No content.
Written by
More to read
LLM Fine-Tuning Frameworks in Production: Unsloth vs. Axolotl vs. LLaMA-Factory vs. Torchtune Architecture, Throughput, and Distributed Scaling
Modern post-training pipelines have moved beyond basic training scripts. As model parameter counts, context windows, and alignment techniques expand, the choice of fine-tuning framework directly dictates GPU memory overhead, token throughput, and developer iteration speed. Four open-source frameworks dominate the enterprise fine-tuning landscape: Unsloth, Axolotl, LLaMA-Factory, and Meta's Torchtune. While all four orchestrate parameter-efficient fine-tuning (PEFT) and full parameter adaptation
1 minAnthropic Prepares Dual-Class Super-Voting Shares for Co-Founders Ahead of Planned IPO
Anthropic is preparing to implement a dual-class share structure that grants super-voting equity to its co-founders ahead of a planned initial public offering, according to a report from The Information. The mechanism is designed to concentrate long-term operational voting control with executive leadership and insulate decision-making from external market and investor pressures. The structure comes as the maker of the Claude model family scales enterprise commercialization, with annual revenue
1 minAlibaba Demonstrates Native Qwen 3.8 27B Inference on XuanTie C950 RISC-V CPU at 30 Tokens per Second
Alibaba's semiconductor division, T-Head, announced day-zero native inference support for its latest open-weight model, Qwen 3.8 27B, running directly on the XuanTie C950 RISC-V server processor. Operating without discrete graphics processing units, the 64-core RISC-V chip delivered sustained decode throughput of 30 tokens per second alongside a time-to-first-token latency of 1.9 seconds. The benchmark demonstrates how architectural extensions on general-purpose open instruction sets can handle
1 min
