LM Studio 0.3.26 • 2025‑09‑15

CLI log streaming: server + model

lms log stream now supports multiple sources and filters.
- --source server streams HTTP server logs (startup, endpoints, status)
- --source model --filter input,output streams formatted user input and model output
- Append --json for machine‑readable logs; --stats adds tokens/sec and related metrics (model source)
See usage and examples: /docs/cli/log-stream. Full release notes: /blog/lmstudio-v0.3.26.

LM Studio 0.3.25 • 2025‑09‑04

New model support (API)

Added support for NVIDIA Nemotron‑Nano‑v2 with tool‑calling via the OpenAI‑compatible endpoints ‡.
Added support for Google EmbeddingGemma for the /v1/embeddings endpoint ‡.

LM Studio 0.3.24 • 2025‑08‑28

Seed‑OSS tool‑calling and template fixes

Added support for ByteDance/Seed‑OSS including tool‑calling and prompt‑template compatibility fixes in the OpenAI‑compatible API ‡.
Fixed cases where tool calls were not parsed for certain prompt templates ‡.

LM Studio 0.3.23 • 2025‑08‑12

Reasoning content and tool‑calling reliability

For gpt‑oss on POST /v1/chat/completions, reasoning content moves out of message.content and into choices.message.reasoning (non‑streaming) and choices.delta.reasoning (streaming), aligning with o3‑mini ‡.
Tool names are normalized (e.g., snake_case) before being provided to the model to improve tool‑calling reliability ‡.
Fixed errors for certain tools‑containing requests to POST /v1/chat/completions (e.g., "reading 'properties'") and non‑streaming tool‑call failures ‡.

LM Studio 0.3.19 • 2025‑07‑21

Bug fixes for streaming and tool calls

Corrected usage statistics returned by OpenAI‑compatible streaming responses ‡.
Improved handling of parallel tool calls via the streaming API ‡.
Fixed parsing of correct tool calls for certain Mistral models ‡.

LM Studio 0.3.18 • 2025‑07‑10

Streaming options and tool‑calling improvements

Added support for the stream_options object on OpenAI‑compatible endpoints. Setting stream_options.include_usage to true returns prompt and completion token usage during streaming ‡.
Errors returned from streaming endpoints now follow the correct format expected by OpenAI clients ‡.
Tool‑calling support added for Mistral v13 tokenizer models, using proper chat templates ‡.
The response_format.type field now accepts "text" in chat‑completion requests ‡.
Fixed bugs where parallel tool calls split across multiple chunks were dropped and where root‑level $defs in tool definitions were stripped ‡.

LM Studio 0.3.17 • 2025‑06‑25

Tool‑calling reliability and token‑count updates

Token counts now include the system prompt and tool definitions ‡. This makes usage reporting more accurate for both the UI and the API.
Tool‑call argument tokens are streamed as they are generated ‡, improving responsiveness when using streamed function calls.
Various fixes improve MCP and tool‑calling reliability, including correct handling of tools that omit a parameters object and preventing hangs when an MCP server reloads ‡.

LM Studio 0.3.16 • 2025‑05‑23

Model capabilities in `GET /models`

The OpenAI‑compatible REST API (/api/v0) now returns a capabilities array in the GET /models response. Each model lists its supported capabilities (e.g. "tool_use") ‡ so clients can programmatically discover tool‑enabled models.
Fixed a streaming bug where an empty function name string was appended after the first packet of streamed tool calls ‡.

👾 LM Studio 0.3.15 • 2025-04-24

Improved Tool Use API Support

OpenAI-like REST API now supports the tool_choice parameter:

{
  "tool_choice": "auto" // or "none", "required"
}

"tool_choice": "none" — Model will not call tools
"tool_choice": "auto" — Model decides
"tool_choice": "required" — Model must call tools (llama.cpp only)

Chunked responses now set "finish_reason": "tool_calls" when appropriate.

👾 LM Studio 0.3.14 • 2025-03-27

[API/SDK] Preset Support

RESTful API and SDKs support specifying presets in requests.

(example needed)

👾 LM Studio 0.3.10 • 2025-02-18

Speculative Decoding API

Enable speculative decoding in API requests with "draft_model":

{
  "model": "deepseek-r1-distill-qwen-7b",
  "draft_model": "deepseek-r1-distill-qwen-0.5b",
  "messages": [ ... ]
}

Responses now include a stats object for speculative decoding:

"stats": {
  "tokens_per_second": ...,
  "draft_model": "...",
  "total_draft_tokens_count": ...,
  "accepted_draft_tokens_count": ...,
  "rejected_draft_tokens_count": ...,
  "ignored_draft_tokens_count": ...
}

👾 LM Studio 0.3.9 • 2025-01-30

Idle TTL and Auto Evict

Set a TTL (in seconds) for models loaded via API requests (docs article: Idle TTL and Auto-Evict)

curl http://localhost:1234/api/v0/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-r1-distill-qwen-7b",
    "messages": [ ... ]
+   "ttl": 300,
}'

With lms:

lms load --ttl <seconds>

Separate `reasoning_content` in Chat Completion responses

For DeepSeek R1 models, get reasoning content in a separate field. See more here.

Turn this on in App Settings > Developer.

👾 LM Studio 0.3.6 • 2025-01-06

Tool and Function Calling API

Use any LLM that supports Tool Use and Function Calling through the OpenAI-like API.

Docs: Tool Use and Function Calling.

👾 LM Studio 0.3.5 • 2024-10-22

Introducing `lms get`: download models from the terminal

You can now download models directly from the terminal using a keyword

lms get deepseek-r1

or a full Hugging Face URL

lms get <hugging face url>

To filter for MLX models only, add --mlx to the command.

lms get deepseek-r1 --mlx

API Changelog