Implementing 'Jev' in 25 Lines of Python for LLM Classification

Introduction to 'Jev' Implementation

The provided Python script outlines a method to create a classification system, humorously termed 'Jev', using a large language model. This implementation focuses on demonstrating the core mechanics of using an LLM for classification in a concise manner.

Core Components and Setup

The script begins by specifying Python 3.12+ and dependencies including `huggingface-hub`, `llama-cpp-python`, and `numpy`. It then loads a GGUF model, specifically 'Qwen3-0.6B-Q8_0.gguf' from 'Qwen/Qwen3-0.6B-GGUF', using `Llama.from_pretrained` with specific configurations like `n_ctx=512` and `logits_all=True`.

Defining Prompts and Choices

The implementation defines a set of labels ('A', 'B', 'C') and corresponding choices ('Legitimate', 'Spam', 'Phishing'). An example email is provided, and a prompt is constructed to instruct the model to choose one option from the given choices. The model then evaluates this prompt.

Processing Logits to Probabilities

After the model evaluates the prompt, the script extracts the logits from the last token. It then identifies the token IDs for each choice label and retrieves their corresponding logits. These choice logits are processed using `numpy.logaddexp.reduce` to calculate log probabilities, which are then converted into final probabilities using `numpy.exp`.

Output and Characteristics

The script outputs the raw logits, log probabilities, and final probabilities for each choice. The author notes that while this implementation is a simplified version of what might be considered a full 'Jev' system (lacking aspects like RLCD or API calls), it effectively demonstrates a fast, local, and classification-capable system that processes prompts and outputs probabilities.