Brief

A user on r/LocalLLaMA has shared positive impressions of JetBrains Mellum 2, a 12B Mixture-of-Experts model. Despite its size, the model demonstrates impressive performance, achieving 111.2 t/s generation speed and maintaining over 100 t/s even with a context window of 131,072 tokens on an AMD Radeon RX 7900 XT. The user highlighted its capability in handling complex tasks like tool calls and data reconstruction, outperforming other models like Qwen3.5-9B on the same hardware. AI

TinySearch, a lightweight open-source web search tool designed for local LLMs, has released version 0.2.0. This update replaces its previous reliance on DuckDuckGo with SearXNG as the default backend, offering greater flexibility and reducing dependency on a single search provider. The tool is optimized to provide smaller LLMs with a compact, source-grounded context blob of up to 8,000 tokens, making it suitable for agents and local setups that cannot handle large amounts of scraped data. AI

This blog post details how to equip a local large language model with real-time web search capabilities, mimicking the functionality of cloud-based AI products. The process involves building a TypeScript application that allows the LLM to decide when to perform a web search, execute that search using an API like SerpApi, and then use the fresh data to formulate a response. The guide recommends using LM Studio for running models locally and suggests models like Qwen3.5-9B or Google's Gemma 4 that support tool-calling for agentic workflows. AI

Researchers have developed POLARIS, a new training method designed to improve the long-form creative writing capabilities of smaller open-weight language models. This method utilizes a frontier LLM as a judge with a structured quality rubric and incorporates human-written story references as high-reward anchors during training. Applied to Qwen3.5-9B, the resulting POLARIS-9B model demonstrates competitive performance against larger models and shows improved adherence to length instructions, even for stories exceeding its training length. AI

A comparison of the Gemma-4-12B-it and Qwen3.5-9B large language models indicates that Qwen generally outperforms Gemma on a per-gigabyte basis. The Qwen model achieved better results in 5 out of 8 benchmarks, despite having a smaller parameter footprint. While Gemma-4-12B-it may show slightly superior coding capabilities, specialized fine-tunes of Qwen are available for such tasks. AI