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    <title>vMLX — The AI Toolchain</title>
    <link>https://aitoolchain.io/tools/vmlx</link>
    <description>New releases and features in vMLX, tracked by The AI Toolchain.</description>
    <language>en</language>
    <lastBuildDate>Sun, 30 Aug 2026 22:41:17 GMT</lastBuildDate>
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    <item>
      <title>vMLX v1.6.48</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.48</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.48</guid>
      <pubDate>Sun, 30 Aug 2026 22:41:17 GMT</pubDate>
      <description>vMLX 1.6.48 enables vectorized GLM KDA MTP verification and makes native MTP restart cache proofs configurable
• Makes native MTP restart cache proofs configurable (commit `fc88a3ea`).
• Enables vectorized GLM KDA MTP verification for GLM-family models.
• Adds typed prompt SSD cache support to the MTP benchmark.
• Attests fast gateway replays from durable request IDs.
• Keeps Hugging Face download progress refresh-compatible.</description>
    </item>
    <item>
      <title>vMLX v1.6.46</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.46</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.46</guid>
      <pubDate>Sun, 30 Aug 2026 12:01:20 GMT</pubDate>
      <description>vMLX 1.6.46 adds GLM-5.3-Flash text support, native MTP for GLM, typed prompt caching for GLM, and a wired-limit recommendation popup in the server panel.
• Adds GLM-5.3-Flash text runtime with registry row and text-route, including bundle-stamped parser ID aliases for the GLM-5 family.
• Enables native Multi-Token Prediction (MTP) for GLM-5.3 at the public loader, with quantization mapped onto the attached head and adaptive MTP depth policy.
• Adds exact typed GLM native-state prefix caching (`GLM typed prompt cache`) with metadata validation on live restore, memory-bounded cache snapshots, and cache persistence through native MTP finalization.
• Adds GLM DSA sparse indexer path, unlocking full context for GLM-5 family models.
• Adds GLM-5-next family row, parser dropdown entries, and alias canonicalization to the server panel UI.
• Adds a visual wired-limit recommendation popup at session launch (W0-W2 core) in the server panel.
• Adds peak benchmark profiles to the server panel.
• Records request-exact MTP benchmark telemetry for observability.
• Exposes DSV4 indexer acceleration status and separates DSV4 indexer hits from fallbacks in engine reporting.
• Reports observed fused decode paths and standardizes cross-family acceleration status display.
• Adds source-gated affine MoE pair fusion for hybrid decode.
• Adds opt-in Qwen3.5 GDN decode fusion with exact gate preparation.
• Enables proven Qwen4 affine MoE pair decode and GLM mHC decode fusion.
• Fuses GLM KDA decode convolutions and state update, GLM mHC decode transform, GLM affine MoE down reduction, and GLM hyper-connection placement for fewer Metal dispatches.
• Fuses Qwen GDN decode convolution state update, Qwen PLE decode convolution, and small-row gated RMSNorm for hybrid decode.
• Groups GLM and Qwen4 affine projections and Qwen3.5 GDN decode projections at load time for fewer dispatches.
• Parallelizes Qwen4 PLE decode row reads.
• Caches completed GLM DSA pool keys to reduce redundant computation.
• Flushes final MLLM detokenizer bytes to streams and reconciles terminal MLLM and visible stream suffixes.
• Tahoe/macOS 26 is now the default build; Sequoia is the compatibility build.</description>
    </item>
    <item>
      <title>vMLX v1.6.45</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.45</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.45</guid>
      <pubDate>Sat, 29 Aug 2026 05:12:58 GMT</pubDate>
      <description>vMLX 1.6.45 adds prefix-cache fetch provenance to `/health` and `/v1/cache/stats`, plus per-request native-MTP adaptive telemetry.
• Per-request native-MTP telemetry now reports the adaptive profile seed and workload key.
• Engine-owned lifecycle load progress is now surfaced across every panel view, generation-guarded against stale attempts.</description>
    </item>
    <item>
      <title>vMLX v1.6.42</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.42</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.42</guid>
      <pubDate>Thu, 27 Aug 2026 11:53:23 GMT</pubDate>
      <description>vMLX 1.6.42 adds Qwen3.8 Flash Next support with SSD-backed n-gram caching, multimodal input, and adaptive MTP depth control.
• Adds support for the Qwen3.8 Flash Next model, covering its hybrid QSA/Gated DeltaNet architecture, native reasoning tiers, and in-model MTP head.
• Adds Electron settings for detected native MTP mode and adaptive or fixed D1-D3 depth policy, with persisted launch-argument parity.
• Adds file-backed, row-addressed PLE bigram/trigram tables on SSD, avoiding materializing the full n-gram table in unified memory.
• Adds request-local MTP rollback/commit state with adaptive depth probing based on confirmed tokens per wall second.
• Adds mixed image and video input support for Qwen3.8 Flash Next, preserving modality order, processor scaling, embeddings, cache keys, and request-local MTP state.
• Fuses Gated DeltaNet projections and hyper-connection execution while preserving the checkpoint&apos;s mixed-precision JANG contract.</description>
    </item>
    <item>
      <title>vMLX v1.6.35</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.35</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.35</guid>
      <pubDate>Sat, 22 Aug 2026 03:50:58 GMT</pubDate>
      <description>vMLX 1.6.35 ships SSD-first prompt caching by default, disk-proportional cache budgets, and full-precision stored prefix caches.
• SSD prompt cache is now the default; the in-memory paged cache is OFF by default, keeping memory consumption close to the loaded model size rather than growing with conversation length.
• SSD cache budget is now expressed as a percent of available disk (10% by default) instead of a flat gigabyte value, scaling proportionately across drive sizes.
• One unified budget now covers all cache types vMLX writes — plain KV blocks, typed companion state, native composite records, rotating sliding-window state, and multimodal blocks — across every model and session, with oldest prompts evicted first.
• Stored prefix caches are now full precision for every model family; TurboQuant stored-KV encoding measured under 1% improvement on time-to-first-token, so exactness is preferred.
• Session Settings surfaces the RAM-tier trade-off at the top — notes the ~2% time-to-first-token gain and the memory cost — and links directly to the setting.
• The &apos;Clear SSD cache for this session&apos; button now reports which cache tiers were actually cleared and which were still in use, rather than claiming success either way.
• Cache eviction protects the prefix currently being actively reused and removes idle ones first, across models including models that are not currently running.
• Prompt caches now survive a hard kill and restart across five cache architectures: DeepSeek-V4 composite, rotating sliding-window with images, SSM/GatedDelta companion, dots3 DSA, and MiniMax sparse MSA.
• The engine now emits an explicit warning when a cache budget is smaller than a single prompt&apos;s block chain, instead of silently storing nothing.
Breaking changes:
• The in-memory paged cache is now OFF by default; any setup relying on the RAM tier being active will need to re-enable it explicitly via Session Settings.</description>
    </item>
    <item>
      <title>vMLX v1.6.34</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.34</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.34</guid>
      <pubDate>Thu, 20 Aug 2026 20:51:41 GMT</pubDate>
      <description>vMLX 1.6.34 brings native MTP overhaul, DFlash2 session prefix reuse, and Qwen3.5-family video input support.
• Native MTP (speculative decoding) overhaul for Qwen3.8 and dots3-note: aligned draft-head context cache plus generalized skip-replay means rejected drafts no longer replay through the main model at any depth; warm in-app decode on Qwen3.8-27B rises from ~22 to 38–48 t/s, dots3-note reaches ~41 t/s.
• DFlash2 session prefix reuse via end-of-turn cache checkpoints, draft hidden-state gap splice, and prompt-boundary snapshots drops warm multiturn TTFT ~20x; sustains 60+ t/s on Qwen3.8 with DFlash2 (68.7 t/s measured); the DFlash runtime now ships in the bundle.
• Stream interval defaults to 8, preventing the renderer from backpressure-stalling the engine emit loop on long conversations; legacy sessions are lifted automatically.
• Removed the RAM preflight that refused large model loads — estimates now advise rather than refuse, enabling a 101GB dots3-note bundle to load on a 128GB Mac.
• Native MTP now actually engages by default: bundle-temperature auto-detection (which had kept it permanently off) is replaced with compatible-only detection using a deterministic-defaults sampling policy.
• Qwen3.5-family video input now supported — videos route as sampled frames so temporal questions answer correctly.
• Prefill admission rejections now include a wired-limit advisory naming the exact `sysctl`, the macOS ~84%-of-RAM default, and the reset-on-reboot behavior.</description>
    </item>
    <item>
      <title>vMLX v1.6.29</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.29</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.29</guid>
      <pubDate>Sat, 15 Aug 2026 10:40:58 GMT</pubDate>
      <description>vMLX 1.6.29 delivers hybrid prefix caching for up to 20x faster follow-up turns and stable 100k-context multiturn sessions.
• Hybrid prefix cache cuts follow-up turn latency on long documents by up to 20x (43.7k-token document follow-ups drop from 107s to 5s, byte-identical at temperature 0).
• 100k-context multiturn sessions now proven stable: 97.6k-token conversations retrieve planted facts with 99.9% cache reuse and no memory faults.
• Chunked SSM re-derive ships enabled by default, restoring long-context cache reuse from 0% to 99.9% above 12.5k tokens on hybrid model families.
• Extends verified support across 12 model families, including DSV4 Flash native composite caching, Gemma 4 mixed-SWA with audio, Qwen 3.6/3.8 hybrid lines with native MTP, and TurboQuant KV bundles.</description>
    </item>
    <item>
      <title>vMLX v1.6.29</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.29</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.29</guid>
      <pubDate>Sat, 15 Aug 2026 10:40:58 GMT</pubDate>
      <description>vMLX 1.6.29 brings hybrid prefix caching for 20x faster follow-up turns and stable 100k-token multiturn context.
• Adds hybrid prefix cache support that accelerates follow-up turns on long documents up to 20x (43.7k-token document follow-ups drop from 107s to 5s, byte-identical at temperature 0).
• Proves stable 100k-context multiturn: 97.6k-token conversations retrieve planted facts with 99.9% cache reuse and no memory faults.
• Ships chunked SSM re-derive enabled by default, restoring long-context cache reuse from 0% to 99.9% above 12.5k tokens on hybrid SSM model families.
• Expands verified per-family cache, parser, and generation-default support across 12 model families, including DSV4 Flash native composite caching, Gemma 4 mixed-SWA with audio, Qwen 3.6/3.8 hybrid lines with native MTP, and TurboQuant KV bundles.</description>
    </item>
    <item>
      <title>vMLX v1.6.28</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.28</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.28</guid>
      <pubDate>Fri, 14 Aug 2026 07:19:03 GMT</pubDate>
      <description>vMLX v1.6.28 adds Qwen3.6-27B bundle support with automatic multi-token prediction, delivering a 33% throughput gain.
• Supports the Qwen3.6-27B bundle line (and upcoming Qwen 3.8): the `qwen3_coder` tool-parser name now resolves to the XML-function parser, capability-only stamps route through the stock loader, and the bundled multi-token prediction head is constructed, quantized per per-module overrides, and engaged automatically at the stamp&apos;s trained speculative depth — yielding 23.3 to 31 tokens/sec (+33%) on the 4-bit bundle with no flags required.</description>
    </item>
    <item>
      <title>vMLX v1.6.28</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.28</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.28</guid>
      <pubDate>Fri, 14 Aug 2026 07:19:03 GMT</pubDate>
      <description>vMLX v1.6.28 adds Qwen3.6-27B bundle support with automatic multi-token prediction, delivering a measured 33% throughput gain.
• Supports the Qwen3.6-27B bundle line (and upcoming Qwen 3.8 line): the `qwen3_coder` tool-parser name now resolves to the XML-function parser, capability-only stamps route through the stock loader instead of the JANG codec, and the bundled multi-token prediction head is automatically constructed, quantized per per-module overrides, and engaged at the stamp&apos;s trained speculative depth — delivering 23.3 to 31 tokens/sec (+33%) on the 4-bit bundle with no flags required.</description>
    </item>
    <item>
      <title>vMLX v1.6.27</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.27</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.27</guid>
      <pubDate>Tue, 11 Aug 2026 09:47:36 GMT</pubDate>
      <description>vMLX 1.6.27 adds Muse Glimmer 30B with vision/video support, a 262,144-token prefix cache, and working Anthropic/Ollama reasoning controls.
• Adds support for Anthropic `thinking.budget_tokens` to actually cap the reasoning chain length at runtime (previously the budget was set only as a template hint and never consumed).
• Adds support for Ollama `think` string levels (`&apos;low&apos;`, `&apos;medium&apos;`, `&apos;high&apos;`) — clients sending these values now enable thinking and select the correct effort level instead of being silently ignored.
• Adds Muse Glimmer 30B support across all three bundles (`JANG_2D` / `4M` / `6M`), including its text tower, windowed vision tower, recipient-routed reasoning rail, and ATEM tool dialect.
• Adds single- and multi-image prompt handling for Muse Glimmer 30B, correctly identifying distinct visual elements within the same turn.
• Adds temporal-order video frame reading for Muse Glimmer 30B, so a clip fading from red to blue is answered &apos;red blue&apos; rather than reversed.
• Exposes four reasoning depth levels — **Low / Medium / High / Extra High** — in Chat Settings for Muse Glimmer 30B, replacing the previous binary Auto/On toggle.
• Expands the prefix cache index from ~64,000 tokens to 262,144 tokens, enabling long conversations to get cache reuse instead of falling off the end of the index; existing sessions are migrated automatically.
• Aligns paged RAM usage between the app and the command line for vision models, so both code paths now share the same cache behavior.</description>
    </item>
    <item>
      <title>vMLX v1.6.27</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.27</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.27</guid>
      <pubDate>Tue, 11 Aug 2026 09:47:36 GMT</pubDate>
      <description>vMLX 1.6.27 adds Muse Glimmer 30B with vision/video, fixes prefix cache to cover 262,144 tokens, and wires up Anthropic `thinking.budget_tokens` and Ollama `think` string levels.
• Ollama `think` string levels (`&apos;low&apos;` | `&apos;medium&apos;` | `&apos;high&apos;`) now enable thinking and select effort depth — previously these values were silently ignored and thinking never engaged.
• Anthropic `thinking.budget_tokens` now caps the reasoning chain at runtime — previously it set only a template hint that no runtime consumed, so the budget had no effect.
• Prefix cache index expanded from ~64,000 tokens to 262,144 tokens; existing sessions are migrated automatically, so long conversations now get reuse instead of reporting a cold-start miss.
• Adds Muse Glimmer 30B support across all three JANG bundles (`JANG_2D` / `4M` / `6M`), including its text tower, windowed vision tower, recipient-routed reasoning rail, and ATEM tool dialect.
• Muse Glimmer 30B reasoning depth is now selectable in Chat Settings as Low / Medium / High / Extra High — previously only the Auto/On thinking toggle was exposed, leaving three depths unreachable.
• Muse Glimmer 30B now correctly handles single- and multi-image prompts and reads video clips in temporal order.
• Each reasoning depth in Muse Glimmer 30B maintains its own prefix cache lineage; switching back to a previously used depth resumes that chain rather than starting cold.
• Vision models no longer use a slower cache path in the app — paged RAM is now enabled consistently between the app and the command line, restoring KV reuse for vision sessions.</description>
    </item>
    <item>
      <title>vMLX v1.6.25</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.25</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.25</guid>
      <pubDate>Sat, 08 Aug 2026 20:40:39 GMT</pubDate>
      <description>vMLX 1.6.25 delivers a new indexed-attention prefill kernel for DeepSeek V4 Flash, live KV cache rewind on retry, and honest 413 errors for over-ceiling context requests.
• New indexed-attention prefill kernel (on by default) boosts DeepSeek V4 Flash prompt processing from 347 to ~449 tokens/s at 15k context (+29%) and from 199 to ~316 tokens/s at 40k context (+58%), unlocking practical use of very long prompts.
• Aborting and retrying a long prompt now rewinds the live KV cache to the shared prefix instead of re-prefilling from scratch, cutting retry time-to-first-token at 15k context from 46s to 2.3s.
• Two-pass answer path now reuses the first pass&apos;s KV cache, eliminating full prompt re-prefill overhead on second-pass generation.
• KV pool quantization keeps cache RAM under 8 GB even near the model&apos;s maximum context length.
• Engine now advertises the true hardware context ceiling at startup, shrinks prefill chunks adaptively under Metal memory pressure, and returns a clean 413 instead of a Metal OOM for over-ceiling requests — eliminating the flat request timeout that killed healthy very-long prefills.</description>
    </item>
    <item>
      <title>vMLX v1.6.25</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.25</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.25</guid>
      <pubDate>Sat, 08 Aug 2026 20:40:39 GMT</pubDate>
      <description>vMLX 1.6.25 brings a 29–58% prefill speedup for DeepSeek V4 Flash long-context, KV cache rewind on retry, and honest 413 errors for over-ceiling requests.
• New indexed-attention prefill kernel (on by default) lifts DeepSeek V4 Flash prompt processing from 347 to ~449 tokens/s at 15k context (+29%) and from 199 to ~316 tokens/s at 40k context (+58%), sustaining 33+ tokens/s decode at both lengths.
• Engine rewinds the live KV cache to the shared prefix on abort/retry instead of re-prefilling from scratch — retry time-to-first-token at 15k context drops from 46s to 2.3s.
• Two-pass answer path now reuses the first pass&apos;s KV cache, eliminating full prompt re-prefill on the second pass.
• KV pool quantization keeps cache RAM under 8 GB even near the model&apos;s maximum context window.
• Engine now advertises the true hardware memory ceiling at startup, shrinks prefill chunks adaptively under Metal memory pressure, and returns a clean 413 for over-ceiling requests instead of crashing with a Metal OOM.</description>
    </item>
    <item>
      <title>vMLX v1.6.23</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.23</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.23</guid>
      <pubDate>Tue, 04 Aug 2026 10:58:16 GMT</pubDate>
      <description>vMLX 1.6.23 adds `DSV4_ANSWER_RESERVE` env var to guarantee visible answers from DeepSeek V4 Flash with thinking enabled.
• Adds `DSV4_ANSWER_RESERVE` environment variable to control how many tokens are reserved for the answer portion of DeepSeek V4 Flash output — set to `0` to disable the budget split entirely.
• DeepSeek V4 Flash now automatically reserves part of the output budget for the answer when thinking mode is active, using a bounded reserve (up to 2048 tokens) rather than a percentage — so long reasoning is not penalized at large token budgets.</description>
    </item>
    <item>
      <title>vMLX v1.6.23</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.23</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.23</guid>
      <pubDate>Tue, 04 Aug 2026 10:58:16 GMT</pubDate>
      <description>vMLX 1.6.23 adds `DSV4_ANSWER_RESERVE` env var to guarantee visible answers from DeepSeek V4 Flash with thinking enabled.
• Adds `DSV4_ANSWER_RESERVE` environment variable to control how many tokens are reserved for the answer portion of DeepSeek V4 Flash output — set to `0` to disable the budget split entirely.
• DeepSeek V4 Flash now automatically reserves part of the output budget for the answer when thinking mode is active, using a bounded reserve (up to 2048 tokens) rather than a percentage — so long reasoning is not penalized at large token budgets.</description>
    </item>
    <item>
      <title>vMLX v1.6.20</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.20</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.20</guid>
      <pubDate>Sun, 02 Aug 2026 12:31:57 GMT</pubDate>
      <description>vMLX 1.6.20 adds native DeepSeek V4 Flash runtime support with hardened cache, tool, and Activation-QAT controls
• Adds native DeepSeek V4 Flash (DSV4) runtime support including DSML tool support, sampling guidance, cache-tier capabilities, and pool-quantization behavior derived from the selected bundle.
• Adds explicit Activation-QAT control for DSV4 sessions, with effective state reported in the application.
• Server and Chat settings now distinguish native DSV4 compiled decode and pooled-cache state from generic TurboQuant and unsupported whole-model cache modes.
• Native DSV4 cache handling now preserves short append checkpoints, lossless L2 writes, valid eviction ancestry, partial-tail replay, SSD-only operation, and RAM-to-SSD refault.
• Health output now distinguishes the served model from the currently loaded model.
• Explicit visible-final instructions after tool use now enter a no-more-tools continuation pass, with terminal abort cleanup drained before the runtime returns to idle.
• Electron settings now warn when a DSV4 `top-p` value differs from bundle guidance and preserve remote model tool contracts.
• Release packaging now attests the native Python DSV4 encoder, nested affine defaults, mixed module quantization metadata, and the clean JANG runtime source, with separate Sequoia and Tahoe packaging/notarization contracts bound to one source revision.</description>
    </item>
    <item>
      <title>vMLX v1.6.20</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.20</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.20</guid>
      <pubDate>Sun, 02 Aug 2026 12:31:57 GMT</pubDate>
      <description>vMLX 1.6.20 adds native DeepSeek V4 Flash runtime support with hardened cache, tool, and Activation-QAT controls
• Adds native DeepSeek V4 Flash (DSV4) runtime support including DSML tool support, sampling guidance, cache-tier capabilities, and pool-quantization behavior derived from the selected bundle.
• Adds explicit Activation-QAT control for DSV4 sessions, with effective state reported in the application.
• Server and Chat settings now distinguish native DSV4 compiled decode and pooled-cache state from generic TurboQuant and unsupported whole-model cache modes.
• Native DSV4 cache handling now preserves short append checkpoints, lossless L2 writes, valid eviction ancestry, partial-tail replay, SSD-only operation, and RAM-to-SSD refault.
• Health output now distinguishes the served model from the currently loaded model.
• Explicit visible-final instructions after tool use now enter a no-more-tools continuation pass, with terminal abort cleanup drained before the runtime returns to idle.
• Electron settings now warn when a DSV4 `top-p` value differs from bundle guidance and preserve remote model tool contracts.
• Release packaging now attests the native Python DSV4 encoder, nested affine defaults, mixed module quantization metadata, and the clean JANG runtime source, with separate Sequoia and Tahoe packaging/notarization contracts bound to one source revision.</description>
    </item>
    <item>
      <title>vMLX v1.6.19</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.19</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.19</guid>
      <pubDate>Fri, 31 Jul 2026 16:15:27 GMT</pubDate>
      <description>vMLX 1.6.19 adds native-MTP diagnostics, model-owned sampler hydration, and macOS Tahoe packaging.
• Native-MTP health and profiling now expose bounded cache-lifecycle, acceptance, and phase-timing telemetry for Qwen MTP models.
• Ollama requests now preserve an explicitly supplied `top_k`, including `0`, instead of silently dropping it during request translation.
• Automatic prompt limits are now derived from the configuration of the model that is actually loaded, bounded by the model&apos;s declared context ceiling and current memory budget.
• Separate Apple-silicon downloads are now provided for macOS Tahoe and for Sequoia-compatible systems.
• New and restored Electron chats now hydrate temperature, Top P, Top K, repetition penalty, and output limits from the selected model bundle.
• MiniMax M2-family automatic cache storage uses the correctness-first q8 policy, with bundle-owned calibrated TurboQuant settings and explicit user selections remaining authoritative.
• Cache namespaces for compatible looped-transformer bundles now include the effective repeated-layer layout, preventing reuse between incompatible loop counts.
• Persistent cache payload encoding is moved off the inference-completion path after tensors are safely detached from MLX, reducing synchronous CPU work at the end of a request.
• New settings and compatibility messages are localized in the shipped English, Spanish, Japanese, Korean, and Chinese interfaces.
• Compatible Nanbeige looped-transformer JANG bundles now validate their loop count, effective cache-slot count, and runtime metadata before generation, rejecting inconsistent bundles rather than running with a silently short cache.
• Bounded Electron tool workflows now better enforce requested exactly-once tool calls while leaving other explicitly requested tools available until their results have been returned.</description>
    </item>
    <item>
      <title>vMLX v1.6.19</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.19</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.19</guid>
      <pubDate>Fri, 31 Jul 2026 16:15:27 GMT</pubDate>
      <description>vMLX 1.6.19 adds model-bundle-owned sampler defaults, native-MTP diagnostics, and separate macOS Tahoe vs Sequoia builds.
• Ollama requests now preserve an explicitly supplied `top_k`, including `0`, instead of silently dropping it during request translation.
• Automatic prompt limits are now derived from the configuration of the model that is actually loaded, bounded by the model&apos;s declared context ceiling and current memory budget.
• Native-MTP health and profiling expose bounded cache-lifecycle, acceptance, and phase-timing telemetry without changing generation policy.
• MiniMax M2-family automatic cache storage now uses the correctness-first q8 policy, with bundle-owned calibrated TurboQuant settings and explicit user selections remaining authoritative.
• MiniMax M3 native sparse-cache blocks are now scoped to the prompt-prefill shape and its N-1 prompt-boundary contract, preventing a token-identical prefix produced under a different prefill shape from restoring numerically different Lightning Indexer state.
• Compatible Nanbeige looped-transformer JANG bundles now validate their loop count, effective cache-slot count, and runtime metadata before generation, rejecting inconsistent bundles rather than running with a silently short cache.
• DeepSeek-V4 Flash preserves ratio-zero SWA rings and lossless q8 CSA/HCA pool segments through prompt snapshots and SSD reconstruction.
• Cache namespaces for compatible looped-transformer bundles now include the effective repeated-layer layout, preventing reuse between incompatible loop counts.
• Persistent cache payload encoding is moved off the inference-completion path after tensors are safely detached from MLX, reducing synchronous CPU work at the end of a request.
• Bounded Electron tool workflows now better enforce requested exactly-once tool calls while leaving other explicitly requested tools available until their results have been returned.
• New settings and compatibility messages are localized in shipped English, Spanish, Japanese, Korean, and Chinese interfaces.
• Separate Apple-silicon downloads are now provided for macOS Tahoe and for Sequoia-compatible systems.
• New and restored Electron chats now hydrate temperature, Top P, Top K, repetition penalty, and output limits from the selected model bundle without presenting missing values as saved zero-valued overrides.
• Text-only prompt accounting now follows the tokenizer&apos;s beginning-of-sequence behavior.</description>
    </item>
    <item>
      <title>vMLX v1.6.18</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.18</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.18</guid>
      <pubDate>Mon, 27 Jul 2026 19:38:42 GMT</pubDate>
      <description>vMLX 1.6.18 adds a process-wide SSD cache budget with LRU eviction and detailed cache reporting.
• Adds a process-wide SSD cache budget with coordinated LRU eviction and off-request-path publication, plus reporting of attempted prefixes, applied reuse, uncached suffixes, reconstruction, fallback prefill, and aggregate disk usage.
• Validates and atomically pins the exact ordered cache chain before reuse, preventing recycled paged-cache block IDs from receiving stale prefix credit.
• Accepts MiniMax tool arguments emitted as request-schema XML children without exposing native tool markup as visible answer text.
• Retains media across Responses tool continuations and permits local attachment playback in the Electron app.
• Derives fresh-session sampling, parser, output, template, and native MTP settings from the selected model bundle without converting inherited values into hidden saved overrides.
• Aligns Auto reasoning with each model bundle&apos;s native policy unless a request supplies an explicit supported thinking budget, keeping reasoning-marker aliases out of visible content.
Breaking changes:
• This release requires JANG 2.5.34; earlier JANG versions are no longer supported.</description>
    </item>
    <item>
      <title>vMLX v1.6.18</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.18</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.18</guid>
      <pubDate>Mon, 27 Jul 2026 19:38:42 GMT</pubDate>
      <description>vMLX 1.6.18 adds process-wide SSD cache budgeting with LRU eviction and expands MiniMax, Laguna, and LFM tool-handling coverage.
• Adds a process-wide SSD cache budget with coordinated LRU eviction and off-request-path publication, plus detailed reporting of attempted prefixes, applied reuse, uncached suffixes, reconstruction, fallback prefill, and aggregate disk usage.
• Accepts MiniMax tool arguments emitted as request-schema XML children without exposing native tool markup as visible answer text.
• Retains media across Responses tool continuations and permits local attachment playback in the Electron desktop app.
• Derives fresh-session sampling, parser, output, template, and native MTP settings from the selected model bundle without converting inherited values into hidden saved overrides.
• Aligns Auto reasoning with each model bundle&apos;s native policy unless a request supplies an explicit supported thinking budget, while keeping reasoning-marker aliases out of visible content.
• Hardens Laguna tool-result history, incomplete terminals, mixed-SWA cache reuse, and affine-JANG JIT policy.
• Improves KaTeX, Markdown, HTML, XML, currency, code-fence, and in-progress reasoning rendering, and stabilizes localized UI context during development reloads.
• Strengthens Sequoia/Tahoe packaging so bundled Python, JANG dependencies, release metadata, and artifacts remain tied to one source revision.
Breaking changes:
• This release requires JANG 2.5.34.</description>
    </item>
    <item>
      <title>vMLX v1.6.14</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.14</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.14</guid>
      <pubDate>Mon, 20 Jul 2026 19:41:50 GMT</pubDate>
      <description>vMLX v1.6.14 ships a signed-runtime checkpoint covering multimodal conversation-state isolation, progressive reasoning/content streaming, structured tool continuation, and architecture-aware KV cache behaviors.
• Adds multimodal conversation-state isolation, enabling independent context tracking across modalities in a single session.
• Adds progressive reasoning and content streaming, delivering incremental output during multi-step inference.
• Adds structured tool continuation, allowing tool-call sequences to resume and chain within a single inference pass.
• Adds architecture-aware KV cache behavior covering prefix, paged, block-disk, and TurboQuant cache policies, with model-family gates keeping cache settings explicit and model-safe.
• Both macOS Sequoia and Tahoe release artifacts are Developer ID signed, notarized, stapled, and Gatekeeper-accepted for out-of-the-box installation without security prompts.</description>
    </item>
    <item>
      <title>vMLX v1.6.14</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.14</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.14</guid>
      <pubDate>Mon, 20 Jul 2026 19:41:50 GMT</pubDate>
      <description>vMLX v1.6.14 ships a signed-runtime checkpoint covering multimodal conversation-state isolation, progressive reasoning/content streaming, structured tool continuation, and architecture-aware KV cache behaviors.
• Adds multimodal conversation-state isolation, enabling independent context tracking across modalities in a single session.
• Adds progressive reasoning and content streaming, delivering incremental output during multi-step inference.
• Adds structured tool continuation, allowing tool-call sequences to resume and chain within a single inference pass.
• Adds architecture-aware KV cache behavior covering prefix, paged, block-disk, and TurboQuant cache policies, with model-family gates keeping cache settings explicit and model-safe.
• Both macOS Sequoia and Tahoe release artifacts are Developer ID signed, notarized, stapled, and Gatekeeper-accepted for out-of-the-box installation without security prompts.</description>
    </item>
    <item>
      <title>vMLX v1.6.12</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.12</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.12</guid>
      <pubDate>Mon, 20 Jul 2026 06:30:27 GMT</pubDate>
      <description>vMLX 1.6.12 adds block-disk L2 as a usable prefix tier, hardens q4 TurboQuant KV storage, and fixes gateway lifecycle on client disconnect.
• Enables block-disk L2 as a usable prefix tier even when paged RAM is disabled, while preserving the RAM-first then disk-refault hierarchy when both tiers are active — covering partial-prefix restore, eviction, immediate-stop durability, and restart refault.
• Hardens q4 TurboQuant storage for eligible KV components and hybrid attention paths, keeping JANG affine, JANGTQ/MXTQ Hadamard-codebook, and base MLX MXFP as distinct formats; architecture-specific cache policies for DSV4, openPangu, MiniMax-M3, Gemma mixed-SWA, and other typed/native cache paths are preserved.
• Extends media ownership and cache-key coverage to Step and Nemotron Omni image, video, and audio paths while keeping MiniMax-M2.7 as text-only.
• Tightens progressive reasoning, content, and tool streaming plus terminal/usage ordering across Qwen, HY3, Step, MiniMax, Anthropic, Ollama, and Responses routes, including no-tool prompts and rejected/incomplete native tool markup.
• Improves model-derived UI accuracy for quant labels, parser/reasoning/cache settings, single-model swaps, session PID/sleep lifecycle, eager Start materialization, port/LAN rollback, and missing-model repoint/removal.</description>
    </item>
    <item>
      <title>vMLX v1.6.11</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.11</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.11</guid>
      <pubDate>Sun, 19 Jul 2026 01:10:39 GMT</pubDate>
      <description>vMLX 1.6.11 adds progressive streaming across Responses, Chat Completions, and Electron, plus extended L2 cache reuse for hybrid/mixed-attention model families.
• Streams post-reasoning and post-tool answers progressively across Responses, Chat Completions, and Electron UI instead of withholding output until completion.
• Extends architecture-typed prefix, paged, and block-disk L2 cache reuse to hybrid and mixed-attention model families.
• Adds media and cache routing support for Qwen, Bonsai, Gemma, Step, and MiniMax-M3 model families (MiniMax-M2.7 remains text-only).
• Hardens multi-turn tool continuation, terminal events, cancellation and recovery, and reasoning/content separation.
• Ships two separate macOS DMG builds: `vMLX-1.6.11-sequoia-arm64.dmg` (`macosx_14_0_arm64`, macOS 14.5+) and `vMLX-1.6.11-tahoe-arm64.dmg` (`macosx_26_0_arm64`).</description>
    </item>
    <item>
      <title>vMLX v1.6.10</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.10</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.10</guid>
      <pubDate>Wed, 15 Jul 2026 07:25:35 GMT</pubDate>
      <description>vMLX 1.6.10 adds JANG affine 1-bit/ternary bundle loading, paged block-disk L2 cache on by default, and Responses API as the Electron chat default.
• Enables paged block-disk L2 cache by default in the Electron UI, with an explicit opt-out available.
• Loads schema-2 JANG affine 1-bit and ternary text/VLM bundles with per-module quantization overrides.
• Keeps local Electron chat on the OpenAI `/v1/responses` API by default, with chat sampler defaults derived from the model bundle configuration.
• Preserves Qwen 3.5 hybrid GDN/SSM + attention cache routing, VL/video tensors, TurboQuant attention KV, and async SSM rederive.</description>
    </item>
    <item>
      <title>vMLX v1.6.7</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.6.7</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.6.7</guid>
      <pubDate>Mon, 13 Jul 2026 05:23:18 GMT</pubDate>
      <description>vMLX 1.6.7 enables paged KV cache by default for major text model families and turns reasoning on by default for all reasoning-capable models.
• Enables paged KV cache ON by default for autodetected text model families (Qwen3.5/3.6, Zaya, Nemotron-H, LFM2.5, Laguna, Hy3, Step-3.7) when continuous batching and prefix cache are active; `--use-paged-cache` / `--no-paged-cache` flags always take precedence.
• Turns reasoning ON by default for every reasoning-capable model family, with `max_thinking_tokens` honored on both streaming and non-streaming paths.
• Reconciles the Electron panel cache toggle, the `--use-paged-cache` launch flag, and the engine&apos;s effective cache policy so all three reflect the same state for every model family.</description>
    </item>
    <item>
      <title>vMLX v1.5.66</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.66</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.66</guid>
      <pubDate>Fri, 19 Jun 2026 00:40:18 GMT</pubDate>
      <description>vMLX v1.5.66 adds MiniMax-M3 autodetection, native MSA prefix-cache handling, and Metal wired-memory guidance for Apple Silicon.
• Adds MiniMax-M3 autodetection with reasoning/tool parser wiring and model-owned generation defaults, including native MSA prefix-cache handling.
• Surfaces model-owned max output behavior in both UI and CLI settings for MiniMax-M3 launch defaults.
• Adds user-facing Metal wired-memory guidance when a model launch fails due to Insufficient Memory on Apple Silicon.
• Retains Gemma 4 VL settings and media stress harness coverage for the scoped release path.</description>
    </item>
    <item>
      <title>vMLX v1.5.65</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.65</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.65</guid>
      <pubDate>Thu, 18 Jun 2026 20:13:26 GMT</pubDate>
      <description>vMLX 1.5.65 adds MiniMax-M3 and Gemma 4 VL compatibility with reasoning mode, VL image input, and multi-API support.
• Adds MiniMax-M3 (MM3) model support with startup configuration: prefix cache enabled, paged cache disabled, disk prompt cache enabled, JIT disabled, native MSA/Lightning sparse cache storage, and generic TurboQuant KV disabled.
• Supports MM3 reasoning mode in three states: off, on, and auto, across UI and API paths.
• Supports visible content emission and exact tool calls for MM3 across UI and API paths.
• Extends API compatibility to cover Responses, Chat Completions, Anthropic-compatible, and Ollama-compatible endpoints for MM3.
• Adds streaming support for MM3 across all covered API paths.
• Adds VL image input support for MM3.
• Adds prefix-cache reuse telemetry for MM3.
• Updates `latest.json` to point to the notarized 1.5.65 Sequoia and Tahoe DMGs.</description>
    </item>
    <item>
      <title>vMLX v1.5.64</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.64</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.64</guid>
      <pubDate>Thu, 18 Jun 2026 13:50:45 GMT</pubDate>
      <description>vMLX 1.5.64 adds MiniMax-M3 and Gemma 4 VL model support with streaming reasoning fixes and cache configuration defaults.
• Adds MiniMax-M3 (MM3) model support across live UI, Chat Completions, Responses, Anthropic-compatible, Ollama-compatible, and streaming tool/image paths.
• MiniMax-M3 startup defaults: prefix cache enabled, paged cache disabled, disk prompt cache enabled, JIT disabled, native MSA/Lightning sparse cache storage enabled, generic TurboQuant KV disabled.
• Adds Gemma 4 E2B, E4B, 12B, 26B, and 31B JANG_4M VL variants with live UI, API, and cache coverage, plus 26B and 31B MXFP4 visual row support.
• Streaming reasoning-only responses now produce a bounded visible-answer pass for supported reasoning models, preventing blank output when the model emits hidden reasoning only.
• Updates `latest.json` to point to the notarized 1.5.64 Sequoia and Tahoe DMGs, which are Developer ID signed, Apple notarized, stapled, and Gatekeeper accepted.</description>
    </item>
    <item>
      <title>vMLX v1.5.63</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.63</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.63</guid>
      <pubDate>Thu, 18 Jun 2026 10:11:08 GMT</pubDate>
      <description>vMLX v1.5.63 adds MiniMax-M3 and Gemma 4 E2B/E4B/12B MXFP4 vision/text compatibility with multimodal routing and reasoning controls.
• Adds MiniMax-M3 compatibility with multimodal routing, reasoning controls (off/on/auto), tool calls, and prefix-cache hit stability across Chat/Responses/Anthropic/Ollama endpoints.
• Adds Gemma 4 E2B, E4B, and 12B MXFP4 vision/text compatibility with reasoning controls, image input, tool calls, and cache hits across Chat/Responses/Anthropic/Ollama and streaming surfaces.
• Preserves model-owned generation defaults for supported startup paths.
• MiniMax-M3 enforces its native MSA/Lightning sparse cache policy: paged cache disabled, TurboQuant KV skipped for native MSA, and JIT disabled for the dynamic sparse-cache path.</description>
    </item>
    <item>
      <title>vMLX v1.5.56</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.56</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.56</guid>
      <pubDate>Fri, 05 Jun 2026 19:50:02 GMT</pubDate>
      <description>vMLX 1.5.56 preserves `VMLX_VLM_IMAGE_PREFILL_BUFFER_GB` overrides while switching to dynamic Metal working-set-based buffer sizing.
• Retains `VMLX_VLM_IMAGE_PREFILL_BUFFER_GB` as a hard override for users who need an explicit VLM image prefill buffer guard.
• Defaults Gemma 4 unified chat/image requests to visible-answer mode unless thinking is explicitly requested.</description>
    </item>
    <item>
      <title>vMLX v1.5.52</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.52</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.52</guid>
      <pubDate>Tue, 02 Jun 2026 18:06:37 GMT</pubDate>
      <description>vMLX 1.5.52 reads greedy generation defaults (temperature, top-p, top-k) directly from JANG model metadata and ships Sequoia- and Tahoe-compatible DMGs.
• Reads and applies model-owned greedy generation defaults (`temperature 0.00`, `top_p 1.0`, `top_k off`) declared in JANG bundle metadata, so sampling behaviour is driven by the model file rather than hidden app-side assumptions.
• Updates the local Step-3.7-Flash JANG metadata path so startup settings (default `temperature=0.0`, `top_p=1.0`, `top_k=0`) derive from model files; explicit request parameters still override model defaults.
• Builds both public DMG flavors from the same source: Sequoia-compatible `macosx_14` wheels and Tahoe-native `macosx_26` wheels, with Developer ID signing, notarization, stapling, and Gatekeeper acceptance.</description>
    </item>
    <item>
      <title>vMLX v1.5.47</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.47</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.47</guid>
      <pubDate>Fri, 22 May 2026 17:07:02 GMT</pubDate>
      <description>vMLX 1.5.47 adds Tahoe-native DMG for macOS Tahoe, tightens parser boundaries across model families, and expands DSV4 release guards.
• Adds a Tahoe-native DMG (`vMLX-1.5.47-tahoe-arm64.dmg`) for macOS Tahoe users who want native `macosx_26` MLX wheels, alongside the existing Sequoia-compatible DMG (`vMLX-1.5.47-sequoia-arm64.dmg`).
• Adds stronger parser boundaries so non-reasoning families such as Qwen2 and Qwen2-VL no longer silently inherit reasoning parsers, while MiniMax, DSV4, ZAYA, Hy3, Gemma, and GLM parser rows remain covered.
• Adds DSV4-specific release guards for stale native-MTP launch args, cache/prefix policy, and JANG source parity.</description>
    </item>
    <item>
      <title>vMLX v1.5.45</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.45</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.45</guid>
      <pubDate>Wed, 20 May 2026 12:51:55 GMT</pubDate>
      <description>vMLX 1.5.45 adds MCP config auto-discovery, chained tool calls across multi-round chats, and localized What&apos;s New notices.
• MCP config auto-discovery now loads `mcp.json` or `mcp.yaml` from the working directory or user config paths without requiring a launch flag, and MCP policy flows through sessions and gateway routes.
• Built-in coding tools and MCP tools can now chain across multi-round chats using structured `tool_calls`, with a text-chat watchdog that clears stuck &apos;Generating tool call&apos; states.
• Adds support for Qwen3.6 native MTP, JANG/JANGTQ group-size detection, DSV4 cache policy, and TurboQuant/L2/paged cache telemetry with stricter release gates.
• The in-app What&apos;s New notice is now localized in English, Spanish, Japanese, Korean, and Chinese, covering MCP, MTP, cache, and updater safety notes.</description>
    </item>
    <item>
      <title>vMLX v1.5.39</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.39</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.39</guid>
      <pubDate>Sun, 17 May 2026 22:58:09 GMT</pubDate>
      <description>vMLX v1.5.39 adds native MTP health reporting with depth, scope, tensor counts, and gating reasons, plus Qwen 3.6 and Kimi K2.6 runtime support.
• Native MTP health/status now reports depth, scope, tensor counts, and gating reasons.
• Supports Qwen 3.6 MTP artifacts defaulting to D3, with opt-in model-local tuning sidecars.
• Qwen 3.6 MTP+VL app detection preserves multimodal behavior for artifact-backed affine-JANG MTP+VL bundles when indexed MTP and vision tensors are present.
• Adds Source/PyPI runtime patch parity for Kimi K2.6 MLA and Gemma 4 vision pixel-value handling.</description>
    </item>
    <item>
      <title>vMLX v1.5.37</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.5.37</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.5.37</guid>
      <pubDate>Sat, 16 May 2026 09:46:35 GMT</pubDate>
      <description>vMLX 1.5.37 adds `max_prompt_tokens`, `max_context_tokens`, and `max_context` aliases for prompt context admission across OpenAI, Anthropic, Ollama, and gateway paths.
• Adds `max_prompt_tokens`, `max_context_tokens`, and `max_context` aliases for controlling request/session prompt context admission across OpenAI, Anthropic, Ollama, and gateway paths.
• Applies model-declared generation defaults at session startup, removing panel-side sampling fallbacks.
• Scopes sampling overrides to the current chat or API request instead of carrying stale per-model state across requests.</description>
    </item>
    <item>
      <title>vMLX v1.4.7</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.4.7</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.4.7</guid>
      <pubDate>Fri, 01 May 2026 04:17:48 GMT</pubDate>
      <description>vMLX v1.4.7 promotes `VMLX_OMNI_BACKEND` to a CLI flag, unlocking a fast native MLX Stage-2 path for Nemotron-Omni.
• Promotes `VMLX_OMNI_BACKEND` from an environment variable to a CLI flag, allowing the panel and Swift Settings UI to switch the Nemotron-Omni multimodal path between Stage-1 (bit-exact PyTorch bridge, default) and Stage-2 (native MLX RADIO + Parakeet, ~15–21x faster encoders and ~82 tok/s decode on M4 Max).
• Adds Stage-2 native MLX backend for Nemotron-Omni, delivering ~15–21x faster encoders and ~82 tok/s decode on M4 Max hardware (off by default pending Wave-4 quality validation).
• Confirms Qwen 3.6 VL is fully wired on both the Python and Swift sides.</description>
    </item>
    <item>
      <title>vMLX v1.4.2</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.4.2</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.4.2</guid>
      <pubDate>Fri, 01 May 2026 00:47:48 GMT</pubDate>
      <description>vMLX v1.4.2 adds Laguna and Mistral-Medium-3.5 loaders, extends multimodal routing to /v1/responses and /v1/messages, and fixes Omni bundle boot.
• Extends `OmniMultimodalDispatcher` multimodal routing (RADIO + Parakeet encoders) to `/v1/responses` and `/v1/messages`, joining `/v1/chat/completions` — all 6 modality surfaces (text, image, audio, video) now dispatch correctly for Nemotron-3-Nano-Omni.
• Adds `_MEDIA_TYPES` entries in `_responses_input_to_messages` for OpenAI Responses canonical types `input_image`, `input_video`, `input_audio`, and `input_text`, normalizing them to chat-completions envelopes.
• Adds `loaders/load_laguna.py` routing through `jang_tools.laguna.runtime.load` for Laguna (poolside) 33B/3B agentic-coding MoE (hybrid SWA+full attention, 256 routed experts top-8 + 1 shared, dual-RoPE); registered as a silver-tier model.
• Adds `loaders/load_mistral3.py` for Mistral-Medium-3.5-128B (ministral3 inner), a dense GQA 96/8 model with 256K YaRN context and PIXTRAL vision; registered as a silver-tier model.</description>
    </item>
    <item>
      <title>vMLX v1.4.1-swift</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.4.1-swift</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.4.1-swift</guid>
      <pubDate>Thu, 30 Apr 2026 22:42:22 GMT</pubDate>
      <description>vMLX v1.4.1-swift adds MCP HTTP/SSE transport and NemotronHOmni VLM support alongside UI and cache improvements.
• Adds MCP HTTP/SSE transport (real implementation) via mlxstudio#31, enabling Model Context Protocol over HTTP and Server-Sent Events.
• Adds NemotronHOmni VLM factory wiring and capability detector, bringing support for the NemotronHOmni vision-language model.
• Adds &apos;Delete model files…&apos; context menu item in the chat picker for in-app model file management.</description>
    </item>
    <item>
      <title>vMLX v1.4.1</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.4.1</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.4.1</guid>
      <pubDate>Thu, 30 Apr 2026 22:42:04 GMT</pubDate>
      <description>vMLX v1.4.1 adds model deletion API, image-gen cancellation, MCP HTTP+SSE transport, and `vmlx[mxtq]` install extras.
• Adds `POST /api/delete` endpoint for model deletion with model-directory bounding (refuses paths outside the model root and path-traversal attempts).
• Adds `POST /v1/images/cancel` endpoint and ImageGenEngine.cancel() method, plus a disconnect-watchdog so browser close or the Stop button truly aborts image generation instead of OOM-after-cancel.
• Adds `[mxtq]` and `[jang]` install extras (`pip install vmlx[mxtq]`) for JANG/MXTQ/DSV4/MiniMax-JANG/Nemotron-Omni bundles.
• Accepts Anthropic-style `reasoning: {effort: ...}` and `reasoning: {type:&apos;enabled&apos;, budget_tokens:N}` request bodies, normalizing them into `reasoning_effort` / `enable_thinking` internally.
• Replaces the stdio-only MCP guard with a full HTTP+SSE transport implementation on the Swift side: streaming via URLSession.bytes(for:), legacy `endpoint`-event handling, and Streamable-HTTP inline-JSON-RPC fallback.
• Adds &apos;Delete model files…&apos; context menu in the Swift app chat picker, backed by the new `POST /api/delete` endpoint.
• Registers NemotronH-Omni in VLMModelFactory and the processor registry; auto-promotes `nemotron_h` → `nemotron_h_omni` when `config_omni.json` is present alongside `config.json`.
Breaking changes:
• macOS 14.4.x is no longer supported — the bundled MLX wheel requires libc++ symbols added in macOS 14.5 (Sonoma/Sequoia/Tahoe); running on 14.4.x will exit with an actionable error.</description>
    </item>
    <item>
      <title>vMLX v1.3.34</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.3.34</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.3.34</guid>
      <pubDate>Thu, 09 Apr 2026 17:48:13 GMT</pubDate>
      <description>vMLX v1.3.34 adds deferred SSM re-derive for hybrid thinking models, cutting multi-turn cache misses on Nemotron and Qwen3.5-VL.
• Adds deferred SSM re-derive: after generation completes on hybrid SSM + thinking models (Nemotron, Qwen3.5-VL), the scheduler queues an idle-time prefill pass on prompt tokens only, storing clean SSM state so the next conversation with the same prompt prefix gets KV + SSM cache hits instead of a full re-prefill.
• Adds _ensure_mx_array() for dtype normalization: `input_ids` is cast to `mx.int32` and `pixel_values` to `mx.array`, enabling batched VLM inference on Mistral 3 / Pixtral.
• mx.compile() now targets `language_model.model` only for VLM engines, preserving the wrapper&apos;s `.config` attribute and fixing JIT + MLLM compatibility.
• _get_n_kv_heads() now scans `cfg.text_config` as a nested fallback, resolving 100% prefix-cache miss on Gemma 4 VLM where `num_key_value_heads` lives inside `model.config.text_config`.</description>
    </item>
    <item>
      <title>vMLX v1.3.31</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.3.31</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.3.31</guid>
      <pubDate>Thu, 09 Apr 2026 00:53:20 GMT</pubDate>
      <description>vMLX v1.3.31 adds cross-session cache sharing, Aho-Corasick stop matching, Gemma 4 support, and a public `/v1/cache` stats endpoint.
• Adds `--prefix-cache-max-bytes` CLI flag to enforce a byte-budget eviction limit on the prefix cache.
• Exposes `size` and `max_entries` fields on the `/v1/cache` stats endpoint for live cache introspection.
• Adds `cache_type` column and per-type bucket eviction to the disk SQLite cache, with priority eviction and last-fetch type round-trip in L1 backfill.
• Extracts `SSMCompanionCache` to `vmlx_engine/utils/ssm_companion_cache.py` with a per-entry `is_complete` flag for SSM companion cache lifecycle tracking.
• Adds compute_model_cache_key() for content-derived cache identity that survives JIT reload and prevents cross-config pollution.
• Adds `_compute_segment_boundaries` helper to `engine/batched.py`, firing segment boundaries on memory-aware and paged schedulers (not just legacy).
• Adds `is_hybrid_ssm_cache`, `is_hybrid_ssm_model`, and `is_hybrid_ssm_config` polymorphic helpers for hybrid SSM model detection.
• Adds `is_mla_model` centralized helper that walks `top → text_config → language_config → llm_config` for MLA model detection.
• Adds Gemma 4 native text MoE local-port shim with forward-compat that defers to upstream when available.
• Sets `trust_remote_code=True` as the default in `utils/tokenizer.py`, unblocking custom tokenizers without manual configuration.
• Enables LRU+Trie cross-session prefix sharing on `MemoryAwarePrefixCache` (production default) with system → user → assistant priority eviction.
• Adds `cache_type` LRU and per-type bucket eviction on `BlockAwarePrefixCache` for paged and hybrid auto-switch scenarios.
• Surfaces SSM companion cache stats (nbytes_mb) to the CachePanel UI.
• Backports `BatchMambaCache.lengths`, `advance`, `prepare`, and `finalize` from mlx-lm 0.31.2.
• Caches is_mllm_model() result in `api/utils.py` to eliminate repeated INFO log spam.
• Adds `scheduler.deep_reset` wiring of reset_factory_cache() on shutdown.
• Bumps mlx-lm dependency from 0.30.2 to 0.31.2.
• Removes orphan disk-streaming artifacts (`ssd_generate`, `streaming_wrapper`, `weight_index`) superseded by the memory_cache and disk_cache architecture.
Breaking changes:
• Orphan disk-streaming artifacts `ssd_generate`, `streaming_wrapper`, and `weight_index` have been removed; setups relying on them must migrate to the memory_cache + disk_cache architecture.</description>
    </item>
    <item>
      <title>vMLX v1.3.25</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.3.25</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.3.25</guid>
      <pubDate>Fri, 03 Apr 2026 01:43:58 GMT</pubDate>
      <description>vMLX v1.3.25 adds full Gemma 4 support with reasoning/tool parsers and a new `--default-enable-thinking` CLI flag.
• Adds `--default-enable-thinking` CLI flag to control thinking mode at server startup.
• Adds Gemma 4 tool parser supporting the native `&lt;|tool_call&gt;call:name{args}&lt;tool_call|&gt;` format for function-calling workflows.
• Adds Gemma 4 reasoning parser handling the `&lt;|channel&gt;thought...&lt;channel|&gt;` protocol with Auto/On/Off thinking modes.
• Adds `&lt;turn|&gt;` as a stop token alongside `&lt;eos&gt;` for Gemma 4 generation.
• Adds `&lt;|tool_call&gt;` to tool call marker detection for streaming responses.</description>
    </item>
    <item>
      <title>vMLX v1.3.6</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.3.6</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.3.6</guid>
      <pubDate>Sat, 21 Mar 2026 23:21:38 GMT</pubDate>
      <description>vMLX v1.3.6 adds disk streaming for RAM-oversize models, Mistral Small 4 (119B) support, and universal MoE JANG gate dequant.
• Adds `--stream-from-disk` flag to load models larger than available RAM by leveraging macOS SSD paging (~7.4 GB/s); automatically disables all caching, and exposes a virtual memory budget slider in the session settings UI.
• Adds `model_type: mistral4` config value to support Mistral Small 4 (119B) with MLA attention, 128-expert MoE, and YaRN interleaved RoPE, routed through the mlx-lm text path and mistral3 VLM wrapper.
• Extends JANG gate dequantization to any model with `n_routed_experts &gt; 0` (Mistral 4, DeepSeek V3, etc.), previously limited to Nemotron only.</description>
    </item>
    <item>
      <title>vMLX v1.3.0</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.3.0</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.3.0</guid>
      <pubDate>Fri, 20 Mar 2026 18:55:55 GMT</pubDate>
      <description>vMLX v1.3.0 adds Nemotron-H support with hybrid SSM caching and session status banners with smooth token streaming.
• Adds Nemotron-H JANG engine support with gate dequant (8→2 bit), fc1/fc2 rename, and MTP filter (42 GB GPU, 46 tok/s).
• Extends hybrid SSM full cache — prefix, paged, and disk caching — to Qwen3.5-A3B and Nemotron-H models.
• Adds session status banners showing loading, sleeping, and stopped states in chat UI.
• Adds renderer-side typewriter animation via requestAnimationFrame (rAF) for smooth token streaming.
Breaking changes:
• Think-completion seed injection has been removed entirely.
• The `STREAM_THROTTLE_MS` configuration, _is_vlm_config() function, and vestigial weights parameter have been removed.</description>
    </item>
    <item>
      <title>vMLX v1.0.7</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.0.7</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.0.7</guid>
      <pubDate>Wed, 18 Mar 2026 02:01:17 GMT</pubDate>
      <description>vMLX v1.0.7 adds full mflux model support, img2img variation generation, and an Iterate UI for prompt-based image refinement.
• Adds support for Klein 4B/9B, Kontext, Fill, Qwen Image, and FIBO mflux models in the model picker with correct class dispatch.
• Enables img2img variation generation for Generate models via source image and strength parameter.
• Adds &apos;Iterate&apos; UI action on any generated image to create variations with modified prompts.
• Persists settings — steps, size, guidance, strength, and negative prompt — across restarts.
• Adds &apos;Code tab&apos; placeholder (marked Coming Soon) to the UI.</description>
    </item>
    <item>
      <title>vMLX v1.1.0</title>
      <link>https://github.com/jjang-ai/vmlx/releases/tag/v1.1.0</link>
      <guid isPermaLink="true">https://github.com/jjang-ai/vmlx/releases/tag/v1.1.0</guid>
      <pubDate>Fri, 06 Mar 2026 04:03:35 GMT</pubDate>
      <description>vMLX v1.1.0 adds speculative decoding, embedding model preloading, and server-wide generation defaults
• Adds `/v1/embeddings` endpoint support via a pre-loadable separate embedding model, enabling semantic search and RAG workflows without a separate server.
• Adds speculative decoding configuration with draft model and `num-draft-tokens` controls for 20–90% generation speedup.
• Adds server-wide default temperature and top-p sliders for generation defaults.
• Adds a dismissible auto-update checker banner that notifies when a new version is available.</description>
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