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Unsloth

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Headlines · 9

  1. Notebooks released for fine-tuning the d1-3B decision model

    A post reposted by UnslothAI on X points to two notebooks in the Liquid4All cookbook for fine-tuning the d1-3B decision model on your own data, one built on Unsloth and one on Hugging Face TRL. The post says the notebooks cover preparing training data and the fine-tuning workflow, and claims the process fits in 30 minutes on a free-tier GPU. The time and ease claims come from the post and have not been independently verified.

    UnslothAI (X) · 🔥 6
  2. RTX Spark review: beats M5 Max in local AI, lags in gaming

    Tom's Hardware tested Nvidia's RTX Spark platform in a top-trim Microsoft Surface Laptop Ultra carrying the full RTX Spark Superchip with 20 CPU cores, 6,144 CUDA cores and 128GB of LPDDR5X-9400 memory. Running Artificial Analysis's new end-to-end agentic benchmark AgentPerf-local with Qwen 3.8 27B and Qwen 3.6 35B A3B, the RTX Spark system finished about 25% faster than a 14-inch MacBook Pro with Apple's M5 Max, while AMD's Ryzen AI Max+ 395 (Strix Halo) needed roughly twice as long, about 40 minutes, on the heavier test. Gaming is a weaker point: roughly 300 GB/s of shared memory bandwidth, Arm CPU cores and the overhead of the Prism x86 emulator leave frame rates and input latency behind dedicated RTX 50-series gaming laptops, and the reviewer leans on DLSS Frame Generation to compensate. The outlet notes that launch embargoes limit this first round to platform-wide results, with battery life, build and display details to follow.

    Tom’s Hardware · 🔥 4
  3. Soup v0.76.0 ships layer streaming to fine-tune 8B models on 4 GB laptop GPUs

    Soup is a command-line tool that fine-tunes LLMs from a single YAML config; its v0.76.0 release now refuses at load time settings that were previously accepted but never actually applied, and includes breaking changes such as no longer holding out validation rows by default for grpo, ppo and online_dpo. Its opt-in layer streaming feature (stream_layers: true, still BETA) keeps the frozen base weights out of VRAM and feeds them to the GPU one decoder layer at a time: the project records Llama-3.1-8B-Instruct + NF4 on a 4 GB RTX 3050 Laptop at 3.32 GB peak and 119.6 tok/s (v0.72.2, before the correctness repair), while a later post-repair run reads 208.6 tok/s in a 2.40 GB peak — the project notes the two are not directly comparable because clock speeds differ. The release also evaluates validation loss each epoch or every N steps, and reads the streaming disk tier on a background thread via direct I/O with optional striping across multiple NVMe drives.

    GitHub Trending · Python · 🔥 7
  4. NVIDIA releases Nemotron 3.5 Super VL, an open 120B multimodal agent model

    NVIDIA released Nemotron 3.5 Super VL, an open multimodal model built on a 120-billion-parameter mixture-of-experts architecture with 12 billion active parameters, combining reasoning, instruction following and tool use with text, image, document and video understanding for long-running agents. NVIDIA says it delivers up to 5x the agentic-task throughput of comparable open models on a single GPU, and reports 80.8% on the CharXiv chart-reasoning benchmark (84% with a Python code interpreter), 63.44% on Hermes Bench and 87.05% on LangChain behavioral evaluations. The launch also includes edge models Nemotron 3.5 Bolt 74B-A9B 24GB, available now, and Bolt E4B 4GB, due Oct. 23; weights are available via Hugging Face, OpenRouter and ModelScope, with day-0 support from vLLM and Baseten.

    vLLM Blog · 🔥 68
  5. Hugging Face Transformers tops GitHub Trending

    Hugging Face's Transformers library appeared on GitHub Trending's daily list. The project positions itself as the model-definition framework for state-of-the-art text, vision, audio, video and multimodal models, for both inference and training, and its documentation says there are over 1M Transformers model checkpoints on the Hugging Face Hub. It says the library interoperates with training frameworks such as Axolotl, Unsloth, DeepSpeed, FSDP and PyTorch-Lightning, and with inference engines and modeling libraries including vLLM, SGLang, TGI, llama.cpp and mlx, and requires Python 3.10+ and PyTorch 2.6+.

    GitHub Trending（每日） · 🔥 0
  6. Unsloth ships desktop app and Studio web UI

    The open-source Unsloth project is trending on GitHub's Python list, and its README now describes three ways to use it: the native Unsloth Desktop app for Windows, macOS and Linux, the Unsloth Studio web UI, and code-based Unsloth Core. The project says it can run and train LLMs plus diffusion, embedding and audio models, naming Qwen3.8, DeepSeek-V4, Gemma 4, GLM-5.3-Flash, Kimi K3 and MiniMax-H3, and its Unsloth Start command wires local models into agents such as Claude Code and Codex. The README also claims 2x faster training with up to 70% less VRAM and no accuracy loss — figures that come from the project's own documentation.

    GitHub Trending · Python · 🔥 0
  7. Unsloth trains a decision model on Qwen3.5-2B in about 2 minutes on one DGX Spark

    Unsloth shared an Ivan Fioravanti demo showing a "decision model" trained in roughly two minutes on a single NVIDIA DGX Spark using Unsloth, with Qwen3.5-2B as the base model. The post points to Unsloth's documentation page on training your own decision model with Unsloth. The source gives no details on the model's intended use, evaluation results, or training data.

    UnslothAI (X) · 🔥 0
  8. Unsloth releases free notebook to train decision models locally

    Unsloth published a free Colab notebook and companion guide showing how to train Qwen3.5-4B locally into a “decision model” that outputs decisions instead of text, using only 8GB of VRAM. Unsloth says its open-source repo can turn any model, such as Qwen3.8 or Gemma 4, into a decision model, and claims it raised Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across three decision benchmarks on just 4GB of VRAM (these figures are Unsloth’s own and the benchmark methodology was not disclosed). The walkthrough covers data preparation (state, questions, gold answers), training and serving, and likens the result to Jev.

    UnslothAI (X) · 🔥 0
  9. Microsoft open-sources mxc sandbox; Unsloth integrates it with <100ms overhead

    Microsoft has released an open-source repository called mxc for sandboxed code execution. Unsloth says it collaborated with Windows to add mxc OS-level sandboxing to Unsloth, adding under 100 ms of overhead, and points to its GitHub repo and a documentation guide. These claims come from Unsloth's post on X; no independent statement or further technical detail from Microsoft was seen in the source.

    UnslothAI (X) · 🔥 0

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