Validation

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Model Validation provides a tool for side-by-side comparison of multiple LLM models, including those that have been fine-tuned. This feature allows you to assess model performance at different training stages (epochs) by evaluating their responses to a given set of questions.

Validation Interface

Configuration Options for Model Validation

• Max Token Input: Allows users to set a limit on the maximum number of tokens generated by the model.
• Temperature Control: Provides a slider to adjust the randomness of the model's outputs, promoting deterministic or creative responses.
• Top-p Sampling: Enables users to fine-tune the sampling technique for better response quality.

Model Selection Drop-down

• Users can select from multiple models available for validation.
• Clicking on the model list allows you to choose from a variety of available models. These include both pre-trained base models and custom models that have been fine-tuned for specific tasks.
• Example Models:
  • Foundation Model: meta-llama/Llama-3.1-8B-Instruct
  • After Fine-tuning Model: AIR-8B -Epoch 4.

Side-by-Side Output Comparison

• The interface displays outputs from different models or configurations in parallel for easy comparison.
• Each column corresponds to a model with its specific parameters.

Question Input Area

• A dedicated input field for entering questions or prompts to be validated.
• Users can add questions using the text input area at the bottom of the interface or upload a JSON file to provide a batch of questions.

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The provided JSON snippet adheres to the standard JSON format. It consists of a list (indicated by square brackets []) containing multiple strings, each representing a question.

For Example:

[
  "What processor is integrated into the AIR-100 system?",
  "What graphics engine is used for HDMI-1 and HDMI-2 outputs in the AIR-100?",
  "How many HDMI outputs does the AIR-100 support, and what are their versions?"
]

Action Buttons

• Submit Button: Initiates the validation process based on the current configuration.
• Reset Button: Clears all question inputs.

Validate and Compare the Model

Upon clicking submit, the system will query both models with the provided question. During this process, there will be a brief loading time as the models are loaded onto the GPU. Once the models have generated their responses, a "like" icon will appear next to each answer. If you are satisfied with a particular response, please click the corresponding icon. The system will record the number of "likes" each model receives, which will be used for subsequent model quantification.

• Case I: Foundation Model vs Fine-tuning Mode (Epoch 4)
• Case II: Fine-tuning Mode (Epoch 1) vs Fine-tuning Mode (Epoch 4)

Action Buttons

• Download: Download the questions and their corresponding model-generated answers as a CSV file.

• Create (Model Quantization):

  • Create Workspace with this inference: Quantize the model and create a new Workspace directly, applying the quantized model. The default quantization format is q4_k_m. Advanced quantization options are available in advanced mode. The model selection list will display fine-tuned models from various training epochs. A statistical summary of user ratings for each model's responses will be included to aid in your selection.
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  The Q_K_M configuration is designed to:

  • Reduce memory usage: By lowering the precision of weights, models can run on devices with limited RAM.
  • Improve inference speed: Quantized models require fewer computational resources, making them faster.
  • Maintain acceptable accuracy: Advanced quantization methods like K-type aim to minimize the loss in model performance caused by reduced precision.

  For example, in llama.cpp, a Q4_K_M model uses 4-bit quantization with K-type optimization, striking a balance between memory efficiency and model accuracy.

  Breaking Down Q_K_M:

  • Q:
    • Represents Quantization, a process of reducing the precision of model weights (e.g., from 16-bit floating point to 4-bit integers).
    • Quantization reduces the memory footprint and speeds up inference, especially on resource-constrained devices.
  • K:
    • Refers to a K-type quantization method, which is a specific algorithm or approach used to optimize the quantization process.
    • K-type quantization typically focuses on minimizing perplexity loss (a measure of model performance) while maintaining efficiency. It is often more advanced than simpler quantization methods.
  • M:
    • Likely stands for a mode or configuration within the quantization method. For example:
      • M could mean "Medium," indicating a balance between performance and efficiency.
      • Other suffixes (e.g., S for "Small" or L for "Large") might represent different trade-offs between speed, memory usage, and accuracy.

• Import to Ollama's Inference Repo: To place a model into a repository that Ollama can access and use.

Once the model is compressed, you can locate it in the AI Provider section, under LLM and then Ollama.