Update docs

apps/kilocode-docs/docs/providers/lmstudio.md

@@ -4,37 +4,40 @@ sidebar_label: LM Studio
 
 # Using LM Studio With Kilo Code
 
-Kilo Code supports running models locally using LM Studio.  LM Studio provides a user-friendly interface for downloading, configuring, and running local language models.  It also includes a built-in local inference server that emulates the OpenAI API, making it easy to integrate with Kilo Code.
+Kilo Code supports running models locally using LM Studio. LM Studio provides a user-friendly interface for downloading, configuring, and running local language models. It also includes a built-in local inference server that emulates the OpenAI API, making it easy to integrate with Kilo Code.
 
 **Website:** [https://lmstudio.ai/](https://lmstudio.ai/)
 
 ## Setting Up LM Studio
 
 1.  **Download and Install LM Studio:** Download LM Studio from the [LM Studio website](https://lmstudio.ai/).
-2.  **Download a Model:**  Use the LM Studio interface to search for and download a model.  Some recommended models include:
-    *   CodeLlama models (e.g., `codellama:7b-code`, `codellama:13b-code`, `codellama:34b-code`)
-    *   Mistral models (e.g., `mistralai/Mistral-7B-Instruct-v0.1`)
-    *   DeepSeek Coder models (e.g., `deepseek-coder:6.7b-base`)
-    * Any other model that is supported by Kilo Code, or for which you can set the context window.
+2.  **Download a Model:** Use the LM Studio interface to search for and download a model. Some recommended models include:
+
+    - CodeLlama models (e.g., `codellama:7b-code`, `codellama:13b-code`, `codellama:34b-code`)
+    - Mistral models (e.g., `mistralai/Mistral-7B-Instruct-v0.1`)
+    - DeepSeek Coder models (e.g., `deepseek-coder:6.7b-base`)
+    - Any other model that is supported by Kilo Code, or for which you can set the context window.
+
+    Look for models in the GGUF format. LM Studio provides a search interface to find and download models.
 
-    Look for models in the GGUF format.  LM Studio provides a search interface to find and download models.
 3.  **Start the Local Server:**
-    *   Open LM Studio.
-    *   Click the **"Local Server"** tab (the icon looks like `<->`).
-    *   Select the model you downloaded.
-    *   Click **"Start Server"**.
+    - Open LM Studio.
+    - Click the **"Local Server"** tab (the icon looks like `<->`).
+    - Select the model you downloaded.
+    - Click **"Start Server"**.
 
 ## Configuration in Kilo Code
 
 1.  **Open Kilo Code Settings:** Click the gear icon (<Codicon name="gear" />) in the Kilo Code panel.
 2.  **Select Provider:** Choose "LM Studio" from the "API Provider" dropdown.
-3.  **Enter Model ID:** Enter the *file name* of the model you loaded in LM Studio (e.g., `codellama-7b.Q4_0.gguf`).  You can find this in the LM Studio "Local Server" tab.
-4.  **(Optional) Base URL:**  By default, Kilo Code will connect to LM Studio at `http://localhost:1234`.  If you've configured LM Studio to use a different address or port, enter the full URL here.
+3.  **Enter Model ID:** Enter the _file name_ of the model you loaded in LM Studio (e.g., `codellama-7b.Q4_0.gguf`). You can find this in the LM Studio "Local Server" tab.
+4.  **(Optional) Base URL:** By default, Kilo Code will connect to LM Studio at `http://localhost:1234`. If you've configured LM Studio to use a different address or port, enter the full URL here.
+5.  **(Optional) Timeout:** By default, API requests time out after 10 minutes. Local models can be slow, if you hit this timeout you can consider increasing it here: VS Code Extensions panel > Kilo Code gear menu > Settings > API Request Timeout.
 
 ## Tips and Notes
 
-*   **Resource Requirements:** Running large language models locally can be resource-intensive. Make sure your computer meets the minimum requirements for the model you choose.
-*   **Model Selection:**  LM Studio provides a wide range of models.  Experiment to find the one that best suits your needs.
-*   **Local Server:**  The LM Studio local server must be running for Kilo Code to connect to it.
-*   **LM Studio Documentation:** Refer to the [LM Studio documentation](https://lmstudio.ai/docs) for more information.
-*   **Troubleshooting:** If you see a "Please check the LM Studio developer logs to debug what went wrong" error, you may need to adjust the context length settings in LM Studio.
+- **Resource Requirements:** Running large language models locally can be resource-intensive. Make sure your computer meets the minimum requirements for the model you choose.
+- **Model Selection:** LM Studio provides a wide range of models. Experiment to find the one that best suits your needs.
+- **Local Server:** The LM Studio local server must be running for Kilo Code to connect to it.
+- **LM Studio Documentation:** Refer to the [LM Studio documentation](https://lmstudio.ai/docs) for more information.
+- **Troubleshooting:** If you see a "Please check the LM Studio developer logs to debug what went wrong" error, you may need to adjust the context length settings in LM Studio.

apps/kilocode-docs/docs/providers/ollama.md

@@ -63,28 +63,12 @@ ollama pull qwen3-coder:30b
 By default Ollama truncates prompts to a very short length, [as documented here](https://github.com/ollama/ollama/blob/4383a3ab7a075eff78b31f7dc84c747e2fcd22b8/docs/faq.md#how-can-i-specify-the-context-window-size).
 
 You need to have at least 32k to get decent results, but increasing the context size increases memory usage and may decrease performance, depending on your hardware.
-To configure a model, you need to set its parameters and save a copy of it.
 
-Load the model (we will use `qwen3-coder:30b` as an example):
+To configure the context window, set "Context Window Size (num_ctx)" in the API Provider settings.
 
-```bash
-ollama run qwen3-coder:30b
-```
-
-Change context size parameter:
-
-```bash
-/set parameter num_ctx 32768
-```
-
-Save the model with a new name:
-
-```bash
-/save qwen3-coder-30b-c32k
-```
+### Configure the Timout
 
-You can also set the `OLLAMA_CONTEXT_LENGTH` environment variable,
-but this is not recommended as it changes the context for all models and the environment variable needs to be visible to both the Ollama server and the IDE.
+By default, API requests time out after 10 minutes. Local models can be slow, if you hit this timeout you can consider increasing it here: VS Code Extensions panel > Kilo Code gear menu > Settings > API Request Timeout.
 
 ### Configure Kilo Code