Refactors MCP server enabling logic to remove the dependency on global settings.
This simplifies the logic by directly checking the per-chat override status, and removes the need to pass the global enabled state as a parameter.
Additionally:
- Only shows MCP servers that are enabled in settings in the selector.
- Sorts the servers by whether they are enabled for the current chat.
Remove all frontend validation logic that prevented users from selecting
models based on multimodal capabilities. This refactoring removes
restrictive UI code while maintaining full functionality
- Vision models can describe images as text
- That text remains useful for non-vision models
- Chaining vision -> non-vision is a valid workflow
- Users know their use case better than the UI
- Users can return to vision models when needed
LLM can reference tool-generated images using markdown links like,
plugin resolves attachment names to base64 from message.extra when present,
regular HTTP/data URLs pass through unchanged (no regression)
- rehypeResolveAttachmentImages plugin in markdown pipeline
- Pass message prop to MarkdownContent and AgenticContent
- Force processor reactivity on message.extra changes
- Filter assistant images from API context (display-only)
* initial commit for branch
* simplify constants
* add params to `struct common_params_sampling`, add reference to PR
* explicitly clamp `min_target` and `max_target` to `[0.0, 1.0]`
* add args, rename `queue_size` -> `window_size`
* improved comments
* minor
* remove old unused code from algorithm
* minor
* add power law case to `common_sampler_init`, add sampler name mappings
* clarify behaviour when `window_size = 0`
* add missing enums
* remove `target_range` param, make `target == 1` no-op, cleanup code
* oops, straggler
* add missing parameters in `server-task.cpp`
* copy from author
ref:
https://gist.github.com/MrJackSpade/9be99c7efbba7b95a41377e123b7b069
* remove old debug log, style nit
* fix compiler warning, add commented-out logging per token
* re-write + change parameters + simplify
* oops forgot args.cpp
* fix leftover `window_size`
* add missing values to `common_params_sampling::print()`
* with logging
* does this fix it?
* no, but does this?
* update default decay
* optimize
* fix bad merge
my git skills are lacking
* silence `missing initializer for member`
* update default decay to 0.9
* fix logging
* format (double)
* add power law to the new `samplers` vector
* log sampler init values
* improve logging messages in llama_sampler_power_law
* remove extraneous logging
* simplify target computation
last commit with debug logging!
* remove debug logging, explicitly clamp params at init
* add `use_power_law` flag + logic, minor cleanup
* update `power-law` -> `adaptive-p`
* fix cold start EMA
- `ctx->weighted_sum` is now initialized and reset to `target / (1.0f -
clamped_decay)`
- `ctx->total_weight` is now initialized and reset to `1.0f / (1.0f -
clamped_decay)`
this fixes a "cold start" problem with the moving average
* update `SHARPNESS` constant to `10.0f`
* minor style fixes
no functional changes
* minor style fixes cont.
* update `llama_sampler_adaptive_p_i` for backend sampling (ref: #17004)
* separate into `apply` + `accept` functions
* `pending_token_idx`: switch from `llama_token` to `int32`
functionally identical (`llama.h` has `typedef int32_t llama_token;`),
but its more correct now
* don't transform logits <= -1e9f
* fix masking in backend top-p, min-p
* address review comments
* typo in comments `RND` -> `RNG`
* add docs
* add recommended values in completion docs
* address PR feedback
* remove trailing whitespace (for CI `editorconfig`)
* add to adaptive-p to `common_sampler_types_from_chars`
* server : make sure children tasks are scheduled to launch with parent
* fix
* add comment pointing to this PR
* fix
* clean up
* more debug messages
* add pop_deferred_task with specific ID version
* improve the logic
* simple approach
* no double move
* correct return type of launch_slots_with_parent_task
* server : add arg for disabling prompt caching
Disabling prompt caching is useful for clients who are restricted to
sending only OpenAI-compat requests and want deterministic
responses.
* address review comments
* address review comments
Resolves conflicts by:
- Keeping clean store architecture (agentic.svelte.ts delegates to client)
- Updating agentic.client.ts to use TOOL_ARGS_START/END format
- Accepting remote AgenticContent.svelte with direct JSON parsing
- Updating ChatMessageAssistant to match new AgenticContent props
* server : add thinking content blocks to Anthropic Messages API
Add support for returning reasoning/thinking content in Anthropic API
responses when using models with --reasoning-format deepseek and the
thinking parameter enabled.
- Non-streaming: adds thinking block before text in content array
- Streaming: emits thinking_delta events with correct block indices
- Partial streaming: tracks reasoning state across chunks via
anthropic_has_reasoning member variable
Tested with bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF model.
* server : fix Anthropic API streaming for thinking content blocks
Add signature field and fix duplicate content_block_start events in
Anthropic Messages API streaming responses for reasoning models.
* server: refactor Anthropic streaming state to avoid raw pointer
Replace raw pointer to task_result_state with direct field copies:
- Copy state fields in update() before processing chunk
- Use local copies in to_json_anthropic() instead of dereferencing
- Pre-compute state updates for next chunk in update()
This makes the data flow clearer and avoids unsafe pointer patterns.
Aleksander Grygier
Pascal
Xuan-Son Nguyen
ddh0
Georgi Gerganov
Radoslav Gerganov
Adrien Gallouët
R
Tarek Dakhran
Vladislav Sayapin