For local models I've been trying it with GLM-4.7-Flash and the new LFM2 24B model. I'm excited to try it with the new Qwen3.5 models that came out today as well.
I've been using Pi day to day recently for simple, smaller tasks. It's a great harness for use with smaller parameter size models given the system prompt is quite a bit shorter vs Claude or Codex (and it uses a nice small set of tools by default).
It's available (with tool parsing, etc.): https://ollama.com/library/glm-4.7-flash but requires 0.14.3 which is in pre-release (and available on Ollama's GitHub repo)
The gpt-oss weights on Ollama are native mxfp4 (the same weights provided by OpenAI). No additional quantization is applied, so let me know if you're seeing any strange results with Ollama.
Most gpt-oss GGUF files online have parts of their weights quantized to q8_0, and we've seen folks get some strange results from these models. If you're importing these to Ollama to run, the output quality may decrease.
We did consider building functionality into Ollama that would go fetch search results and website contents using a headless browser or similar. However we had a lot of worries about result quality and also IP blocking from Ollama creating crawler-like behavior. Having a hosted API felt like a fast path to get results into users' context window, but we are still exploring the local option. Ideally you'd be able to stay fully local if you want to (even when using capabilities like search)
Sorry it's taking so long to review and for the radio silence on the PR.
We have been trying to figure out how to support more structured output formats without some of the side effects of grammars. With JSON mode (which uses grammars under the hood) there were originally quite a few issue reports namely around lower performance and cases where the model would infinitely generate whitespace causing requests to hang. This is an issue with OpenAI's JSON mode as well which requires the caller to "instruct the model to produce JSON" [1]. While it's possible to handle edge cases for a single grammar such as JSON (i.e. check for 'JSON' in the prompt), it's hard to generalize this to any format.
Supporting more structured output formats is definitely important. Fine-tuning for output formats is promising, and this thread [2] also has some great ideas and links.
Pre-release versions are created to test new updates on bunch of different hardware setups (OS/GPUs) before releasing more broadly (and making new versions the default for the Linux/macOS/Windows installers – those pull from the 'latest' release).
There are a good number of folks that test the pre-releases as well (thank you!) especially if there's a bug fix or new feature they are waiting for. "Watch"ing the repo on GitHub will send emails/notifications of new pre-release versions
Not at the moment, although it is a highly requested feature (specifically fine-tuning). There are a few tools that you can (or will soon be able to) use to fine tune a model and then import the resulting adapter layers into Ollama: MLX [1] on macOS, Unsloth [2] on Windows and Linux