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    <title>Building software with AI — USASI news</title>
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    <description>Dated, sourced news items about the organizations and records featured in the USASI Building software with AI hub. Independent project. Not a United States government website.</description>
    <language>en-us</language>
    <lastBuildDate>Fri, 09 Oct 2026 12:00:00 GMT</lastBuildDate>
    <item>
      <title>TRL 1.15.0 released with a fused LM head enabled by default</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/trl-1-15-0-released/</link>
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      <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
      <category>release</category>
      <description>On October 8, 2026 Hugging Face published version 1.15.0 of TRL on GitHub. The release notes say the SFT, DPO, KTO, GRPO, RLOO and Distillation trainers now score tokens with a fused LM head, a Triton kernel that avoids building the full logits tensor, and that it is on by default; scoring this way needs Triton on a GPU. The notes list breaking changes: a PEFT adapter on `lm_head` now raises an error, and `use_liger_kernel=True` is deprecated in the GRPO, RLOO, DPO and KTO trainers and will be removed in v2.0.0. The release also adds selective activation checkpointing in SFT. USASI has not tested these changes.</description>
    </item>
    <item>
      <title>MLflow 3.17.0 released with fine-grained permissions and TypeSafe judge support</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/mlflow-3-17-0-released/</link>
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      <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
      <category>release</category>
      <description>On October 7, 2026 the MLflow project published version 3.17.0 on GitHub. The release notes list &quot;Jev Decisions Across Evaluation, Gateway, and Tracing&quot;, which lets TypeSafe models be used in built-in scorers and custom judges, and &quot;Fine-Grained Permissions for Shared Resources&quot;, with wildcard grants and explicit DENY for contained resource types such as runs, traces, and versions. They also describe opt-in daily summaries for trace analytics. The notes say SQL-backed servers need a coordinated `mlflow db upgrade` with all writers stopped, and that mixed-version rolling upgrades are not supported. USASI has not tested these changes.</description>
    </item>
    <item>
      <title>Diffusers 0.41.0 adds a Qwen-Image 2.1 pipeline and deprecates ONNX support</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/diffusers-0-41-0-released/</link>
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      <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
      <category>release</category>
      <description>On October 6, 2026 Hugging Face published Diffusers 0.41.0 on GitHub. The release notes say it adds Qwen-Image 2.1, with text-to-image generation, image editing, native transparency, and LoRA training, and introduces tensor-parallel checkpoint loading in which each rank reads its own slice of sharded weights. The notes deprecate ONNX support in favor of Optimum and remove previously deprecated APIs, including the `LuminaText2ImgPipeline` and `Lumina2Text2ImgPipeline` aliases. They also say minor releases will now be coordinated around new model integrations, as in Transformers. Qwen-Image 2.1 is a third-party model and is not a catalog entry; the notes do not state its license.</description>
    </item>
    <item>
      <title>Transformers 5.19.0 adds the EmbeddingGemma2 multimodal embedding model</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/transformers-5-19-0-adds-embeddinggemma2/</link>
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      <pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate>
      <category>release</category>
      <description>On October 6, 2026 Hugging Face published version 5.19.0 of its Transformers library on GitHub. The release notes add support for EmbeddingGemma2, which the notes describe as a multimodal embedding model from Google built on the Gemma 4 architecture that encodes text, images, audio, and video into a shared 768-dimensional vector space and uses Matryoshka Representation Learning so embeddings can be truncated to 512, 256, or 128 dimensions. The notes also list breaking changes: every mixture-of-experts model that computes router logits now returns them when `output_router_logits=True`, the `&quot;paged|&quot;` prefix for SDPA and flash attention implementations is deprecated in favor of continuous batching on the regular functions, and `Owlv2ForObjectDetection.embed_image_query` now selects the query box by objectness score. EmbeddingGemma2 itself is a Google model and is not a separate catalog entry; USASI has not tested these changes.</description>
    </item>
    <item>
      <title>vLLM 0.31.0 released with a preload command for faster engine restarts</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/vllm-0-31-0-released/</link>
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      <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
      <category>release</category>
      <description>On October 5, 2026 the vLLM project published version 0.31.0 on GitHub. The release notes highlight a new `vllm preload` command that launches a weight-cache daemon to keep post-quantized weights in GPU memory across engine restarts, and experimental engine snapshots (`vllm snapshot create/restore`) that use CRIU to restore a fully initialized TP1 engine. Under security, the notes say per-request `mm_processor_kwargs` and `media_io_kwargs` are now rejected unless `--trust-request-mm-kwargs` is set. The release also lists breaking changes, including the removal of `tokenizer_mode=&quot;slow&quot;`, and performance work for DeepSeek-V4.1-Flash. USASI has not tested any of these changes.</description>
    </item>
    <item>
      <title>llama.cpp server adds support for decision models through a /v1/systemone endpoint</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/llama-cpp-server-adds-decision-model-support/</link>
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      <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
      <category>release</category>
      <description>On October 2, 2026 the ggml team announced on the Hugging Face blog that the llama.cpp server now supports decision models through a new /v1/systemone endpoint. Decision models score the options a caller supplies instead of generating text. The pull request that adds the endpoint (#29818) was merged into the llama.cpp repository the same day; it describes these models as wrappers around existing embedding-model architectures and adds a decision head and server handling for them. The announcement lists six supported models (Julia-1, Laya, Kev-4B, lev, OpenJev, and Clef), while the pull request itself covers five of them and lists Clef support as a planned follow-up.</description>
    </item>
    <item>
      <title>CoreWeave launches Forge, bringing Weights &amp; Biases Models into one AI development platform</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/coreweave-launches-forge-with-weights-and-biases/</link>
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      <pubDate>Thu, 01 Oct 2026 12:00:00 GMT</pubDate>
      <category>release</category>
      <description>CoreWeave announced CoreWeave Forge on September 30, 2026, a development platform that brings training, inference, evaluation, and agent development into one environment. Forge includes Weights &amp; Biases Models for tracking and comparing experiments. Weights &amp; Biases, which CoreWeave acquired in 2025, says W&amp;B software-as-a-service accounts now sign in through CoreWeave Forge and that self-hosted and dedicated deployments are unchanged. The open-source wandb client library remains available under the MIT License.</description>
    </item>
    <item>
      <title>NVIDIA agrees to acquire Hugging Face; the deal has not yet closed</title>
      <link>https://unitedstatesofamericasuperintelligence.com/news/nvidia-agrees-to-acquire-hugging-face/</link>
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      <pubDate>Tue, 29 Sep 2026 12:00:00 GMT</pubDate>
      <category>acquisition</category>
      <description>NVIDIA entered into a definitive agreement to acquire Hugging Face, Inc. on September 2, 2026, and announced it the next day. NVIDIA's Form 8-K says the transaction is expected to close in the first half of 2027, subject to closing conditions including regulatory approvals, so Hugging Face is still listed as a privately held company with no parent organization. NVIDIA says the Hugging Face platform will stay open to models from other developers and will not require NVIDIA hardware.</description>
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