* launch: add Muse Code integration
Add `ollama launch muse` for Meta's Muse Code CLI.
Muse only takes a model catalog from settings.json (normally it fetches one from its provider and refuses to start otherwise), and that file's endpoint_transport is a global provider switch. So the integration writes a settings file under its own config root (~/.ollama/launch/muse-config via XDG_CONFIG_HOME), leaving a Meta-backed muse install untouched, and re-seeds it from muse's own persisted copy on later runs.
The launched model is preloaded so its catalog row carries the context length the server actually allocated, not the trained maximum; the loaded-context helpers move from cmd/agent_tui.go into cmd/launch for reuse.
Muse sends reasoning efforts outside Ollama's scale (minimal, xhigh, ultra), which were hard 400s; clamp them to the nearest tier in one helper shared by the chat and responses converters.
The registry entry stays Hidden (alias "muse-code"), like kimi and vscode.
* review comments
* skip muse test on windows (unsupported platform)
Add support to launch the hermes-desktop app alongside the hermes agent from ollama launch. It will go through the install on first run if hermes-desktop is not already installed.
* Reduce startup model hydration
Add a lightweight model list cache for tags and launch inventory, while keeping show cache population lazy. This avoids loading every local model at startup on large model stores.
* harden flaky scheduler unit test
* remove extra launch model metadata text
* review comments
* review comments
* mlx: add laguna model support
* convert: support fp8 safetensors import
Decode HF F8_E4M3 safetensors with block scale companions into GGUF-supported tensor types, and record which output tensors came from FP8 source weights.
Use that source-precision metadata during create quantization: default FP8-sourced GGUFs to Q8_0, keep non-FP8 tensors at their original precision for Q8_0, and promote non-FP8 quantizable tensors to Q8_0 for Q4_K requests.
* ggml: add laguna model support
* server: preserve generate logprobs with builtin parsers
Generate requests were dropping logprob-only chunks whenever a builtin parser buffered visible content. Chat already handled this case, but generate only forwarded chunks with visible response, thinking, or tool-call output.
Keep generate chunks that carry logprobs even when the builtin parser has not flushed visible content yet, and add a regression test that exercises the behavior with a generic thinking parser.
* review comments - perf improvements
* ggml: implement nemotron 3 nano omni
* add poolside integration
* update poolside doc
* adapt to new cache setup
* fix test
* fix test
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Co-authored-by: Eva Ho <hoyyeva@gmail.com>
Parth SareenandGitHub
Eva HandGitHub
Daniel HiltgenandGitHub
Bruce MacDonaldandGitHub
