Classify

/llamafarm:classify - Text Classification

Guide users through text classification workflows — zero-shot labeling, training custom classifiers, and batch prediction.

Usage

/llamafarm:classify                 # Zero-shot classification (default)
/llamafarm:classify train           # Train a custom classifier
/llamafarm:classify predict         # Predict with a trained classifier
/llamafarm:classify models          # List trained classifiers

What This Command Does

1. Identifies the workflow — zero-shot, train, predict, or informational
2. Gathers inputs — text, candidate labels, training examples
3. Recommends configuration — base model, label set, parameters
4. Executes the workflow — runs classification or trains model
5. Presents results — per-label scores and interpretation

Implementation

When the user runs /llamafarm:classify, follow these steps:

Subcommand: (default) Zero-Shot Classification

Step 1: Gather Inputs

Ask the user:

1. What text do you want to classify?
2. What are the candidate labels? (comma-separated list)
3. Is this multi-label? (can multiple labels apply simultaneously)

Step 2: Execute Classification

curl -X POST http://localhost:14345/v1/classify \
  -H "Content-Type: application/json" \
  -d '{
    "text": "<user_text>",
    "labels": ["<label_1>", "<label_2>", "<label_3>"],
    "multi_label": <true_or_false_from_user>
  }'

Step 3: Present Results

Classification Results
======================

Text: "<truncated input>"

Scores:
  label_1:  0.87  ████████▋
  label_2:  0.09  ▉
  label_3:  0.04  ▍

Predicted: label_1 (confidence: 87%)

Show:

• Per-label scores sorted by confidence
• Top prediction with confidence percentage
• If multi-label, all labels above threshold (default 0.5)

Subcommand: train

Step 1: Collect Training Examples

Ask the user:

1. How many labels/classes? (list them)
2. Where are your training examples? (file path, inline, or API)
3. How many examples per class? (recommend minimum 10-20)

Step 2: Recommend Base Model

Step 3: Execute Training

curl -X POST http://localhost:14345/v1/classifier/fit \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "<user_chosen_name>",
    "base_model": "<recommended>",
    "training_data": {
      "texts": ["<example_1>", "<example_2>"],
      "labels": ["<label_a>", "<label_b>"]
    },
    "epochs": 3,
    "batch_size": 16,
    "learning_rate": 2e-5
  }'

Step 4: Evaluate Results

Present:

• Model name and version
• Base model used
• Training accuracy and loss
• Per-class precision, recall, F1
• Next steps: run predictions or fine-tune further

Subcommand: predict

Step 1: Select Model

curl http://localhost:14345/v1/classifier/models

Present available models and ask the user to select one.

Step 2: Run Prediction

curl -X POST http://localhost:14345/v1/classifier/predict \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "<selected_model>",
    "texts": ["<text_1>", "<text_2>"]
  }'

Step 3: Present Results

Show:

• Per-text predicted label with confidence
• Per-label score breakdown for each text
• Summary statistics across batch

Subcommand: models

curl http://localhost:14345/v1/classifier/models

Present classifiers with name, base model, label count, training date, and accuracy.

Error Handling

Skills to Load

• text-classification - Detailed model reference and tuning guidance

Related Commands

• /llamafarm:anomaly - Anomaly detection
• /llamafarm:ml-status - Check ML service health
• /llamafarm:ocr - Extract text from images for classification