vault backup: 2024-06-13T11:08:03

Affected files:
content/projects/Breadboard/Phase 2/LM Studio.md

Author: Mearman

content/projects/Breadboard/Phase 2/LM Studio.md

@@ -4,5 +4,99 @@ aliases: []
 tags:
   - breadboard/phase/2
 created: 2024-05-31T19:25:49
-modified: 2024-06-12T10:34:26
+modified: 2024-06-13T11:07:40
 ---
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/0qr_Tk39zWg?rel=0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## Introduction
+
+Here we demonstrates the integration of a local language model with a Breadboard using the LM Studio software. Below, we will guide you through the process and explain the functionality of each input parameter.
+
+## Objective
+
+The goal of this project is to showcase how easy it is to integrate a breadboard with a language model and to provide a step-by-step example using the LM Studio software.
+
+## Prerequisites
+
+- LM Studio software installed on your local machine.
+- A basic understanding of language models and breadboards.
+
+## Inputs and Parameters
+
+### 1. Max Tokens
+
+- **Description**: Specifies the maximum length of the response generated by the language model.
+- **Example Value**: `-1` (indicating no limit on response length for this example).
+
+### 2. Stream
+
+- **Description**: A flag that indicates whether the language model should return the response as it is being generated.
+- **Example Value**: `true` or `false`.
+
+### 3. System Context
+
+- **Description**: Defines the context in which the language model should reply.
+- **Example Value**: `"Professional Chef"` (in this example, the language model responds as a professional chef).
+
+### 4. Temperature
+
+- **Description**: Adjusts the creativity of the language model's responses. Lower values make the model more deterministic.
+- **Example Value**: `1` (to ensure a balanced response).
+
+### 5. User Context
+
+- **Description**: The question or command provided to the language model.
+- **Example Value**: `"List the ingredients needed to make a cake."`
+
+## Step-by-Step Guide
+
+### Step 1: Setting Up Inputs
+
+1. **Max Tokens**: Set to `-1` for this example.
+2. **Stream**: Set to `true` or `false` based on your requirement.
+3. **System Context**: Set to `"Professional Chef"`.
+4. **Temperature**: Set to `1` to maintain response consistency.
+5. **User Context**: Enter the question `"List the ingredients needed to make a cake."`
+
+### Step 2: Running the Board
+
+- Execute the board with the provided inputs.
+- Open LM Studio to observe the response generation in real time.
+
+### Step 3: Viewing the Response
+
+- The response will be generated word by word if the stream flag is set.
+- Observe the final response in LM Studio or on your board.
+
+## Example Output
+
+### Request
+
+```markdown
+Max Tokens: -1
+Stream: true
+System Context: "Professional Chef"
+Temperature: 1
+User Context: "List the ingredients needed to make a cake."
+```
+
+### Response
+
+```markdown
+To make a cake, you will need the following ingredients:
+
+- Flour
+- Sugar
+- Eggs
+- Butter
+- Baking powder
+- Milk
+- Vanilla extract
+```
+
+## Conclusion
+
+This example illustrates the simplicity and potential of integrating breadboards with language models using LM Studio. By following the steps outlined above, you can create responsive and contextually aware applications.
+
+We hope you found this guide useful. For further inquiries or advanced configurations, feel free to contact our support team.