (kernel_cpy_tile_rows_f16_f32 / _f32_f32) writes the whole
batch in one dispatch with identical values. README: updated two-Mac numbers
and the new opt-out env vars.
Two-Mac TP prefill, byte-exact vs the TensorOps-MoE hashes: 2048 tokens
785 -> 830 t/s, 128 tokens 242 -> 253 t/s.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WjmBSeiH6tPDtJuiQNFSpq
Measure the runtime decode map before selecting the global Metal path, account its mapped bytes once, and fall back to per-layer mapping when it cannot fit. Treat explicit cache sizes as performance targets that may be reduced after model, graph, context, and platform reserves are known. Share the final guard-budget calculation between fitting and refusal on Metal and ROCm.
Implement GLM 5.3 Q2 and Q4 inference across Metal and CUDA, including tensor parallelism, RDMA, batching, MTP, snapshots, SSD streaming, and GLM 5.2 shared-path improvements.
Add quantization and validation tools, official quality fixtures, model downloads, regression coverage, release QA, and user documentation.
Tarjei Mandt
Salvatore Sanfilippo
Donato Capitella
Jonathan Yates